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Zhou Q, Ke X, Man J, Jiang J, Ren J, Xue C, Zhang B, Zhang P, Zhao J, Zhou J. Integrated MRI radiomics, tumor microenvironment, and clinical risk factors for improving survival prediction in patients with glioblastomas. Strahlenther Onkol 2024:10.1007/s00066-024-02283-x. [PMID: 39249499 DOI: 10.1007/s00066-024-02283-x] [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: 12/27/2023] [Accepted: 07/14/2024] [Indexed: 09/10/2024]
Abstract
PURPOSE To construct a comprehensive model for predicting the prognosis of patients with glioblastoma (GB) using a radiomics method and integrating clinical risk factors, tumor microenvironment (TME), and imaging characteristics. MATERIALS AND METHODS In this retrospective study, we included 148 patients (85 males and 63 females; median age 53 years) with isocitrate dehydrogenase-wildtype GB between January 2016 and April 2022. Patients were randomly divided into the training (n = 104) and test (n = 44) sets. The best feature combination related to GB overall survival (OS) was selected using LASSO Cox regression analyses. Clinical, radiomics, clinical-radiomics, clinical-TME, and clinical-radiomics-TME models were established. The models' concordance index (C-index) was evaluated. The survival curve was drawn using the Kaplan-Meier method, and the prognostic stratification ability of the model was tested. RESULTS LASSO Cox analyses were used to screen the factors related to OS in patients with GB, including MGMT (hazard ratio [HR] = 0.642; 95% CI 0.414-0.997; P = 0.046), TERT (HR = 1.755; 95% CI 1.095-2.813; P = 0.019), peritumoral edema (HR = 1.013; 95% CI 0.999-1.027; P = 0.049), tumor purity (TP; HR = 0.982; 95% CI 0.964-1.000; P = 0.054), CD163 + tumor-associated macrophages (TAMs; HR = 1.049; 95% CI 1.021-1.078; P < 0.001), CD68 + TAMs (HR = 1.055; 95% CI 1.018-1.093; P = 0.004), and the six radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C‑index was 0.768 (0.717-0.819). The AUC of 1‑, 2‑, and 3‑year OS prediction in the test set was 0.842, 0.844, and 0.795, respectively. CONCLUSION The clinical-radiomics-TME model is the most effective for predicting the survival of patients with GB. Radiomics features, TP, and TAMs play important roles in the prognostic model.
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Affiliation(s)
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Jiangwei Man
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China
- Department of Surgical, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Peng Zhang
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China
- Department of Pathology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
- Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China.
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, 730030, Lanzhou, Gansu, China.
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Cho NS, Hagiwara A, Sanvito F, Ellingson BM. A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors. Neuroradiology 2023; 65:559-568. [PMID: 36301349 PMCID: PMC9905164 DOI: 10.1007/s00234-022-03072-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods. METHODS Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated. RESULTS Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar. CONCLUSION The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Francesco Sanvito
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Benjamin M Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Ladenhauf VK, Galijasevic M, Kerschbaumer J, Freyschlag CF, Nowosielski M, Birkl-Toeglhofer AM, Haybaeck J, Gizewski ER, Mangesius S, Grams AE. Peritumoral ADC Values Correlate with the MGMT Methylation Status in Patients with Glioblastoma. Cancers (Basel) 2023; 15:cancers15051384. [PMID: 36900177 PMCID: PMC10000073 DOI: 10.3390/cancers15051384] [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: 01/11/2023] [Revised: 02/14/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.
