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Doniselli FM, Pascuzzo R, Mazzi F, Padelli F, Moscatelli M, Akinci D'Antonoli T, Cuocolo R, Aquino D, Cuccarini V, Sconfienza LM. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. Eur Radiol 2024; 34:5802-5815. [PMID: 38308012 PMCID: PMC11364578 DOI: 10.1007/s00330-024-10594-x] [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: 09/23/2023] [Revised: 12/04/2023] [Accepted: 12/31/2023] [Indexed: 02/04/2024]
Abstract
OBJECTIVES To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. METHODS PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. RESULTS We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01). CONCLUSIONS Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. CLINICAL RELEVANCE STATEMENT MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. KEY POINTS • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.
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Affiliation(s)
- Fabio M Doniselli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy
| | - Riccardo Pascuzzo
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy.
| | - Federica Mazzi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Francesco Padelli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Marco Moscatelli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Rheinstrasse 26, 4410, Liestal, Switzerland
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende 43, Baronissi, 84081, Salerno, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Valeria Cuccarini
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy
- IRCCS Ospedale Galeazzi-Sant'Ambrogio, Via Cristina Belgioioso 173, 20157, Milan, Italy
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Jiang Y, Peng Y, Wu Y, Sun Q, Hua T. Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis. Cancer Manag Res 2024; 16:811-823. [PMID: 39044747 PMCID: PMC11264379 DOI: 10.2147/cmar.s467400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024] Open
Abstract
Objective To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis. Methods The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models. Results Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model. Conclusion The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.
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Affiliation(s)
- Yan Jiang
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yuanyuan Peng
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yingyi Wu
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Qing Sun
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Tebo Hua
- Department of Thyroid Breast Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
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Zheng F, Zhang L, Chen H, Zang Y, Chen X, Li Y. Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. JOURNAL OF RADIATION RESEARCH 2024; 65:350-359. [PMID: 38650477 PMCID: PMC11115443 DOI: 10.1093/jrr/rrae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/12/2023] [Indexed: 04/25/2024]
Abstract
Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.
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Affiliation(s)
- Fei Zheng
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
- Department of Radiology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China
| | - Lingling Zhang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Hongyan Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yuying Zang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Xuzhu Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yiming Li
- Department of Neurosurgery, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
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Demircioğlu A. The effect of data resampling methods in radiomics. Sci Rep 2024; 14:2858. [PMID: 38310165 PMCID: PMC10838284 DOI: 10.1038/s41598-024-53491-5] [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: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/05/2024] Open
Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
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Sanada T, Kinoshita M, Sasaki T, Yamamoto S, Fujikawa S, Fukuyama S, Hayashi N, Fukai J, Okita Y, Nonaka M, Uda T, Arita H, Mori K, Ishibashi K, Takano K, Nishida N, Shofuda T, Yoshioka E, Kanematsu D, Tanino M, Kodama Y, Mano M, Kanemura Y. Prediction of MGMT promotor methylation status in glioblastoma by contrast-enhanced T1-weighted intensity image. Neurooncol Adv 2024; 6:vdae016. [PMID: 38410136 PMCID: PMC10896622 DOI: 10.1093/noajnl/vdae016] [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] [Indexed: 02/28/2024] Open
Abstract
Background The study aims to explore MRI phenotypes that predict glioblastoma's (GBM) methylation status of the promoter region of MGMT gene (pMGMT) by qualitatively assessing contrast-enhanced T1-weighted intensity images. Methods A total of 193 histologically and molecularly confirmed GBMs at the Kansai Network for Molecular Diagnosis of Central Nervous Tumors (KANSAI) were used as an exploratory cohort. From the Cancer Imaging Archive/Cancer Genome Atlas (TCGA) 93 patients were used as validation cohorts. "Thickened structure" was defined as the solid tumor component presenting circumferential extension or occupying >50% of the tumor volume. "Methylated contrast phenotype" was defined as indistinct enhancing circumferential border, heterogenous enhancement, or nodular enhancement. Inter-rater agreement was assessed, followed by an investigation of the relationship between radiological findings and pMGMT methylation status. Results Fleiss's Kappa coefficient for "Thickened structure" was 0.68 for the exploratory and 0.55 for the validation cohort, and for "Methylated contrast phenotype," 0.30 and 0.39, respectively. The imaging feature, the presence of "Thickened structure" and absence of "Methylated contrast phenotype," was significantly predictive of pMGMT unmethylation both for the exploratory (p = .015, odds ratio = 2.44) and for the validation cohort (p = .006, odds ratio = 7.83). The sensitivities and specificities of the imaging feature, the presence of "Thickened structure," and the absence of "Methylated contrast phenotype" for predicting pMGMT unmethylation were 0.29 and 0.86 for the exploratory and 0.25 and 0.96 for the validation cohort. Conclusions The present study showed that qualitative assessment of contrast-enhanced T1-weighted intensity images helps predict GBM's pMGMT methylation status.
