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Roux A, Elia A, Hudelist B, Benzakoun J, Dezamis E, Parraga E, Moiraghi A, Simboli GA, Chretien F, Oppenheim C, Zanello M, Pallud J. Prognostic significance of MRI contrast enhancement in newly diagnosed glioblastoma, IDH-wildtype according to WHO 2021 classification. J Neurooncol 2024; 169:445-455. [PMID: 38913230 DOI: 10.1007/s11060-024-04747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/11/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND AND OBJECTIVES Contrast enhancement in glioblastoma, IDH-wildtype is common but not systematic. In the era of the WHO 2021 Classification of CNS Tumors, the prognostic impact of a contrast enhancement and the pattern of contrast enhancement is not clearly elucidated. METHODS We performed an observational, retrospective, single-centre cohort study at a tertiary neurosurgical oncology centre (January 2006 - December 2022). We screened adult patients with a newly-diagnosed glioblastoma, IDH-wildtype in order to assess the prognosis role of the contrast enhancement and the pattern of contrast enhancement. RESULTS We included 1149 glioblastomas, IDH-wildtype: 26 (2.3%) had a no contrast enhancement, 45 (4.0%) had a faint and patchy contrast enhancement, 118 (10.5%) had a nodular contrast enhancement, and 960 (85.5%) had a ring-like contrast enhancement. Overall survival was longer in non-contrast enhanced glioblastomas (26.7 months) than in contrast enhanced glioblastomas (10.9 months) (p < 0.001). In contrast enhanced glioblastomas, a ring-like pattern was associated with shorter overall survival than in faint and patchy and nodular patterns (10.0 months versus 13.0 months, respectively) (p = 0.033). Whatever the presence of a contrast enhancement and the pattern of contrast enhancement, surgical resection was an independent predictor of longer overall survival, while age ≥ 70 years, preoperative KPS score < 70, tumour volume ≥ 30cm3, and postoperative residual contrast enhancement were independent predictors of shorter overall survival. CONCLUSION A contrast enhancement is present in the majority (97.7%) of glioblastomas, IDH-wildtype and, regardless of the pattern, is associated with a shorter overall survival. The ring-like pattern of contrast enhancement is typical in glioblastomas, IDH-wildtype (85.5%) and remains an independent predictor of shorter overall survival compared to other patterns (faint and patchy and nodular).
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Affiliation(s)
- Alexandre Roux
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France.
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France.
| | - Angela Elia
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
| | - Benoit Hudelist
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
| | - Joseph Benzakoun
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
- Service de Neuroradiologie, GHU Paris Psychiatrie et Neurosciences, Site Sainte Anne, Paris, F- 75014, France
| | - Edouard Dezamis
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
| | - Eduardo Parraga
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
| | - Alessandro Moiraghi
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
| | - Giorgia Antonia Simboli
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
- Service de Neuropathologie, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, F- 75014, France
| | - Fabrice Chretien
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
- Service de Neuropathologie, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, F- 75014, France
| | - Catherine Oppenheim
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
- Service de Neuroradiologie, GHU Paris Psychiatrie et Neurosciences, Site Sainte Anne, Paris, F- 75014, France
| | - Marc Zanello
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
| | - Johan Pallud
- Service de Neurochirurgie, GHU-Paris Psychiatrie et Neurosciences, Site Sainte Anne, 1, rue Cabanis, Paris Cedex 14, F-75014, France
- Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, INSERM U1266, IMA-Brain, Paris, F-75014, France
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Shin I, Park YW, Sim Y, Choi SH, Ahn SS, Chang JH, Kim SH, Lee SK, Jain R. Revisiting gliomatosis cerebri in adult-type diffuse gliomas: a comprehensive imaging, genomic and clinical analysis. Acta Neuropathol Commun 2024; 12:128. [PMID: 39127694 DOI: 10.1186/s40478-024-01832-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024] Open
Abstract
Although gliomatosis cerebri (GC) has been removed as an independent tumor type from the WHO classification, its extensive infiltrative pattern may harbor a unique biological behavior. However, the clinical implication of GC in the context of the 2021 WHO classification is yet to be unveiled. This study investigated the incidence, clinicopathologic and imaging correlations, and prognostic implications of GC in adult-type diffuse glioma patients. Retrospective chart and imaging review of 1,211 adult-type diffuse glioma patients from a single institution between 2005 and 2021 was performed. Among 1,211 adult-type diffuse glioma patients, there were 99 (8.2%) patients with GC. The proportion of molecular types significantly differed between patients with and without GC (P = 0.017); IDH-wildtype glioblastoma was more common (77.8% vs. 66.5%), while IDH-mutant astrocytoma (16.2% vs. 16.9%) and oligodendroglioma (6.1% vs. 16.5%) were less common in patients with GC than in those without GC. The presence of contrast enhancement, necrosis, cystic change, hemorrhage, and GC type 2 were independent risk factors for predicting IDH mutation status in GC patients. GC remained as an independent prognostic factor (HR = 1.25, P = 0.031) in IDH-wildtype glioblastoma patients on multivariable analysis, along with clinical, molecular, and surgical factors. Overall, our data suggests that although no longer included as a distinct pathological entity in the WHO classification, recognition of GC may be crucial considering its clinical significance. There is a relatively high incidence of GC in adult-type diffuse gliomas, with different proportion according to molecular types between patients with and without GC. Imaging may preoperatively predict the molecular type in GC patients and may assist clinical decision-making. The prognostic role of GC promotes its recognition in clinical settings.
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Affiliation(s)
- Ilah Shin
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea.
| | - Yongsik Sim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun- gu, Seoul, 03722, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul, 03722, Republic of Korea
| | - Rajan Jain
- Department of Radiology, New York University Grossman School of Medicine, 550 1st Ave, New York, NY States, USA
- Department of Neurosurgery, New York University Grossman School of Medicine, 550 1st Ave, New York, NY States, USA
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Azizova A, Prysiazhniuk Y, Wamelink IJHG, Petr J, Barkhof F, Keil VC. Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance. AJNR Am J Neuroradiol 2024; 45:1053-1062. [PMID: 38937115 DOI: 10.3174/ajnr.a8274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 03/01/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking. PURPOSE Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment. DATA SOURCES MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023. STUDY SELECTION Original articles predicting diagnosis, progression, and survival in patients with glioma were included. DATA ANALYSIS The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided. DATA SYNTHESIS Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 17). LIMITATIONS Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity. CONCLUSIONS Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.
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Affiliation(s)
- Aynur Azizova
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Yeva Prysiazhniuk
- The Second Faculty of Medicine (Y.P.), Department of Pathophysiology, Charles University, Prague, Czech Republic
| | - Ivar J H G Wamelink
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Jan Petr
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Institute of Radiopharmaceutical Cancer Research (J.P.), Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Frederik Barkhof
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Brain Imaging (F.B., V.C.K.), Amsterdam Neuroscience, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing (F.B.), University College London, London, United Kingdom
| | - Vera C Keil
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
- Brain Imaging (F.B., V.C.K.), Amsterdam Neuroscience, Amsterdam, the Netherlands
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Xie X, Luo C, Wu S, Qiao W, Deng W, Jin L, Lu J, Bu L, Duffau H, Zhang J, Yao Y. Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients. BMC Cancer 2024; 24:818. [PMID: 38982347 PMCID: PMC11232293 DOI: 10.1186/s12885-024-12542-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/20/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.