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Affiliation(s)
- Valentin Karl Ladenhauf
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Malik Galijasevic
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Correspondence: ; Tel.: +43-50-504-83248
| | - Johannes Kerschbaumer
- Department of Neurosurgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | | | - Martha Nowosielski
- Department of Neurology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Anna Maria Birkl-Toeglhofer
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Johannes Haybaeck
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Diagnostic & Research Center for Molecular BioMedicine, Institute of Pathology, Medical University of Graz, 8010 Graz, Austria
| | - Elke Ruth Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Astrid Ellen Grams
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Qing Z, Xiaoai K, Caiqiang X, Shenglin L, Xiaoyu H, Bin Z, Junlin Z. Nomogram for predicting early recurrence in patients with high-grade gliomas. World Neurosurg 2022; 164:e619-e628. [PMID: 35589036 DOI: 10.1016/j.wneu.2022.05.039] [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: 02/19/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters. METHODS 154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram. RESULTS Univariate analysis showed that the number of tumors (OR, 0.258; 95% CI: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI 0.942, 0.988; P = 0.003; mean in the recurrence group 22.04±17.21 mm; mean in the non-recurrence group 14.22±12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI 1.874, 10.353; P= 0.001), and MGMT (OR, 2.389; 95% CI 1.234, 4.628; P= 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI 1.602, 10.013; P= 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%. CONCLUSION A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG .
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Affiliation(s)
- Zhou Qing
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Ke Xiaoai
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Xue Caiqiang
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Li Shenglin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Huang Xiaoyu
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Zhang Bin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School,Lanzhou University, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China
| | - Zhou Junlin
- Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,China.
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5
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Defeudis A, Mazzetti S, Panic J, Micilotta M, Vassallo L, Giannetto G, Gatti M, Faletti R, Cirillo S, Regge D, Giannini V. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur Radiol Exp 2022; 6:19. [PMID: 35501512 PMCID: PMC9061921 DOI: 10.1186/s41747-022-00272-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/23/2022] [Indexed: 12/29/2022] Open
Abstract
Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00272-2.
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Affiliation(s)
- Arianna Defeudis
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy. .,Department of Surgical Sciences, University of Turin, Turin, Italy. .,Radiology Unit, SS Annunziata Savigliano Hospital, Cuneo, Italy.
| | - Simone Mazzetti
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Jovana Panic
- Department of Surgical Sciences, University of Turin, Turin, Italy.,Politecnico di Torino, Electronic and Telecommunication Department (DET), Turin, Italy
| | | | - Lorenzo Vassallo
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Giuliana Giannetto
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | | | - Daniele Regge
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Valentina Giannini
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
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Cheng J, Liu J, Yue H, Bai H, Pan Y, Wang J. Prediction of Glioma Grade Using Intratumoral and Peritumoral Radiomic Features From Multiparametric MRI Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1084-1095. [PMID: 33104503 DOI: 10.1109/tcbb.2020.3033538] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The accurate prediction of glioma grade before surgery is essential for treatment planning and prognosis. Since the gold standard (i.e., biopsy)for grading gliomas is both highly invasive and expensive, and there is a need for a noninvasive and accurate method. In this study, we proposed a novel radiomics-based pipeline by incorporating the intratumoral and peritumoral features extracted from preoperative mpMRI scans to accurately and noninvasively predict glioma grade. To address the unclear peritumoral boundary, we designed an algorithm to capture the peritumoral region with a specified radius. The mpMRI scans of 285 patients derived from a multi-institutional study were adopted. A total of 2153 radiomic features were calculated separately from intratumoral volumes (ITVs)and peritumoral volumes (PTVs)on mpMRI scans, and then refined using LASSO and mRMR feature ranking methods. The top-ranking radiomic features were entered into the classifiers to build radiomic signatures for predicting glioma grade. The prediction performance was evaluated with five-fold cross-validation on a patient-level split. The radiomic signatures utilizing the features of ITV and PTV both show a high accuracy in predicting glioma grade, with AUCs reaching 0.968. By incorporating the features of ITV and PTV, the AUC of IPTV radiomic signature can be increased to 0.975, which outperforms the state-of-the-art methods. Additionally, our proposed method was further demonstrated to have strong generalization performance in an external validation dataset with 65 patients. The source code of our implementation is made publicly available at https://github.com/chengjianhong/glioma_grading.git.