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Affiliation(s)
- Takahiro Sanada
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
| | - Takahiro Sasaki
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Shota Yamamoto
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Osaka General Medical Center, Osaka, Japan
| | - Seiya Fujikawa
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Neurosurgery, Japanese Red Cross Kitami Hospital, Kitami, Japan
| | - Shusei Fukuyama
- Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
| | - Nobuhide Hayashi
- Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan
| | - Junya Fukai
- Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
| | - Masahiro Nonaka
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Neurosurgery, Kansai Medical University, Hirakata, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kanji Mori
- Department of Neurosurgery, Yao Municipal Hospital, Yao, Japan
| | - Kenichi Ishibashi
- Department of Neurosurgery, Osaka City General Hospital, Osaka, Japan
| | - Koji Takano
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
- Department of Neurosurgery, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Namiko Nishida
- Department of Neurosurgery, Tazuke Kofukai Foundation, Medical Research Institute, Kitano Hospital, Osaka, Japan
| | - Tomoko Shofuda
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Ema Yoshioka
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Daisuke Kanematsu
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
| | - Mishie Tanino
- Department of Diagnostic Pathology, Asahikawa Medical University Hospital, Asahikawa, Japan
| | - Yoshinori Kodama
- Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
- Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayuki Mano
- Department of Central Laboratory and Surgical Pathology, NHO Osaka National Hospital, Osaka, Japan
| | - Yonehiro Kanemura
- Department of Diagnostic Pathology and Cytology, Osaka International Cancer Institute, Osaka, Japan
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Pan I, Huang RY. Artificial intelligence in neuroimaging of brain tumors: reality or still promise? Curr Opin Neurol 2023; 36:549-556. [PMID: 37973024 DOI: 10.1097/wco.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
PURPOSE OF REVIEW To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption. RECENT FINDINGS A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation. SUMMARY While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.
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Affiliation(s)
- Ian Pan
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School
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7
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Choi Y, Jang J, Kim BS, Ahn KJ. Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints. Eur J Radiol 2023; 168:111130. [PMID: 37827087 DOI: 10.1016/j.ejrad.2023.111130] [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: 06/27/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma. METHODS PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test. RESULTS Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall. CONCLUSION Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.
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Affiliation(s)
- Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea
| | - Bum-Soo Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea
| | - Kook-Jin Ahn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea.
