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Akkurt BH, Spille DC, Peetz-Dienhart S, Kiolbassa NM, Mawrin C, Musigmann M, Heindel WL, Paulus W, Stummer W, Mannil M, Brokinkel B. Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas. Cancers (Basel) 2023; 15:4415. [PMID: 37686690 PMCID: PMC10486806 DOI: 10.3390/cancers15174415] [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: 07/26/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
PURPOSE In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. METHODS Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. RESULTS A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. CONCLUSIONS Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.
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Affiliation(s)
- Burak Han Akkurt
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
| | | | - Susanne Peetz-Dienhart
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
| | - Nora Maren Kiolbassa
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
| | - Christian Mawrin
- Department of Neuropathology, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Manfred Musigmann
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
| | | | - Werner Paulus
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
| | - Walter Stummer
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
| | - Manoj Mannil
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
- Institute for Diagnostic and Interventional Radiology, Caritas-Hospital, DE-97980 Bad Mergentheim, Germany
| | - Benjamin Brokinkel
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
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Chilaca-Rosas MF, Contreras-Aguilar MT, Garcia-Lezama M, Salazar-Calderon DR, Vargas-Del-Angel RG, Moreno-Jimenez S, Piña-Sanchez P, Trejo-Rosales RR, Delgado-Martinez FA, Roldan-Valadez E. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation. Diagnostics (Basel) 2023; 13:2669. [PMID: 37627927 PMCID: PMC10453217 DOI: 10.3390/diagnostics13162669] [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: 06/22/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is associated with a worse prognosis. PATIENTS AND METHODS This was a retrospective study. From a database of 126 DMG patients (children, adolescents, and young adults), only 12 had H3.3K27M mutation and available brain magnetic resonance DICOM file. The MRI T1 post-gadolinium and T2 sequences were uploaded to LIFEx software to post-process and extract radiomic features. Statistical analysis included normal distribution tests and the Mann-Whitney U test performed using IBM SPSS® (Version 27.0.0.1, International Business Machines Corp., Armonk, NY, USA), considering a significant statistical p-value ≤ 0.05. RESULTS EMNT vs. Tumor: From the T1 sequence 10 radiomics were identified, and 14 radiomics from the T2 sequence, but only one radiomic identified viable tumors in both sequences (p < 0.05) (DISCRETIZED_Q1). Peritumoral edema vs. EMNT: From the T1 sequence, five radiomics were identified, and four radiomics from the T2 sequence. However, four radiomics could discriminate peritumoral edema in both sequences (p < 0.05) (CONVENTIONAL_Kurtosis, CONVENTIONAL_ExcessKurtosis, DISCRETIZED_Kurtosis, and DISCRETIZED_ExcessKurtosis). There were no radiomics useful for distinguishing tumor tissue from peritumoral edema in both sequences. CONCLUSIONS Less than 5% of the radiomic characteristics identified tumor regions of medical-clinical interest in T1 and T2 sequences of conventional magnetic resonance imaging. The first-order and second-order radiomic features suggest support to investigators and clinicians for careful evaluation for diagnosis, patient classification, and multimodality cancer treatment planning.
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Affiliation(s)
- Maria-Fatima Chilaca-Rosas
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Manuel-Tadeo Contreras-Aguilar
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Melissa Garcia-Lezama
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
| | - David-Rafael Salazar-Calderon
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | | | - Sergio Moreno-Jimenez
- Neurological Center, Neurosurgery Department of National Institute of Neurology and Neurosurgery, Mexico City 14269, Mexico;
- Neurological Center, Neurosurgery Department of American British Cowdray Medical Center, Mexico City 01120, Mexico
| | - Patricia Piña-Sanchez
- Oncology Diagnostic, Unidad de Investigacion Medica en Enfermedades Oncologicas U.I.M.E.O, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Raul-Rogelio Trejo-Rosales
- Medical Oncology, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Felipe-Alfredo Delgado-Martinez
- Magnetic Resonance Service, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico;
| | - Ernesto Roldan-Valadez
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
- Department of Radiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119992 Moscow, Russia
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [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: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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MGMT gene promoter methylation by pyrosequencing method correlates volumetric response and neurological status in IDH wild-type glioblastomas. J Neurooncol 2022; 157:561-571. [PMID: 35397757 PMCID: PMC9072488 DOI: 10.1007/s11060-022-03999-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/24/2022] [Indexed: 01/03/2023]
Abstract
Purpose Although the usefulness of O6-methylguanine DNA methyltransferase (MGMT) promoter methylation analysis for predicting response to chemoradiotherapy and the prognosis of patients with glioblastoma has been widely reported, there is still no consensus regarding how to define MGMT promoter methylation percentage (MGMTpm%) cutoffs by pyrosequencing method. The aim of this study was to determine the optimal cutoff value of MGMT promoter methylation status using volumetric analysis focused on the tumor volume ratio (TVR) measured by MRI. Methods This retrospective study included newly diagnosed IDH wild-type glioblastoma patients with residual tumor after surgery, followed by local radiotherapy with temozolomide. TVR was defined as the tumor volume at 6 months after the initial chemoradiotherapy administration divided by the tumor volume before the start of therapy. The mean MGMTpm% of 16 CpG islands (74–89) was analyzed using pyrosequencing. We statistically analyzed the correlation between MGMTpm%, TVR, and change in Karnofsky performance status. Results The study included 44 patients with residual tumors. Thirteen (92.9%) of 14 patients with MGMTpm% ≥ 23.9% showed 50% or more volumetric response, leading to prolonged survival, and 17 (70.8%) of 24 patients with MGMTpm% < 8.2% had progressive disease after initial chemoradiotherapy administration. Three (50.0%) of six patients with MGMTpm% 8.2% to < 23.9% had stable disease or partial response. Conclusion Evaluation of MGMTpm% by pyrosequencing is important in predicting the volumetric response and prognosis of glioblastoma patients with residual tumors.
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Krauze AV, Zhuge Y, Zhao R, Tasci E, Camphausen K. AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models. JOURNAL OF BIOTECHNOLOGY AND BIOMEDICINE 2022; 5:1-19. [PMID: 35106480 PMCID: PMC8802234 DOI: 10.26502/jbb.2642-91280046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.
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Affiliation(s)
- A V Krauze
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - Y Zhuge
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - R Zhao
- University of British Columbia, Faculty of Medicine, 317 - 2194 Health Sciences Mall, Vancouver, Canada
| | - E Tasci
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - K Camphausen
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
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