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Zhou Q, Xue C, Ke X, Zhou J. Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI. J Magn Reson Imaging 2022; 56:325-340. [PMID: 35129845 DOI: 10.1002/jmri.28103] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/19/2022] Open
Abstract
In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
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8
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Hagiwara A, Oughourlian TC, Cho NS, Schlossman J, Wang C, Yao J, Raymond C, Everson R, Patel K, Mareninov S, Rodriguez FJ, Salamon N, Pope WB, Nghiemphu PL, Liau LM, Prins RM, Cloughesy TF, Ellingson BM. Diffusion MRI is an early biomarker of overall survival benefit in IDH wild-type recurrent glioblastoma treated with immune checkpoint inhibitors. Neuro Oncol 2021; 24:1020-1028. [PMID: 34865129 DOI: 10.1093/neuonc/noab276] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Diffusion MRI estimates of the apparent diffusion coefficient (ADC) have been shown to be useful in predicting treatment response in patients with glioblastoma (GBM), with ADC elevations indicating tumor cell death. We aimed to investigate whether the ADC values measured before and after treatment with immune checkpoint inhibitors (ICIs) and the changes in these ADC values could predict overall survival (OS) in patients with recurrent IDH wild-type GBM. METHODS Forty-four patients who met the following inclusion criteria were included in this retrospective study: (i) diagnosed with recurrent IDH wild-type GBM and treated with either pembrolizumab or nivolumab and (ii) availability of diffusion data on pre- and post-ICI MRI. Tumor volume and the median relative ADC (rADC) with respect to the normal-appearing white matter within the enhancing tumor were calculated. RESULTS Median OS among all patients was 8.1 months (range, 1.0-22.5 months). Log-rank test revealed that higher post-treatment rADC was associated with a significantly longer OS (median, 10.3 months for rADC ≧ 1.63 versus 6.1 months for rADC < 1.63; P = 0.02), whereas tumor volume, pre-treatment rADC, and changes in rADC after treatment were not significantly associated with OS. Cox regression analysis revealed that post-treatment rADC significantly influenced OS (P = 0.02, univariate analysis), even after controlling for age and sex (P =0.01, multivariate analysis), and additionally controlling for surgery after ICI treatment (P = 0.045, multivariate analysis). CONCLUSIONS Elevated post-treatment rADC may be an early imaging biomarker for OS benefits in GBM patients receiving ICI treatment.
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Affiliation(s)
- Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Talia C Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Neuroscience Interdepartmental PhD Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.,Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jacob Schlossman
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sergey Mareninov
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Fausto J Rodriguez
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.,UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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9
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Cheng J, Gao M, Liu J, Yue H, Kuang H, Liu J, Wang J. Multimodal Disentangled Variational Autoencoder with Game Theoretic Interpretability for Glioma grading. IEEE J Biomed Health Inform 2021; 26:673-684. [PMID: 34236971 DOI: 10.1109/jbhi.2021.3095476] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to boost the accuracy of glioma grading thanks to the complementary information provided by different imaging modalities. However, how to extract the common and distinctive information from MRI to achieve complementarity is still an open problem in information fusion research. In this study, we propose a deep neural network model termed as multimodal disentangled variational autoencoder (MMD-VAE) for glioma grading based on radiomics features extracted from preoperative multimodal MRI images. Specifically, the radiomics features are quantized and extracted from the region of interest for each modality. Then, the latent representations of variational autoencoder for these features are disentangled into common and distinctive representations to obtain the shared and complementary data among modalities. Afterward, cross-modality reconstruction loss and common-distinctive loss are designed to ensure the effectiveness of the disentangled representations. Finally, the disentangled common and distinctive representations are fused to predict the glioma grades, and SHapley Additive exPlanations (SHAP) is adopted to quantitatively interpret and analyze the contribution of the important features to grading. Experimental results on two benchmark datasets demonstrate that the proposed MMD-VAE model achieves encouraging predictive performance (AUC:0.9939) on a public dataset, and good generalization performance (AUC:0.9611) on a cross-institutional private dataset. These quantitative results and interpretations may help radiologists understand gliomas better and make better treatment decisions for improving clinical outcomes.