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Li L, Wang M, Jiang X, Lin Y. Universal multi-factor feature selection method for radiomics-based brain tumor classification. Comput Biol Med 2023; 164:107122. [PMID: 37517322 DOI: 10.1016/j.compbiomed.2023.107122] [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: 02/08/2023] [Revised: 05/19/2023] [Accepted: 05/30/2023] [Indexed: 08/01/2023]
Abstract
Brain tumor mortality is high, and accurate classification before treatment can improve patient prognosis. Radiomics, which extracts numerous features from medical images, has been widely applied in brain tumor classification studies. Feature selection (FS) is a critical step in radiomics because it reduces redundant information and enhances classification performance. However, the lack of universal FS methods limits the development of radiomics-based brain tumor classification studies. To address this issue, we summarize the characteristics of the FS methods used in related studies and propose a universal method based on three selection factors called triple-factor cascaded selection (TFCS). Particularly, these factors correspond to the correlation between features and task labels, interdependence among features, and role of features in the model. The TFCS method divides FS into two steps. First, it utilizes mutual information to select features that are strongly correlated with the task and contain less redundant information. Recursive feature elimination is then employed to obtain the subset with the best classification performance. To validate the universality of the TFCS, we conducted experiments on seven datasets containing 13 brain tumor classification tasks and evaluated the overall performance using five types of indicators. Results: TFCS exhibited excellent overall performance for all tasks. Compared to the 13 related methods, it takes less time, has moderate parsimony, the best classification performance, adaptability, and stability, and shows better universality. Our study demonstrates that the reasonable utilization of multiple factors can enhance FS performance and provide new insights for future method design.
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Affiliation(s)
- Longfei Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China; Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China
| | - Meiyun Wang
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China; Department of Radiology, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoming Jiang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Post and Telecommunications, Chongqing, China.
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China; School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China; Hanwei IoT Institute, Zhengzhou University, Zhengzhou, China.
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9
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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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Lopez-Rueda A, Puig J, Thió-Henestrosa S, Moreno-Negrete JL, Zwanzger C, Pujol T, Aldecoa I, Pineda E, Valduvieco I, González JJ, Oleaga L. Texture Analysis of the Apparent Diffusion Coefficient Focused on Contrast-Enhancing Lesions in Predicting Survival for Bevacizumab-Treated Patients with Recurrent Glioblastoma. Cancers (Basel) 2023; 15:cancers15113026. [PMID: 37296988 DOI: 10.3390/cancers15113026] [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: 01/23/2023] [Revised: 04/19/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Glioblastoma often recurs after treatment. Bevacizumab increases progression-free survival in some patients with recurrent glioblastoma. Identifying pretreatment predictors of survival can help clinical decision making. Magnetic resonance texture analysis (MRTA) quantifies macroscopic tissue heterogeneity indirectly linked to microscopic tissue properties. We investigated the usefulness of MRTA in predicting survival in patients with recurrent glioblastoma treated with bevacizumab. METHODS We evaluated retrospective longitudinal data from 33 patients (20 men; mean age 56 ± 13 years) who received bevacizumab on the first recurrence of glioblastoma. Volumes of contrast-enhancing lesions segmented on postcontrast T1-weighted sequences were co-registered on apparent diffusion coefficient maps to extract 107 radiomic features. To assess the performance of textural parameters in predicting progression-free survival and overall survival, we used receiver operating characteristic curves, univariate and multivariate regression analysis, and Kaplan-Meier plots. RESULTS Longer progression-free survival (>6 months) and overall survival (>1 year) were associated with lower values of major axis length (MAL), a lower maximum 2D diameter row (m2Ddr), and higher skewness values. Longer progression-free survival was also associated with higher kurtosis, and longer overall survival with higher elongation values. The model combining MAL, m2Ddr, and skewness best predicted progression-free survival at 6 months (AUC 0.886, 100% sensitivity, 77.8% specificity, 50% PPV, 100% NPV), and the model combining m2Ddr, elongation, and skewness best predicted overall survival (AUC 0.895, 83.3% sensitivity, 85.2% specificity, 55.6% PPV, 95.8% NPV). CONCLUSIONS Our preliminary analyses suggest that in patients with recurrent glioblastoma pretreatment, MRTA helps to predict survival after bevacizumab treatment.
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Affiliation(s)
- Antonio Lopez-Rueda
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Josep Puig
- Department of Radiology (IDI) and IDIBGI Hospital Universitari de Girona Doctor Josep Trueta, 17190 Girona, Spain
| | - Santiago Thió-Henestrosa
- Department of Computer Science Applied Mathmatics and Statistics, University of Girona, 17003 Girona, Spain
| | | | | | - Teresa Pujol
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Iban Aldecoa
- Department of Anatomical Pathology, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Estela Pineda
- Translational Genomics and Targeted Therapeutics in Solid Tumors Group, Medical Oncology Department, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Izaskun Valduvieco
- Radiotherapy Oncology Service, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - José Juan González
- Department of Neurosurgery, Laboratory of Experimental Oncological Neurosurgery, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Laura Oleaga
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
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11
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Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11:35. [PMID: 36991494 DOI: 10.1186/s40364-023-00476-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.