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Affiliation(s)
- Xian Xie
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Chen Luo
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Shuai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Wanyu Qiao
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Wei Deng
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China
| | - Lei Jin
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Junfeng Lu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai, 200052, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China
| | - Linghao Bu
- Department of Neurosurgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 80 Avenue Agustin Fliche, Montpellier, 34295, France
| | - Jie Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
- National Center for Neurological Disorders, Shanghai, 200052, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040, China.
- Neurosurgical Institute of Fudan University, Shanghai, 200052, China.
| | - Ye Yao
- Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200032, China.
- Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, 200032, China.
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5
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Truong NCD, Bangalore Yogananda CG, Wagner BC, Holcomb JM, Reddy D, Saadat N, Hatanpaa KJ, Patel TR, Fei B, Lee MD, Jain R, Bruce RJ, Pinho MC, Madhuranthakam AJ, Maldjian JA. Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma. Radiol Artif Intell 2024; 6:e230218. [PMID: 38775670 PMCID: PMC11294953 DOI: 10.1148/ryai.230218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 06/21/2024]
Abstract
Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.
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Affiliation(s)
- Nghi C. D. Truong
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Chandan Ganesh Bangalore Yogananda
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Benjamin C. Wagner
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - James M. Holcomb
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Divya Reddy
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Niloufar Saadat
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Kimmo J. Hatanpaa
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Toral R. Patel
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Baowei Fei
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Matthew D. Lee
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Rajan Jain
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Richard J. Bruce
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Marco C. Pinho
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Ananth J. Madhuranthakam
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Joseph A. Maldjian
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
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6
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Lin D, Liu J, Ke C, Chen H, Li J, Xie Y, Ma J, Lv X, Feng Y. Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas. Clin Neuroradiol 2024:10.1007/s00062-024-01421-3. [PMID: 38858272 DOI: 10.1007/s00062-024-01421-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/03/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. METHODS Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. RESULTS Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). CONCLUSION Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.
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Affiliation(s)
- Danlin Lin
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jiehong Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chao Ke
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haolin Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
- Guangdong-Hong Kong-Macao Greater Bay Area Centre for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Guangzhou, China.
- Department of Radiology, The First People's Hospital of Shunde, Southern Medical University, Foshan, China.
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Huang D, Gao T, Zhang Y, Lyu X, Liu S, Chen Y, Su C, Hu W, Lv Y. A Study on Prognosis of Diffuse Glioma Based on Clinical Factors and Magnetic Resonance Imaging Radiomics. World Neurosurg 2024; 186:e514-e530. [PMID: 38583562 DOI: 10.1016/j.wneu.2024.03.166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVE To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma. METHODS Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort: validation cohort=7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) was also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a "Prognosis Calculator for Diffuse Glioma" was constructed based on the nomogram. RESULTS The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year area under the curve of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year area under the curve was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The "Prognosis Calculator for Diffuse Glioma" was available at https://github.com/HDCurry/prognosis. CONCLUSIONS Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The "Prognosis Calculator for Diffuse Glioma" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies.
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Affiliation(s)
- Dongcun Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Tianyu Gao
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ying Zhang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaofei Lyu
- China Quality Certification Centre, Guangzhou, China
| | - Siheng Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Changliang Su
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wanming Hu
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yanchun Lv
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
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8
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Roller LA, Wan Q, Liu X, Qin L, Chapel D, Burk KS, Guo Y, Shinagare AB. MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma. Abdom Radiol (NY) 2024; 49:1522-1533. [PMID: 38467853 DOI: 10.1007/s00261-024-04198-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). METHODS This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables. RESULTS 12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929). CONCLUSION While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.
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Affiliation(s)
- Lauren A Roller
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA.
| | - Qi Wan
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoyang Liu
- Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, Toronto, ON, M5T1W7, Canada
| | - Lei Qin
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - David Chapel
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kristine S Burk
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - Yang Guo
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - Atul B Shinagare
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, 02115, USA
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9
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Pons-Escoda A, Majos C, Smits M, Oleaga L. Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units. RADIOLOGIA 2024; 66:260-277. [PMID: 38908887 DOI: 10.1016/j.rxeng.2024.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/31/2023] [Indexed: 06/24/2024]
Abstract
The 2021 World Health Organization classification of CNS tumours was greeted with enthusiasm as well as an initial potential overwhelm. However, with time and experience, our understanding of its key aspects has notably improved. Using our collective expertise gained in neuro-oncology units in hospitals in different countries, we have compiled a practical guide for radiologists that clarifies the classification criteria for diffuse gliomas in adults. Its format is clear and concise to facilitate its incorporation into everyday clinical practice. The document includes a historical overview of the classifications and highlights the most important recent additions. It describes the main types in detail with an emphasis on their appearance on imaging. The authors also address the most debated issues in recent years. It will better prepare radiologists to conduct accurate presurgical diagnoses and collaborate effectively in clinical decision making, thus impacting decisions on treatment, prognosis, and overall patient care.
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Affiliation(s)
- A Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Facultat de Medicina i Ciencies de La Salut, Universitat de Barcelona (UB), Barcelona, Spain.
| | - C Majos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain; Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - M Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands; Medical Delta, Delft, The Netherlands
| | - L Oleaga
- Radiology Department, Hospital Clínic Barcelona, Barcelona, Spain
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10
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Foltyn-Dumitru M, Schell M, Rastogi A, Sahm F, Kessler T, Wick W, Bendszus M, Brugnara G, Vollmuth P. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes. Eur Radiol 2024; 34:2782-2790. [PMID: 37672053 PMCID: PMC10957611 DOI: 10.1007/s00330-023-10034-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. METHODS Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). RESULTS Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. CONCLUSION Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. CLINICAL RELEVANCE STATEMENT Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. KEY POINTS • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Division of Medical Image Computing (MIC), German Cancer Research Center (DFKZ), Heidelberg, Germany.
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11
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Duan W, Wang Z, Ma Z, Zheng H, Li Y, Pei D, Wang M, Qiu Y, Duan M, Yan D, Ji Y, Cheng J, Liu X, Zhang Z, Yan J. Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities. Cancer Sci 2024; 115:1261-1272. [PMID: 38279197 PMCID: PMC11007007 DOI: 10.1111/cas.16089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/28/2024] Open
Abstract
Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.
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Affiliation(s)
- Wenchao Duan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zilong Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zeyu Ma
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Hongwei Zheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yinhua Li
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongling Pei
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Minkai Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuning Qiu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Mengjiao Duan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongming Yan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuchen Ji
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jingliang Cheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Xianzhi Liu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zhenyu Zhang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jing Yan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
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12
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Lv C, Shu XJ, Chang H, Qiu J, Peng S, Yu K, Chen SB, Rao H. Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images. Front Neurosci 2024; 18:1349781. [PMID: 38560048 PMCID: PMC10979639 DOI: 10.3389/fnins.2024.1349781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/13/2024] [Indexed: 04/04/2024] Open
Abstract
Background and objectives Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET. Materials and methods For this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results. Results By employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet. Conclusion This study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches.