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10
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Jabehdar Maralani P, Myrehaug S, Mehrabian H, Chan AKM, Wintermark M, Heyn C, Conklin J, Ellingson BM, Rahimi S, Lau AZ, Tseng CL, Soliman H, Detsky J, Daghighi S, Keith J, Munoz DG, Das S, Atenafu EG, Lipsman N, Perry J, Stanisz G, Sahgal A. Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma. Radiother Oncol 2021; 156:258-265. [PMID: 33418005 PMCID: PMC8186561 DOI: 10.1016/j.radonc.2020.12.037] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022]
Abstract
Background: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. Methods: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. Results: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS. Conclusion: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada.
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hatef Mehrabian
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Max Wintermark
- Department of Radiology, Stanford University, United States
| | - Chris Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, United States
| | - Benjamin M Ellingson
- Department of Radiological Sciences and Psychiatry, University of California Los Angeles, United States
| | - Saba Rahimi
- Department of Biomedical Engineering, University of Toronto, Canada
| | - Angus Z Lau
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Shadi Daghighi
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Julia Keith
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - David G Munoz
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - Sunit Das
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | | | - Nir Lipsman
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | - James Perry
- Department of Medicine, Division of Neurology, University of Toronto, Canada
| | - Greg Stanisz
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
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11
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Liu X, Su Y, Jiang M, Fang S, Huang Y, Li Y, Zhong S, Wang Y, Zhang S, Wu Y, Sun J, Fan X, Zhou H. Application of Magnetic Resonance Imaging in the Evaluation of Disease Activity in Graves' Ophthalmopathy. Endocr Pract 2020; 27:198-205. [PMID: 33658136 DOI: 10.1016/j.eprac.2020.09.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 09/01/2020] [Accepted: 09/17/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To evaluate magnetic resonance imaging parameters, T2 signal intensity ratios (SIRs), and normalized apparent diffusion coefficients (n-ADC) of the extraocular muscles (EOMs) in the identification of different stages of Graves' ophthalmopathy (GO) and to find out the correlation of T2-SIRs and n-ADC values with disease changes after anti-inflammatory treatment. METHODS Altogether, 43 patients (86 orbits) were enrolled and classified into "active" or "inactive" stages by clinical activity score (CAS). Twenty-three (53.5%) patients received anti-inflammatory treatment and underwent a follow-up evaluation. Fifteen age- and gender-matched control participants (30 orbits) were included. T2-SIRs and n-ADC values of EOMs were calculated among GO and healthy controls and were correlated with CAS. Changes in these parameters were also evaluated before and after anti-inflammatory treatment. RESULTS Mean T2-SIRs and n-ADC values were both significantly higher in GO patients than in controls and higher in active GO than in inactive GO. In the inactive stage, n-ADC values of inferior rectus muscles were still higher than those in healthy controls. Both T2-SIRs and n-ADC values decreased after intravenous steroid pulse therapy. The cutoff value of pretreatment n-ADC was 1.780 to detect stages with specificity of 93.7% and sensitivity of 48.3% (P = .035). CONCLUSION T2-SIRs and n-ADC values are valuable magnetic resonance imaging indicators of the inflammatory activity in GO by detecting involvement of EOMs. They are also ideal tools to monitor the efficacy of anti-inflammatory treatment in patients with active stage GO. n-ADC values, when combined with CAS, can be promising predictive factors in the detection of stages of diseases.
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Affiliation(s)
- Xingtong Liu
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Yun Su
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Mengda Jiang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011
| | - Sijie Fang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Yazhuo Huang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Yinwei Li
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Sisi Zhong
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Yang Wang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Shuo Zhang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Yu Wu
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011
| | - Jing Sun
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011.
| | - Xianqun Fan
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011.
| | - Huifang Zhou
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 200011; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China, 200011.