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Affiliation(s)
- Fang-Ying Chiu
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Center for Brain and Neurobiology Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Teaching and Research Headquarters for Sustainable Development Goals, Tzu Chi University, Hualien City, 970374, Taiwan.
| | - Yun Yen
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Ph.D. Program for Cancer Biology and Drug Discovery, Taipei Medical University, Taipei City, 110301, Taiwan.
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110301, Taiwan.
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei City, 110301, Taiwan.
- Cancer Center, Taipei Municipal WanFang Hospital, Taipei City, 116081, Taiwan.
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12
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Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08405-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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13
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Hosseini SA, Hosseini E, Hajianfar G, Shiri I, Servaes S, Rosa-Neto P, Godoy L, Nasrallah MP, O’Rourke DM, Mohan S, Chawla S. MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas. Cancers (Basel) 2023; 15:cancers15030951. [PMID: 36765908 PMCID: PMC9913426 DOI: 10.3390/cancers15030951] [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: 01/01/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.
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Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
| | - Elahe Hosseini
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran 15719-14911, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran 19956-14331, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Laiz Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - MacLean P. Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Donald M. O’Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
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14
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Hooper GW, Ginat DT. MRI radiomics and potential applications to glioblastoma. Front Oncol 2023; 13:1134109. [PMID: 36874083 PMCID: PMC9982088 DOI: 10.3389/fonc.2023.1134109] [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: 12/29/2022] [Accepted: 02/07/2023] [Indexed: 02/19/2023] Open
Abstract
MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.
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Affiliation(s)
- Grayson W Hooper
- Landstuhl Regional Medical Center, Department of Radiology, Landstuhl, Germany
| | - Daniel T Ginat
- University of Chicago, Department of Radiology, Chicago, IL, United States
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15
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García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
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Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
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16
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Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
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Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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17
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Correlation of T1- to T2-weighted signal intensity ratio with T1- and T2-relaxation time and IDH mutation status in glioma. Sci Rep 2022; 12:18801. [PMID: 36335158 PMCID: PMC9637175 DOI: 10.1038/s41598-022-23527-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/01/2022] [Indexed: 11/07/2022] Open
Abstract
The current study aimed to test whether the ratio of T1-weighted to T2-weighted signal intensity (T1W/T2W ratio: rT1/T2) derived from conventional MRI could act as a surrogate relaxation time predictive of IDH mutation status in histologically lower-grade gliomas. Strong exponential correlations were found between rT1/T2 and each of T1- and T2-relaxation times in eight subjects (rT1/T2 = 1.63exp-0.0005T1-relax + 0.30 and rT1/T2 = 1.27exp-0.0081T2-relax + 0.48; R2 = 0.64 and 0.59, respectively). In a test cohort of 25 patients, mean rT1/T2 (mrT1/T2) was significantly higher in IDHwt tumors than in IDHmt tumors (p < 0.05) and the optimal cut-off of mrT1/T2 for discriminating IDHmt was 0.666-0.677, (AUC = 0.75, p < 0.05), which was validated in an external domestic cohort of 29 patients (AUC = 0.75, p = 0.02). However, this result was not validated in an external international cohort derived from TCIA/TCGA (AUC = 0.63, p = 0.08). The t-Distributed Stochastic Neighbor Embedding analysis revealed a greater diversity in image characteristics within the TCIA/TCGA cohort than in the two domestic cohorts. The failure of external validation in the TCIA/TCGA cohort could be attributed to its wider variety of original imaging characteristics.