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Affiliation(s)
- Cheng Lv
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi Province, China
| | - Xu-Jun Shu
- Department of Neurosurgery, Nanjing Jinling Hospital, Nanjing, Jiangsu Province, China
| | - Hui Chang
- Department of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Jun Qiu
- Department of Critical Care Medicine, The Second People’s Hospital of Yibin, Yibin, Sichuan Province, China
| | - Shuo Peng
- Department of Computer Science, Jinggangshan University, Ji’an, China
| | - Keping Yu
- School of Science and Engineering, Hosei University, Tokyo, Japan
| | - Sheng-Bo Chen
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi Province, China
| | - Hong Rao
- Department of Neurosurgery, Nanjing Jinling Hospital, Nanjing, Jiangsu Province, China
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Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, Nie X, Yu J, Tong J, Liao X. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging 2024; 24:36. [PMID: 38486342 PMCID: PMC10938723 DOI: 10.1186/s40644-024-00682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
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Affiliation(s)
- Haiqing Fan
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Fang Gu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yongqin Xiong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Guipeng Wu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Nie
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Jing Yu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Juan Tong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
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Sawlani V, Jen JP, Patel M, Jain M, Haq H, Ughratdar I, Wykes V, Nagaraju S, Watts C, Pohl U. Multiparametric MRI and T2/FLAIR mismatch complements the World Health Organization 2021 classification for the diagnosis of IDH-mutant 1p/19q non-co-deleted/ATRX-mutant astrocytoma. Clin Radiol 2024; 79:197-204. [PMID: 38101998 DOI: 10.1016/j.crad.2023.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/14/2023] [Accepted: 11/14/2023] [Indexed: 12/17/2023]
Abstract
AIM To investigate whether T2-weighted imaging-fluid-attenuated inversion recovery (T2/FLAIR) mismatch, T2∗ dynamic susceptibility contrast (DSC) perfusion, and magnetic resonance spectroscopy (MRS) correlated with the histological diagnosis and grading of IDH (isocitrate dehydrogenase)-mutant, 1p/19q non-co-deleted/ATRX (alpha-thalassemia mental retardation X-linked)-mutant astrocytoma. MATERIALS Imaging of 101 IDH-mutant diffuse glioma cases of histological grades 2-3 (2019-2021) were analysed retrospectively by two neuroradiologists blinded to the molecular diagnosis. T2/FLAIR mismatch sign is used for radio-phenotyping, and pre-biopsy multiparametric MRI images were assessed for grading purposes. Cut-off values pre-determined for radiologically high-grade lesions were relative cerebral blood volume (rCBV) ≥2, choline/creatine ratio (Cho/Cr) ≥1.5 (30 ms echo time [TE]), Cho/Cr ≥1.8 (135 ms TE). RESULTS Sixteen of the 101 cases showed T2/FLAIR mismatch, all of which were histogenetically confirmed IDH-mutant 1p/19q non-co-deleted/ATRX mutant astrocytomas; 50% were grade 3 (8/16) and 50% grade 2 (8/16). None showed contrast enhancement. Nine of the 16 had adequate multiparametric MRI for analysis. Any positive value by combining rCBV ≥2 with Cho/Cr ≥1.5 (30 ms TE) or Cho/Cr ≥1.8 (135 ms TE) predicted grade 3 histology with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%. CONCLUSION The T2/FLAIR mismatch sign detected diffuse astrocytomas with 100% specificity. When combined with high Cho/Cr and raised rCBV, this predicted histological grading with high accuracy. The future direction for imaging should explore a similar integrated layered approach of 2021 classification of central nervous system (CNS) tumours combining radio-phenotyping and grading from structural and multiparametric imaging.
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Affiliation(s)
- V Sawlani
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK.
| | - J P Jen
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - M Patel
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - M Jain
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - H Haq
- Department of Neuroradiology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - I Ughratdar
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - V Wykes
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - S Nagaraju
- Department of Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - C Watts
- Department of Imaging, Neurosurgery and Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK; Department of Neurosurgery, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
| | - U Pohl
- Department of Neuropathology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS FT, Birmingham, UK
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Dono A, Torres J, Nunez L, Arevalo O, Rodriguez-Quinteros JC, Riascos RF, Kamali A, Tandon N, Ballester LY, Esquenazi Y. Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype. J Neurooncol 2024; 167:99-109. [PMID: 38351343 PMCID: PMC11227885 DOI: 10.1007/s11060-024-04575-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/16/2024] [Indexed: 07/04/2024]
Abstract
INTRODUCTION Recent studies have identified that glioblastoma IDH-wildtype consists of different molecular subgroups with distinct prognoses. In order to accurately describe and classify gliomas, the Visually AcceSAble Rembrandt Images (VASARI) system was developed. The goal of this study was to evaluate the VASARI characteristics in molecular subgroups of IDH-wildtype glioblastoma. METHODS A retrospective analysis of glioblastoma IDH- wildtype with comprehensive next-generation sequencing and pre-operative and post-operative MRI was performed. VASARI characteristics and 205 genes were evaluated. Multiple comparison adjustment by the Bejamin-Hochberg false discovery rate (BH-FDR) was performed. A 1:3 propensity score match (PSM) with a Caliper of 0.2 was done. RESULTS 178 patients with GBM IDH-WT met the inclusion criteria. 4q12 amplified patients (n = 20) were associated with cyst presence (30% vs. 12%, p = 0.042), decreased hemorrhage (35% vs. 62%, p = 0.028), and non-restricting/mixed (35%/60%) rather than restricting diffusion pattern (5%), meanwhile, 4q12 non-amplified patients had mostly restricting (47.4%) rather than a non-restricting/mixed diffusion pattern (28.4%/23.4%). This remained statistically significant after BH-FDR adjustment (p = 0.002). PSM by 4q12 amplification showed that diffusion characteristics continued to be significantly different. Among RB1-mutant patients, 96% had well-defined enhancing margins vs. 70.6% of RB1-WT (p = 0.018), however, this was not significant after BH-FDR or PSM. CONCLUSIONS Patients with glioblastoma IDH-wildtype harboring 4q12 amplification rarely have restricting DWI patterns compared to their wildtype counterparts, in which this DWI pattern is present in ~ 50% of patients. This suggests that some phenotypic imaging characteristics can be identified among molecular subtypes of IDH-wildtype glioblastoma.
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Affiliation(s)
- Antonio Dono
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
| | - Jose Torres
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
| | - Luis Nunez
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
| | - Octavio Arevalo
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
- Department of Radiology, LSU Health Shreveport, 71103, Shreveport, LA, USA
| | - Juan Carlos Rodriguez-Quinteros
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
| | - Roy F Riascos
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
- Memorial Hermann Hospital-TMC, 77030, Houston, TX, USA
| | - Arash Kamali
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
- Memorial Hermann Hospital-TMC, 77030, Houston, TX, USA
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA
- Memorial Hermann Hospital-TMC, 77030, Houston, TX, USA
| | - Leomar Y Ballester
- Department of Pathology, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA.
- Memorial Hermann Hospital-TMC, 77030, Houston, TX, USA.
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 77030, Houston, TX, USA.