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12
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Russo C, Elefante A, Cavaliere M, Di Lullo AM, Motta G, Iengo M, Brunetti A. Apparent diffusion coefficients for predicting primary cholesteatoma risk of recurrence after surgical clearance. Eur J Radiol 2020; 125:108915. [PMID: 32114332 DOI: 10.1016/j.ejrad.2020.108915] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 02/18/2020] [Accepted: 02/23/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Beside the well-known accuracy of non-EPI DWI techniques and relative ADC maps in detecting cholesteatomatous tissue, ADC can also represent a useful tool for stratifying cholesteatoma risk of recurrence. Aim of this study is to test the role of ADC in determining risk of recurrence for primary middle ear cholesteatoma, proposing stratification based on pre-operative mean (mADC) and normalized (nADC) ADC values. METHODS In this prospective study, 60 patients with primary unilateral middle ear cholesteatoma underwent a three-years-long follow-up to assess the presence of recurrent disease after macroscopically complete excisional surgery. Baseline MRI examination mADC and nADC values in the group with early evidence of recurrent cholesteatoma were compared to the group with no evidence of recurrence by using T statistics. RESULTS ADC values on pre-operative MRI examination were lower in cholesteatomas with early evidence of recurrence, and statistical significance was slightly higher for nADC compared to mADC measurements. We also determined a cut-off between the two groups, proposing stratification in high-risk of recurrence cholesteatomas (mADC≤ 1000 or nADC< 1.3) and low-risk cholesteatomas (mADC>1000 or nADC≥1.3). CONCLUSIONS ADC values resulted discriminating in identifying cholesteatomas with higher risk of early recurrence, both for mean and normalized ADC, with optimized tissue characterization and outcome prediction.
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Affiliation(s)
- Camilla Russo
- Dipartimento di Scienze Biomediche Avanzate - Università degli Studi di Napoli "Federico II", Naples, Italy.
| | - Andrea Elefante
- Dipartimento di Scienze Biomediche Avanzate - Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Michele Cavaliere
- Dipartimento di Neuroscienze, Scienze Riproduttive e Odontostomatologiche - Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Antonella M Di Lullo
- Dipartimento di Neuroscienze, Scienze Riproduttive e Odontostomatologiche - Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Gaetano Motta
- Dipartimento di Scienze Anestesiologiche, Chirurgiche e dell'Emergenza - Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Maurizio Iengo
- Dipartimento di Neuroscienze, Scienze Riproduttive e Odontostomatologiche - Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Arturo Brunetti
- Dipartimento di Scienze Biomediche Avanzate - Università degli Studi di Napoli "Federico II", Naples, Italy
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13
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Han W, Qin L, Bay C, Chen X, Yu KH, Miskin N, Li A, Xu X, Young G. Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas. AJNR Am J Neuroradiol 2020; 41:40-48. [PMID: 31857325 PMCID: PMC6975328 DOI: 10.3174/ajnr.a6365] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 10/25/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas. MATERIALS AND METHODS Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors. RESULTS In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value < .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035. CONCLUSIONS A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.
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Affiliation(s)
- W Han
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - L Qin
- Department of Imaging (L.Q., G.Y.), Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - C Bay
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - X Chen
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Department of Radiology (X.C.), Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - K-H Yu
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - N Miskin
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - A Li
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
| | - X Xu
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
| | - G Young
- From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women's Hospital, Boston, Massachusetts
- Department of Imaging (L.Q., G.Y.), Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts
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14
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Park CJ, Choi YS, Park YW, Ahn SS, Kang SG, Chang JH, Kim SH, Lee SK. Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status. Neuroradiology 2019; 62:319-326. [PMID: 31820065 DOI: 10.1007/s00234-019-02312-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE To evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters. METHODS A total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated. RESULTS Adding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040). CONCLUSION DTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.
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Affiliation(s)
- Chae Jung Park
- Department of Radiology and Research Institute of Radiological Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Yoon Seong Choi
- Department of Radiology and Research Institute of Radiological Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Yae Won Park
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, College of Medicine, Yonsei University, Seoul, Korea
| | - Jong-Hee Chang
- Department of Neurosurgery, College of Medicine, Yonsei University, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, College of Medicine, Yonsei University, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science, College of Medicine, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
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