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Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge. Cancers (Basel) 2022; 14:cancers14194827. [PMID: 36230750 PMCID: PMC9562637 DOI: 10.3390/cancers14194827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/19/2022] [Accepted: 09/30/2022] [Indexed: 12/05/2022] Open
Abstract
Simple Summary O6-methylguanine-DNA methyl transferase (MGMT) methylation in glioblastoma is an important prognostic and predictive factor that requires an invasive surgical procedure for identification. In several recent studies, MGMT methylation prediction models were developed using MR images, and good diagnostic performance was achieved, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MRI. With unexpected negative results, approximately 80% of the developed models showed no significant difference with the chance level of 50% in terms of external validation accuracy. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MRI, even using deep learning. Abstract O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Sansone G, Vivori N, Vivori C, Di Stefano AL, Picca A. Basic premises: searching for new targets and strategies in diffuse gliomas. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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Visualization of Resected Area in Endonasal Endoscopic Approach versus Transcranial Approach for Skull Base Meningiomas by Voxel-Based-Lesion Mapping. Brain Sci 2022; 12:brainsci12070875. [PMID: 35884682 PMCID: PMC9313245 DOI: 10.3390/brainsci12070875] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background: We aimed to evaluate the resected area of endonasal endoscopic approach (EEA) and transcranial approach (TCA) for skull base meningiomas (SBMs) using voxel-based-lesion mapping and visualized the appropriate tumor location in each approach. Methods: We retrospectively examined 182 patients with SBMs who underwent tumor resection in our hospital between 2014 and 2019. Pre- and post-operative SBMs were manually delineated on MRI to create the voxels-of-interest (VOIpre and VOIpost) and were registered onto the normalized brain (normalized VOIpre and normalized VOIpost). The resected map was created by subtracting normalized VOIpost from the normalized VOIpre divided by the number of cases. The resected maps of TCA and EEA were compared by subtracting them. Results: Twenty patients underwent EEA and 135 patients underwent TCA. The tumor resected map demonstrated that the resected area of EEA frequently accumulated on the central skull base, while that of TCA accumulated near the central skull base. The border of both approaches matched the circle that connects neural foramens at the skull base. Conclusions: The resected area of SBMs by EEA and TCA was well visualized by voxel-based-lesion mapping. The circle connecting the neural foramens was the border of EEA and TCA.
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Chen S, Xu Y, Ye M, Li Y, Sun Y, Liang J, Lu J, Wang Z, Zhu Z, Zhang X, Zhang B. Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics. J Clin Med 2022; 11:jcm11123445. [PMID: 35743511 PMCID: PMC9224690 DOI: 10.3390/jcm11123445] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/08/2022] [Accepted: 06/13/2022] [Indexed: 02/01/2023] Open
Abstract
This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas.
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Affiliation(s)
- Sixuan Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Yue Xu
- National Institute of Healthcare Data Science, Nanjing University, Nanjing 210023, China;
| | - Meiping Ye
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Yang Li
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Yu Sun
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China; (Y.S.); (J.L.)
| | - Jiawei Liang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China; (Y.S.); (J.L.)
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Zhengge Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Zhengyang Zhu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
- Correspondence:
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; (S.C.); (M.Y.); (Y.L.); (J.L.); (Z.W.); (Z.Z.); (B.Z.)
- Institute of Brain Science, Nanjing University, Nanjing 210023, China
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Mirón Mombiela R, Arildskov AR, Bruun FJ, Hasselbalch LH, Holst KB, Rasmussen SH, Borrás C. What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:6504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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Affiliation(s)
- Rebeca Mirón Mombiela
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Anne Rix Arildskov
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Frederik Jager Bruun
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Lotte Harries Hasselbalch
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Kristine Bærentz Holst
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Sine Hvid Rasmussen
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Consuelo Borrás
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBERFES, Institute of Health Research-INCLIVA, 46010 Valencia, Spain
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Wu W, Wang Y, Xiang J, Li X, Wahafu A, Yu X, Bai X, Yan G, Wang C, Wang N, Du C, Xie W, Wang M, Wang J. A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients. Front Oncol 2022; 12:729002. [PMID: 35646656 PMCID: PMC9133344 DOI: 10.3389/fonc.2022.729002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 03/24/2022] [Indexed: 01/13/2023] Open
Abstract
Background Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches. Methods According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated. Results In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas. Conclusions Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.