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Jin W, Fatehi M, Guo R, Hamarneh G. Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task. Artif Intell Med 2024; 148:102751. [PMID: 38325929 DOI: 10.1016/j.artmed.2023.102751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 11/06/2023] [Accepted: 12/21/2023] [Indexed: 02/09/2024]
Abstract
Clinical evaluation evidence and model explainability are key gatekeepers to ensure the safe, accountable, and effective use of artificial intelligence (AI) in clinical settings. We conducted a clinical user-centered evaluation with 35 neurosurgeons to assess the utility of AI assistance and its explanation on the glioma grading task. Each participant read 25 brain MRI scans of patients with gliomas, and gave their judgment on the glioma grading without and with the assistance of AI prediction and explanation. The AI model was trained on the BraTS dataset with 88.0% accuracy. The AI explanation was generated using the explainable AI algorithm of SmoothGrad, which was selected from 16 algorithms based on the criterion of being truthful to the AI decision process. Results showed that compared to the average accuracy of 82.5±8.7% when physicians performed the task alone, physicians' task performance increased to 87.7±7.3% with statistical significance (p-value = 0.002) when assisted by AI prediction, and remained at almost the same level of 88.5±7.0% (p-value = 0.35) with the additional assistance of AI explanation. Based on quantitative and qualitative results, the observed improvement in physicians' task performance assisted by AI prediction was mainly because physicians' decision patterns converged to be similar to AI, as physicians only switched their decisions when disagreeing with AI. The insignificant change in physicians' performance with the additional assistance of AI explanation was because the AI explanations did not provide explicit reasons, contexts, or descriptions of clinical features to help doctors discern potentially incorrect AI predictions. The evaluation showed the clinical utility of AI to assist physicians on the glioma grading task, and identified the limitations and clinical usage gaps of existing explainable AI techniques for future improvement.
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Affiliation(s)
- Weina Jin
- School of Computing Science, Simon Fraser University, Burnaby, Canada.
| | - Mostafa Fatehi
- Division of Neurosurgery, The University of British Columbia, Vancouver, Canada.
| | - Ru Guo
- Division of Neurosurgery, The University of British Columbia, Vancouver, Canada.
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, Canada.
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Lai Y, Wu Y, Chen X, Gu W, Zhou G, Weng M. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:209-229. [PMID: 38343263 DOI: 10.1007/s10278-023-00905-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 03/02/2024]
Abstract
The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.
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Affiliation(s)
- Yuling Lai
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiyang Wu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiangyuan Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan.
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan.
| | - Guoxia Zhou
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
| | - Meilin Weng
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Mao Y, Jiang L, Wang JL, Chen FQ, Zhang WP, Liu ZX, Li C. Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography. Front Oncol 2024; 13:1268789. [PMID: 38273852 PMCID: PMC10808803 DOI: 10.3389/fonc.2023.1268789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Objectives To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features. Methods This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. Results To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness. Conclusion A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.
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Affiliation(s)
- Yi Mao
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - LiPing Jiang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Jing-Ling Wang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Fang-Qun Chen
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Wie-Ping Zhang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Zhi-Xing Liu
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
- Department of Ultrasound, GanJiang New District Peoples Hospital, Nanchang, Jiangxi, China
| | - Chen Li
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
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Cheng D, Zhuo Z, Du J, Weng J, Zhang C, Duan Y, Sun T, Wu M, Guo M, Hua T, Jin Y, Peng B, Li Z, Zhu M, Imami M, Bettegowda C, Sair H, Bai HX, Barkhof F, Liu X, Liu Y. A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images. Clin Cancer Res 2024; 30:150-158. [PMID: 37916978 DOI: 10.1158/1078-0432.ccr-23-1461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/11/2023] [Accepted: 10/31/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.
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Affiliation(s)
- Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Jiang Du
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, P.R. China
| | - Chengzhou Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, P.R. China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Min Guo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Tiantian Hua
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ying Jin
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Boyang Peng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | | | - Mingwang Zhu
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China
| | - Maliha Imami
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harrison X Bai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Frederik Barkhof
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
| | - Xing Liu
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
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Arrington D, Motley R, Colbert ZM, Lehman M, Ramachandran P. PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction. J Med Phys 2024; 49:12-21. [PMID: 38828062 PMCID: PMC11141757 DOI: 10.4103/jmp.jmp_120_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/27/2023] [Accepted: 01/07/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. Aims To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Materials and Methods Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. Results The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. Conclusion The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.
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Affiliation(s)
- Daniel Arrington
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Ryan Motley
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Zachery Morton Colbert
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Margot Lehman
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Prabhakar Ramachandran
- Department of Radiation Oncology, Cancer Services, Princess Alexandra Hospital, Woolloongabba, Australia
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21
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Jiang B, Zheng Y, She D, Xing Z, Cao D. MRI characteristics predict BRAF V600E status in gangliogliomas and pleomorphic xanthoastrocytomas and provide survival prognostication. Acta Radiol 2024; 65:33-40. [PMID: 37401109 DOI: 10.1177/02841851231183868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
BACKGROUND BRAF V600E mutation is a common genomic alteration in gangliogliomas (GGs) and pleomorphic xanthoastrocytomas (PXAs) with prognostic and therapeutic implications. PURPOSE To investigate the ability of magnetic resonance imaging (MRI) features to predict BRAF V600E status in GGs and PXAs and their prognostic values. MATERIAL AND METHODS A cohort of 44 patients with histologically confirmed GGs and PXAs was reviewed retrospectively. BRAF V600E status was determined by immunohistochemistry (IHC) staining and fluorescence quantitative polymerase chain reaction (PCR). Demographics and MRI characteristics of the two groups were evaluated and compared. Univariate and multivariate Cox regression analyses were performed to identify MRI features that were prognostic for progression-free survival (PFS). RESULTS T1/FLAIR ratio, enhancing margin, and mean relative apparent diffusion coefficient (rADCmea) value showed significant differences between the BRAF V600E-mutant and BRAF V600E-wild groups (all P < 0.05). Binary logistic regression analysis revealed only rADCmea value was the independent predictive factor for BRAF V600E status (P = 0.027). Univariate Cox regression analysis showed age at diagnosis (P = 0.032), WHO grade (P = 0.020), enhancing margin (P = 0.029), and rADCmea value (P = 0.005) were significant prognostic factors for PFS. In multivariate Cox regression analysis, increasing age (P = 0.040, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.002-1.079) and lower rADCmea values (P = 0.021, HR = 0.036, 95% CI = 0.002-0.602) were associated with poor PFS in GGs and PXAs. CONCLUSION Imaging features are potentially predictive of BRAF V600E status in GGs and PXAs. Furthermore, rADCmea value is a valuable prognostic factor for patients with GGs or PXAs.
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Affiliation(s)
- Bingqing Jiang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fujian, PR China
| | - Yingyan Zheng
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fujian, PR China
| | - Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fujian, PR China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fujian, PR China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fujian, PR China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fujian, PR China
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22
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Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay EZ, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo ID, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Sci Rep 2023; 13:22942. [PMID: 38135704 PMCID: PMC10746716 DOI: 10.1038/s41598-023-48918-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.
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Affiliation(s)
- Niklas Tillmanns
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Jan Lost
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Joanna Tabor
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Sagar Vasandani
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Shaurey Vetsa
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Kanat Yalcin
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Marc von Reppert
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Leon Jekel
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Sara Merkaj
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Divya Ramakrishnan
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Arman Avesta
- Department of Radiation Oncology, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Irene Dixe de Oliveira Santo
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Lan Jin
- R&D, Sema4, 333 Ludlow Street, North Tower, 8th Floor, Stamford, CT, 06902, USA
| | - Anita Huttner
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic Arizona, 5711 E Mayo Blvd, Phoenix, AZ, 85054, USA
| | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA, 92130, USA
| | - Sanjay Aneja
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Bernd Turowski
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Mariam Aboian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA.
- , New Haven, USA.
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23
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Lan L, Feng K, Wu Y, Zhang W, Wei L, Che H, Xue L, Gao Y, Tao J, Qian S, Cao W, Zhang J, Wang C, Tian M. Phenomic Imaging. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:597-612. [PMID: 38223684 PMCID: PMC10781914 DOI: 10.1007/s43657-023-00128-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 01/16/2024]
Abstract
Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.
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Affiliation(s)
- Lizhen Lan
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Kai Feng
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Yudan Wu
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Wenbo Zhang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ling Wei
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Huiting Che
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Yidan Gao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ji Tao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Shufang Qian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Wenzhao Cao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Fudan University, Shanghai, 200040 China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Mei Tian
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
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Pemberton HG, Wu J, Kommers I, Müller DMJ, Hu Y, Goodkin O, Vos SB, Bisdas S, Robe PA, Ardon H, Bello L, Rossi M, Sciortino T, Nibali MC, Berger MS, Hervey-Jumper SL, Bouwknegt W, Van den Brink WA, Furtner J, Han SJ, Idema AJS, Kiesel B, Widhalm G, Kloet A, Wagemakers M, Zwinderman AH, Krieg SM, Mandonnet E, Prados F, de Witt Hamer P, Barkhof F, Eijgelaar RS. Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms. Sci Rep 2023; 13:18911. [PMID: 37919354 PMCID: PMC10622563 DOI: 10.1038/s41598-023-44794-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023] Open
Abstract
This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jiaming Wu
- Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Ivar Kommers
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Domenique M J Müller
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Yipeng Hu
- Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Pierre A Robe
- Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hilko Ardon
- Department of Neurosurgery, St. Elisabeth Hospital, Tilburg, The Netherlands
| | - Lorenzo Bello
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Rossi
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Tommaso Sciortino
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Conti Nibali
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Shawn L Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Wim Bouwknegt
- Department of Neurosurgery, Medical Center Slotervaart, Amsterdam, The Netherlands
| | | | - Julia Furtner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Seunggu J Han
- Department of Neurological Surgery, Stanford University, Stanford, USA
| | - Albert J S Idema
- Department of Neurosurgery, Northwest Clinics, Alkmaar, The Netherlands
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Alfred Kloet
- Department of Neurosurgery, Medical Center Haaglanden, The Hague, The Netherlands
| | - Michiel Wagemakers
- Department of Neurosurgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Aeilko H Zwinderman
- Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, The Netherlands
| | - Sandro M Krieg
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Ferran Prados
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Philip de Witt Hamer
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Roelant S Eijgelaar
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
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Chen Q, Wang L, Xing Z, Wang L, Hu X, Wang R, Zhu YM. Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction. Comput Biol Med 2023; 166:107493. [PMID: 37774558 DOI: 10.1016/j.compbiomed.2023.107493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 09/15/2023] [Indexed: 10/01/2023]
Abstract
Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers. First, transformation-invariant features were extracted from multimodal MR images with a WS network, and then the multimodal WS features were used instead of the original images as the inputs of WSOFNet and were fully fused through an adaptive multimodal feature fusion module (AMF2M) and an orthogonal projection module (OPM). Finally, the fused features were input into a fully connected classifier to predict IDH mutation status. In addition, to achieve improved prediction accuracy, four auxiliary losses were also used in the feature extraction modules. The comparison results showed that the prediction area under the curve (AUC) of WSOFNet on a single-center dataset was 0.9966 and that on a multicenter dataset was approximately 0.9655, which was at least 3.9% higher than that of state-of-the-art methods. Moreover, the ablation experimental results also proved that the adaptive multimodal feature fusion strategy based on orthogonal projection could effectively improve the prediction performance of the model, especially for an external validation dataset.
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Affiliation(s)
- Qijian Chen
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Lihui Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
| | - Zhiyang Xing
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Li Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Xubin Hu
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Rongpin Wang
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Yue-Min Zhu
- University Lyon, INSA Lyon, CNRS, Inserm, IRP Metislab CREATIS UMR5220, U1206, Lyon 69621, France
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26
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Kim M, Ong KTI, Choi S, Yeo J, Kim S, Han K, Park JE, Kim HS, Choi YS, Ahn SS, Kim J, Lee SK, Sohn B. Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports. Eur Radiol 2023; 33:8017-8025. [PMID: 37566271 DOI: 10.1007/s00330-023-10061-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/18/2023] [Accepted: 06/22/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVES To evaluate the performance of natural language processing (NLP) models to predict isocitrate dehydrogenase (IDH) mutation status in diffuse glioma using routine MR radiology reports. MATERIALS AND METHODS This retrospective, multi-center study included consecutive patients with diffuse glioma with known IDH mutation status from May 2009 to November 2021 whose initial MR radiology report was available prior to pathologic diagnosis. Five NLP models (long short-term memory [LSTM], bidirectional LSTM, bidirectional encoder representations from transformers [BERT], BERT graph convolutional network [GCN], BioBERT) were trained, and area under the receiver operating characteristic curve (AUC) was assessed to validate prediction of IDH mutation status in the internal and external validation sets. The performance of the best performing NLP model was compared with that of the human readers. RESULTS A total of 1427 patients (mean age ± standard deviation, 54 ± 15; 779 men, 54.6%) with 720 patients in the training set, 180 patients in the internal validation set, and 527 patients in the external validation set were included. In the external validation set, BERT GCN showed the highest performance (AUC 0.85, 95% CI 0.81-0.89) in predicting IDH mutation status, which was higher than LSTM (AUC 0.77, 95% CI 0.72-0.81; p = .003) and BioBERT (AUC 0.81, 95% CI 0.76-0.85; p = .03). This was higher than that of a neuroradiologist (AUC 0.80, 95% CI 0.76-0.84; p = .005) and a neurosurgeon (AUC 0.79, 95% CI 0.76-0.84; p = .04). CONCLUSION BERT GCN was externally validated to predict IDH mutation status in patients with diffuse glioma using routine MR radiology reports with superior or at least comparable performance to human reader. CLINICAL RELEVANCE STATEMENT Natural language processing may be used to extract relevant information from routine radiology reports to predict cancer genotype and provide prognostic information that may aid in guiding treatment strategy and enabling personalized medicine. KEY POINTS • A transformer-based natural language processing (NLP) model predicted isocitrate dehydrogenase mutation status in diffuse glioma with an AUC of 0.85 in the external validation set. • The best NLP models were superior or at least comparable to human readers in both internal and external validation sets. • Transformer-based models showed higher performance than conventional NLP model such as long short-term memory.