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Affiliation(s)
- Wei Wu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yichang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianyang Xiang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaodong Li
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Alafate Wahafu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiao Yu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaobin Bai
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ge Yan
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Chunbao Wang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ning Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Changwang Du
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Wanfu Xie
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Maode Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Deng DB, Liao YT, Zhou JF, Cheng LN, He P, Wu SN, Wang WS, Zhou Q. Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features. Front Neurol 2022; 13:866274. [PMID: 35585843 PMCID: PMC9108285 DOI: 10.3389/fneur.2022.866274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 11/22/2022] Open
Abstract
Objectives To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features. Methods Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models. Results A total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits. Conclusion The combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.
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Affiliation(s)
- Da-Biao Deng
- Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong), Guangzhou, China
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | | | - Jiang-Fen Zhou
- Department of Neuro-Oncology of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Li-Na Cheng
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Peng He
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Sheng-Nan Wu
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
| | - Wen-Sheng Wang
- Imaging Department of Guangdong 999 Brain Hospital, Guangzhou, China
- *Correspondence: Wen-Sheng Wang
| | - Quan Zhou
- Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong), Guangzhou, China
- Quan Zhou
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A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients. J Comput Assist Tomogr 2022; 46:470-479. [PMID: 35405713 DOI: 10.1097/rct.0000000000001300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. MATERIALS AND METHODS Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. RESULTS Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. CONCLUSIONS The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.
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Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. RESULTS Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35-0.84). The features selected by different models varied largely (0.19; range 0.02-0.42), although the selected features themselves were highly correlated (0.71; range 0.4-0.92). CONCLUSIONS Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
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Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
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Chaddad A, Daniel P, Zhang M, Rathore S, Sargos P, Desrosiers C, Niazi T. Deep radiomic signature with immune cell markers predicts the survival of glioma patients. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2020.10.117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Radiomic Features Associated with Extent of Resection in Glioma Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:341-347. [PMID: 34862558 DOI: 10.1007/978-3-030-85292-4_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.
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Pasquini L, Napolitano A, Lucignani M, Tagliente E, Dellepiane F, Rossi-Espagnet MC, Ritrovato M, Vidiri A, Villani V, Ranazzi G, Stoppacciaro A, Romano A, Di Napoli A, Bozzao A. AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Front Oncol 2021; 11:601425. [PMID: 34888226 PMCID: PMC8649764 DOI: 10.3389/fonc.2021.601425] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/02/2021] [Indexed: 12/30/2022] Open
Abstract
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Maria Camilla Rossi-Espagnet
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Matteo Ritrovato
- Unit of Health Technology Assessment (HTA), Biomedical Technology Risk Manager, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Giulio Ranazzi
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonella Stoppacciaro
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
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Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
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Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
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Sohn B, An C, Kim D, Ahn SS, Han K, Kim SH, Kang SG, Chang JH, Lee SK. Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant. J Neurooncol 2021; 155:267-276. [PMID: 34648115 PMCID: PMC8651601 DOI: 10.1007/s11060-021-03870-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/06/2021] [Indexed: 11/30/2022]
Abstract
Purpose In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant. Methods From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features. Results The micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains. Conclusion We built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers’ prediction probability. Supplementary Information The online version contains supplementary material available at 10.1007/s11060-021-03870-z.
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Affiliation(s)
- Beomseok Sohn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Chansik An
- Department of Radiology and Research Institute, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Dain Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Ahn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea.