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Affiliation(s)
- Minjae Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Kai Tzu-Iunn Ong
- Department of Artificial Intelligence, College of Computing, Yonsei University, Seoul, Korea
| | - Seonah Choi
- Department of Neurosurgery, Brain Tumor Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jinyoung Yeo
- Department of Artificial Intelligence, College of Computing, Yonsei University, Seoul, Korea
| | - Sooyon Kim
- Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Yoon Seong Choi
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Zhang S, Yin L, Ma L, Sun H. Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review. J Magn Reson Imaging 2023; 58:1338-1352. [PMID: 37083159 DOI: 10.1002/jmri.28737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Simin Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Lijuan Yin
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Lu Ma
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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Wu Z, Yang Y, Zha Y. Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma. Cancers (Basel) 2023; 15:4661. [PMID: 37760630 PMCID: PMC10527364 DOI: 10.3390/cancers15184661] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The complement component C5a receptor 1 (C5aR1) regulates cancer immunity. This retrospective study aimed to assess its prognostic value in high-grade glioma (HGG) and predict C5aR1 expression using a radiomics approach. METHODS Among 298 patients with HGG, 182 with MRI data were randomly divided into training and test groups for radiomics analysis. We examined the association between C5aR1 expression and prognosis through Kaplan-Meier and Cox regression analyses. We used maximum relevance-minimum redundancy and recursive feature elimination algorithms for radiomics feature selection. We then built a support vector machine (SVM) and a logistic regression model, investigating their performances using receiver operating characteristic, calibration curves, and decision curves. RESULTS C5aR1 expression was elevated in HGG and was an independent prognostic factor (hazard ratio = 3.984, 95% CI: 2.834-5.607). Both models presented with >0.8 area under the curve values in the training and test datasets, indicating efficient discriminatory ability, with SVM performing marginally better. The radiomics score calculated using the SVM model correlated significantly with overall survival (p < 0.01). CONCLUSIONS Our results highlight C5aR1's role in HGG development and prognosis, supporting its potential as a prognostic biomarker. Our radiomics model can noninvasively and effectively predict C5aR1 expression and patient prognosis in HGG.
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Affiliation(s)
| | | | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430000, China; (Z.W.); (Y.Y.)
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30
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Kong X, Mao Y, Xi F, Li Y, Luo Y, Ma J. Development of a nomogram based on radiomics and semantic features for predicting chromosome 7 gain/chromosome 10 loss in IDH wild-type histologically low-grade gliomas. Front Oncol 2023; 13:1196614. [PMID: 37781185 PMCID: PMC10541227 DOI: 10.3389/fonc.2023.1196614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
Purpose To predict chromosome 7 gain and chromosome 10 loss (+7/-10) in IDH wild-type (IDH-wt) histologically low-grade gliomas (LGG) by machine learning models based on MRI radiomics and semantic features. Methods A total of 122 patients diagnosed as IDH-wt histologically LGG were retrospectively included in this study. The patients were randomly divided into a training group and a test group in a ratio of 7:3. The radiomics features were extracted from axial T1WI, T2WI, FLAIR and CET1 sequences, respectively. The distance correlation (DC) and least absolute shrinkage and selection operator (LASSO) were used to select the radiomics signatures. Three machine learning algorithms including neural network (NN), support vector machine (SVM), and linear discriminant analysis (LDA) were used to construct radiomics models. In addition, a nomogram was developed by combining the optimal radiomics signature with clinical risk factors, and the potential clinical utility of the nomogram was evaluated using decision curve analysis. Results The LDA+DC model was identified as the optimal classifier among the six radiomics models. Necrosis was determined as a risk factor for +7/-10 in IDH-wt histologically LGG. The nomogram achieved the best performance, with an AUC of 0.854 and an accuracy of 0.778 in the independent test group. The decision curve of the nomogram confirmed its clinical usefulness in a wide range of thresholds. Conclusion The nomogram combining radiomics and semantic features can predict the +7/-10 status effectively, which may contribute to the risk stratification and individualized treatment planning of patients with IDH-wt histologically LGG.
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Affiliation(s)
- Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yu Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fengjun Xi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Radiology, Beijing Fengtai Hospital, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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31
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de Godoy LL, Lim KC, Rajan A, Verma G, Hanaoka M, O’Rourke DM, Lee JYK, Desai A, Chawla S, Mohan S. Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers (Basel) 2023; 15:4453. [PMID: 37760422 PMCID: PMC10526791 DOI: 10.3390/cancers15184453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/22/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. MATERIALS AND METHODS Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér-Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann-Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. RESULTS Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. CONCLUSIONS Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas.
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Affiliation(s)
- Laiz Laura de Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Kheng Choon Lim
- Department of Neuroradiology, Singapore General Hospital, Singapore 169609, Singapore;
| | - Archith Rajan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Mauro Hanaoka
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Donald M. O’Rourke
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (D.M.O.); (J.Y.K.L.)
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - John Y. K. Lee
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (D.M.O.); (J.Y.K.L.)
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - Arati Desai
- Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19014, USA
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; (L.L.d.G.); (A.R.); (M.H.); (S.M.)
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Bodden J, Dieckmeyer M, Sollmann N, Rühling S, Prucker P, Löffler MT, Burian E, Subburaj K, Zimmer C, Kirschke JS, Baum T. Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework. Quant Imaging Med Surg 2023; 13:5472-5482. [PMID: 37711780 PMCID: PMC10498219 DOI: 10.21037/qims-23-19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/08/2023] [Indexed: 09/16/2023]
Abstract
Background To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment. Methods Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability. Results SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%. Conclusions Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.
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Affiliation(s)
- Jannis Bodden
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Sebastian Rühling
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maximilian T. Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karupppasamy Subburaj
- Department of Mechanical and Production Engineering, Aarhus University, Aarhus, Denmark
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Pan T, Su CQ, Tang WT, Lin J, Lu SS, Hong XN. Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas. Acta Radiol 2023; 64:2552-2560. [PMID: 37331987 DOI: 10.1177/02841851231180291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
BACKGROUND Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. PURPOSE To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. MATERIAL AND METHODS This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. RESULTS Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). CONCLUSION Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.
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Affiliation(s)
- Ting Pan
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - Chun-Qiu Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wen-Tian Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Jie Lin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xun-Ning Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas. J Magn Reson Imaging 2023; 58:677-689. [PMID: 37069792 DOI: 10.1002/jmri.28743] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. Importantly, molecular biomarkers that provide important prognostic information are now a parameter for establishing tumor grades in gliomas. Understanding the 2021 WHO classification is crucial for radiologists for daily imaging interpretation as well as communication with clinicians. Although imaging features are not included in the 2021 WHO classification, imaging can serve as a powerful tool to impact the clinical practice not only prior to tissue confirmation but beyond. This review represents the first of a three-installment review series on the 2021 WHO classification for gliomas, glioneuronal tumors, and neuronal tumors and implications on imaging diagnosis. This Part 1 Review focuses on the major changes to the classification of gliomas and imaging findings on adult-type diffuse gliomas. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Philipp Vollmuth
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
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Gu S, Qian J, Yang L, Sun Z, Hu C, Wang X, Hu S, Xie Y. Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma. BMC Med Imaging 2023; 23:116. [PMID: 37653513 PMCID: PMC10472728 DOI: 10.1186/s12880-023-01086-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients. OBJECTIVE Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma. METHODS Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient's Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed. RESULTS Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively. CONCLUSION Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.
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Affiliation(s)
- Siqian Gu
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Jing Qian
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Ling Yang
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China.
| | - Zhilei Sun
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hosptial of Soochow University, 215006, Suzhou, China
| | - Yuyang Xie
- Soochow University, 215006, Suzhou, China
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Li L, Fu Y, Zhang Y, Mao Y, Huang D, Yi X, Wang J, Tan Z, Jiang M, Chen BT. Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi-center cohort study of 114 cases. Cancer Med 2023; 12:16195-16206. [PMID: 37376821 PMCID: PMC10469843 DOI: 10.1002/cam4.6279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. METHODS A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. RESULTS Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor-enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. CONCLUSION We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.
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Affiliation(s)
- Liyan Li
- Department of RadiologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningP. R. China
| | - Yan Fu
- Department of RadiologyXiangya Hospital, Central South UniversityChangshaP. R. China
| | - Yinping Zhang
- Department of RadiologyXiangya Hospital, Central South UniversityChangshaP. R. China
| | - Yipu Mao
- Department of RadiologyNanning First People's HospitalNanningP. R. China
| | - Deyou Huang
- Department of RadiologyAffiliated Hospital of Youjiang Medical University for NationalitiesBaiseP. R. China
| | - Xiaoping Yi
- Department of RadiologyXiangya Hospital, Central South UniversityChangshaP. R. China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic TechnologyXiangya HospitalChangshaP. R. China
- National Clinical Research Center for Geriatric DisordersXiangya Hospital, Central South UniversityChangshaP. R. China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya HospitalCentral South UniversityChangshaP. R. China
- Hunan Engineering Research Center of Skin Health and DiseaseXiangya Hospital, Central South UniversityChangshaP. R. China
- Department of DermatologyXiangya Hospital, Central South UniversityChangshaP. R. China
| | - Jing Wang
- Department of NeurologyXiangya Hospital, Central South UniversityChangshaP. R. China
| | - Zeming Tan
- Department of NeurosurgeryXiangya Hospital, Central South UniversityChangshaP. R. China
| | - Muliang Jiang
- Department of RadiologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningP. R. China
| | - Bihong T. Chen
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCaliforniaUSA
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Lee SJ, Park JE, Park SY, Kim YH, Hong CK, Kim JH, Kim HS. Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. Korean J Radiol 2023; 24:772-783. [PMID: 37500578 PMCID: PMC10400365 DOI: 10.3348/kjr.2022.0919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 04/05/2023] [Accepted: 05/20/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. MATERIALS AND METHODS This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-co-deleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. RESULTS Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). CONCLUSION Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.
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Affiliation(s)
- So Jeong Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Seo Young Park
- Deparment of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chang Ki Hong
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Gozzi F, Bertolini M, Gentile P, Verzellesi L, Trojani V, De Simone L, Bolletta E, Mastrofilippo V, Farnetti E, Nicoli D, Croci S, Belloni L, Zerbini A, Adani C, De Maria M, Kosmarikou A, Vecchi M, Invernizzi A, Ilariucci F, Zanelli M, Iori M, Cimino L. Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma. Diagnostics (Basel) 2023; 13:2451. [PMID: 37510195 PMCID: PMC10378347 DOI: 10.3390/diagnostics13142451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.
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Affiliation(s)
- Fabrizio Gozzi
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Bertolini
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Pietro Gentile
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Clinical and Experimental Medicine Ph.D. Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Laura Verzellesi
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Valeria Trojani
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca De Simone
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Elena Bolletta
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | | | - Enrico Farnetti
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Davide Nicoli
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Stefania Croci
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Lucia Belloni
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Zerbini
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Chantal Adani
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Michele De Maria
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Areti Kosmarikou
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Vecchi
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Invernizzi
- Eye Clinic, Luigi Sacco Hospital, Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy
- Faculty of Health and Medicine, Save Sight Institute, University of Sydney, Sydney, NSW 2000, Australia
| | | | - Magda Zanelli
- Surgical Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca Cimino
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, with Interest in Transplants, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, 41124 Modena, Italy
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Shao H, Chen N, Su X, Zheng L, Yang X, Wan X, Zhang S, Tan Q, Li S, Gong Q, Yue Q. Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart. World Neurosurg 2023; 175:e1283-e1291. [PMID: 37149089 DOI: 10.1016/j.wneu.2023.04.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/26/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas. METHODS Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma. RESULTS There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618). CONCLUSIONS Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.
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Affiliation(s)
- Hanbing Shao
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Ni Chen
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China; Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaorui Su
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Linmao Zheng
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Xibiao Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Simin Zhang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Qiaoyue Tan
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Division of Radiation Physics, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| | - Shuang Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China.
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Lasocki A, Buckland ME, Molinaro T, Xie J, Whittle JR, Wei H, Gaillard F. Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas. Neuroradiology 2023:10.1007/s00234-023-03175-0. [PMID: 37316586 DOI: 10.1007/s00234-023-03175-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/04/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas. METHODS Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis. RESULTS 8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis. CONCLUSION MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Grattan St, Melbourne, Melbourne, Victoria, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
- School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Tahlia Molinaro
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - James R Whittle
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Frank Gaillard
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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Rauch P, Stefanits H, Aichholzer M, Serra C, Vorhauer D, Wagner H, Böhm P, Hartl S, Manakov I, Sonnberger M, Buckwar E, Ruiz-Navarro F, Heil K, Glöckel M, Oberndorfer J, Spiegl-Kreinecker S, Aufschnaiter-Hiessböck K, Weis S, Leibetseder A, Thomae W, Hauser T, Auer C, Katletz S, Gruber A, Gmeiner M. Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma. Sci Rep 2023; 13:9494. [PMID: 37302994 DOI: 10.1038/s41598-023-36298-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/31/2023] [Indexed: 06/13/2023] Open
Abstract
Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.
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Affiliation(s)
- P Rauch
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - H Stefanits
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria.
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria.
| | - M Aichholzer
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - C Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital, University of Zurich, Zurich, Switzerland
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - D Vorhauer
- Institute of Statistics, Johannes Kepler University, Linz, Austria
| | - H Wagner
- Institute of Statistics, Johannes Kepler University, Linz, Austria
| | - P Böhm
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - S Hartl
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | | | - M Sonnberger
- Institute of Neuroradiology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - E Buckwar
- Institute of Stochastics, Johannes Kepler University, Linz, Austria
| | - F Ruiz-Navarro
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - K Heil
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - M Glöckel
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - J Oberndorfer
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - S Spiegl-Kreinecker
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - K Aufschnaiter-Hiessböck
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - S Weis
- Institute of Pathology and Neuropathology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - A Leibetseder
- Department of Neurology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - W Thomae
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - T Hauser
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - C Auer
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - S Katletz
- Department of Neurology, Kepler University Hospital and Johannes Kepler University, Linz, Austria
| | - A Gruber
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
| | - M Gmeiner
- Department of Neurosurgery, Kepler University Hospital, Wagner-Jauregg Weg 15, 4020, Linz, Austria
- Johannes Kepler University, Altenberger Strasse 69, 4040, Linz, Austria
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Cui J, Miao X, Yanghao X, Qin X. Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases. Front Neurol 2023; 14:1171167. [PMID: 37360350 PMCID: PMC10288367 DOI: 10.3389/fneur.2023.1171167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Background The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.
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Milara E, Alonso R, Masseing L, Seiffert AP, Gómez-Grande A, Gómez EJ, Martínez-López J, Sánchez-González P. Radiomics analysis of bone marrow biopsy locations in [ 18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma. Phys Eng Sci Med 2023; 46:903-913. [PMID: 37155114 DOI: 10.1007/s13246-023-01265-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 04/19/2023] [Indexed: 05/10/2023]
Abstract
The combination of visual assessment of whole body [18F]FDG PET images and evaluation of bone marrow samples by Multiparameter Flow Cytometry (MFC) or Next-Generation Sequencing (NGS) is currently the most common clinical practice for the detection of Measurable Residual Disease (MRD) in Multiple Myeloma (MM) patients. In this study, radiomic features extracted from the bone marrow biopsy locations are analyzed and compared to those extracted from the whole bone marrow in order to study the representativeness of these biopsy locations in the image-based MRD assessment. Whole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A methodology for the segmentation of biopsy sites from PET images, including sternum and posterior iliac crest, and their subsequent quantification is proposed. First, starting from the bone marrow segmentation, a segmentation of the biopsy sites is performed. Then, segmentations are quantified extracting SUV metrics and radiomic features from the [18F]FDG PET images and are evaluated by Mann-Whitney U-tests as valuable features differentiating PET+/PET- and MFC+ /MFC- groups. Moreover, correlation between whole bone marrow and biopsy sites is studied by Spearman ρ rank. Classification performance of the radiomics features is evaluated applying seven machine learning algorithms. Statistical analyses reveal that some images features are significant in PET+/PET- differentiation, such as SUVmax, Gray Level Non-Uniformity or Entropy, especially with a balanced database where 16 of the features show a p value < 0.001. Correlation analyses between whole bone marrow and biopsy sites results in significant and acceptable coefficients, with 11 of the variables reaching a correlation coefficient greater than 0.7, with a maximum of 0.853. Machine learning algorithms demonstrate high performances in PET+/PET- classification reaching a maximum AUC of 0.974, but not for MFC+/MFC- classification. The results demonstrate the representativeness of sample sites as well as the effectiveness of extracted features (SUV metrics and radiomic features) from the [18F]FDG PET images in MRD assessment in MM patients.
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Affiliation(s)
- Eva Milara
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | - Rafael Alonso
- Department of Hematology and Instituto de Investigación Sanitaria (imas12), Hospital Universitario, 12 de Octubre, 28041, Madrid, Spain
- Clinical Research Hematology Unit, Centro Nacional de Investigaciones Oncológicas (CNIO), 28029, Madrid, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
- Facultad de Medicina, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Lena Masseing
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | - Alexander P Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | - Adolfo Gómez-Grande
- Facultad de Medicina, Universidad Complutense de Madrid, 28040, Madrid, Spain
- Department of Nuclear Medicine, Hospital Universitario, 12 de Octubre, 28041, Madrid, Spain
| | - Enrique J Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040, Madrid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Joaquín Martínez-López
- Department of Hematology and Instituto de Investigación Sanitaria (imas12), Hospital Universitario, 12 de Octubre, 28041, Madrid, Spain
- Clinical Research Hematology Unit, Centro Nacional de Investigaciones Oncológicas (CNIO), 28029, Madrid, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Madrid, Spain
- Facultad de Medicina, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040, Madrid, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029, Madrid, Spain.
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Yang J, Li L, Luo T, Nie C, Fan R, Li D, Yang R, Zhou C, Li Q, Hu X, Chen W. Cyclin-Dependent Kinase Inhibitor 2A/B Homozygous Deletion Prediction and Survival Analysis. Brain Sci 2023; 13:brainsci13040548. [PMID: 37190513 DOI: 10.3390/brainsci13040548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/09/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Cyclin-Dependent Kinase Inhibitor 2A/B (CDKN2A/B) homozygous deletion was a significant prognostic factor for gliomas and affected the treatment strategy. However, the radiomic features of CDKN2A/B homozygous deletion in gliomas have not been developed, and whether the radiomic features and molecular subgroups can provide prognostic value in low-grade gliomas (LGGs) has yet to be studied. Thus, this study aimed to develop a predictive model of CDKN2A/B in gliomas and investigate the prognostic value of this biomarker and radiomic features in isocitrate dehydrogenase (IDH)-mutant LGGs. First, we developed the predictive model of CDKN2A/B homozygous deletion in 292 patients. The results revealed that radiomic features predict CDKN2A/B homozygous deletion with high accuracy and reliability. Subsequently, the prognostic survival models of 104 patients (IDH-mutant LGGs) were established, which provided an essential value for prognostic evaluation and indicated that CDKN2A/B homozygous deletion can be used as an independent predictor of prognosis in LGGs.
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Gemini L, Tortora M, Giordano P, Prudente ME, Villa A, Vargas O, Giugliano MF, Somma F, Marchello G, Chiaramonte C, Gaetano M, Frio F, Di Giorgio E, D'Avino A, Tortora F, D'Agostino V, Negro A. Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? J Imaging 2023; 9:jimaging9040075. [PMID: 37103226 PMCID: PMC10143099 DOI: 10.3390/jimaging9040075] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/04/2023] [Accepted: 03/21/2023] [Indexed: 04/28/2023] Open
Abstract
(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.
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Affiliation(s)
- Laura Gemini
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Mario Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Pasqualina Giordano
- Oncology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Maria Evelina Prudente
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alessandro Villa
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Ottavia Vargas
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | | | - Francesco Somma
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Giulia Marchello
- CNRS, Laboratoire J.A. Dieudonné, Inria, Universitè Côte d'Azur, Avenue Valrose, 06108 Nice, France
| | - Carmela Chiaramonte
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Marcella Gaetano
- Radiotherapy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Federico Frio
- Neurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Eugenio Di Giorgio
- Nuclear Medicine Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alfredo D'Avino
- Pathological Anatomy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Fabio Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 80131 Naples, Italy
| | - Vincenzo D'Agostino
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
| | - Alberto Negro
- Neuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy
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Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
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Du N, Shu W, Li K, Deng Y, Xu X, Ye Y, Tang F, Mao R, Lin G, Li S, Fang X. An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma. J Transl Med 2023; 21:119. [PMID: 36774480 PMCID: PMC9922464 DOI: 10.1186/s12967-023-03950-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/01/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. METHODS Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. RESULTS ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879). CONCLUSIONS There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.
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Affiliation(s)
- Ningfang Du
- grid.8547.e0000 0001 0125 2443Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Kefeng Li
- grid.266100.30000 0001 2107 4242School of Medicine, University of California, San Diego, CA USA ,Faculty of Health Sciences and Sports, Macao Polytechnic University, Macao SAR, China
| | - Yao Deng
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Xinxin Xu
- grid.8547.e0000 0001 0125 2443Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- grid.8547.e0000 0001 0125 2443Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Feng Tang
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Renling Mao
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China.
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Kihira S, Derakhshani A, Leung M, Mahmoudi K, Bauer A, Zhang H, Polson J, Arnold C, Tsankova NM, Hormigo A, Salehi B, Pham N, Ellingson BM, Cloughesy TF, Nael K. Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign. Cancers (Basel) 2023; 15:cancers15041037. [PMID: 36831380 PMCID: PMC9954034 DOI: 10.3390/cancers15041037] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/10/2023] Open
Abstract
PURPOSE The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.
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Affiliation(s)
- Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Ahrya Derakhshani
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Michael Leung
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Haoyue Zhang
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer Polson
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Corey Arnold
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nadejda M. Tsankova
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adilia Hormigo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Banafsheh Salehi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Nancy Pham
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Benjamin M. Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Timothy F. Cloughesy
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Correspondence: ; Tel.: +1-310-267-5932
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Wang X, Dai Y, Lin H, Cheng J, Zhang Y, Cao M, Zhou Y. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol 2023; 33:3312-3321. [PMID: 36738323 DOI: 10.1007/s00330-023-09412-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/21/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model. METHODS Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation. RESULTS Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set. CONCLUSION The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas. KEY POINTS • Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.
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Affiliation(s)
- Xiaoqing Wang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jiahui Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengqiu Cao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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