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea
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Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas. Neuroradiology 2021; 64:323-331. [PMID: 34368897 DOI: 10.1007/s00234-021-02780-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors. METHODS A total of 182 patients with diffuse gliomas were included. All subjects underwent pre-operative MRI and post-operative pathological diagnosis. EphA2 expression of tumors was scored on pathological sections with immunohistochemical staining using monoclonal EphA2 antibody. MRI radiomics features were extracted from three-dimensional contrast-enhanced T1-weighted imaging and diffusion kurtosis imaging. Predictive models were constructed using machine learning-based radiomics features selection and three classifiers for predicting EphA2 expression and tumor grade. Features of best EphA2 expression model were subsequently used to construct another model of tumor grading. For each model, 146 cases (80%) were randomly picked as training and the rest 36 (20%) were testing cohorts. EphA2 expression was further correlated to the radiomics features in both grade models using Spearman's correlation. RESULTS Logistic regression model presented highest performance for predicting EphA2 expression (AUC: 0.836/0.724 in training/validation set). Tumor gradings model guided by features from EphA2 expression model demonstrated comparable performance (AUC: 0.930/0.983) to that constructed directly using imaging radiomics features (AUC: 0.960/0.977). Two radiomics features which included in both LR-grade models showed strong correlation (P < 0.05) with EphA2 expression. CONCLUSION The expression of EphA2 in gliomas could be predicted by radiomics features extracted from diffusion kurtosis MRI, which could also be used to assist tumor grading.
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Kinoshita M. [1. Current and Future Prospective of Radiomics in Glioma Imaging]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:761-764. [PMID: 34305066 DOI: 10.6009/jjrt.2021_jsrt_77.7.761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Liu D, Chen J, Hu X, Yang K, Liu Y, Hu G, Ge H, Zhang W, Liu H. Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. Front Oncol 2021; 11:699265. [PMID: 34295824 PMCID: PMC8290166 DOI: 10.3389/fonc.2021.699265] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/23/2021] [Indexed: 12/12/2022] Open
Abstract
Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.
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Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenbin Zhang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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Yogananda CGB, Shah BR, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, Fei B, Madhuranthakam AJ, Maldjian JA. MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status. AJNR Am J Neuroradiol 2021; 42:845-852. [PMID: 33664111 PMCID: PMC8115363 DOI: 10.3174/ajnr.a7029] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/21/2020] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND PURPOSE O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only. MATERIALS AND METHODS Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Three-fold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy. RESULTS The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008]. CONCLUSIONS We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.
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Affiliation(s)
- C G B Yogananda
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B R Shah
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - S S Nalawade
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - G K Murugesan
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - F F Yu
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - M C Pinho
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B C Wagner
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Mickey
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - T R Patel
- Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - B Fei
- Department of Bioengineering (B.F.), University of Texas at Dallas, Richardson, Texas
| | - A J Madhuranthakam
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - J A Maldjian
- From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
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Vils A, Bogowicz M, Tanadini-Lang S, Vuong D, Saltybaeva N, Kraft J, Wirsching HG, Gramatzki D, Wick W, Rushing E, Reifenberger G, Guckenberger M, Weller M, Andratschke N. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial. Front Oncol 2021; 11:636672. [PMID: 33937035 PMCID: PMC8079773 DOI: 10.3389/fonc.2021.636672] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/17/2021] [Indexed: 12/21/2022] Open
Abstract
Background Based on promising results from radiomic approaches to predict O6-methylguanine DNA methyltransferase promoter methylation status (MGMT status) and clinical outcome in patients with newly diagnosed glioblastoma, the current study aimed to evaluate radiomics in recurrent glioblastoma patients. Methods Pre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed via principal component analysis, and multivariable models were trained to predict MGMT status, progression-free survival from first salvage therapy, referred to herein as PFS2, and overall survival (OS). The prognostic power of models was quantified with concordance index (CI) for survival data and area under receiver operating characteristic curve (AUC) for the MGMT status. Results We established and validated a radiomic model to predict MGMT status using linear intensity interpolation and considering features extracted from gadolinium-enhanced T1-weighted MRI (training AUC = 0.670, validation AUC = 0.673). Additionally, models predicting PFS2 and OS were found for the training cohort but were not confirmed in our validation cohort. Conclusions A radiomic model for prediction of MGMT promoter methylation status from tumor texture features in patients with recurrent glioblastoma was successfully established, providing a non-invasive approach to anticipate patient's response to chemotherapy if biopsy cannot be performed. The radiomic approach to predict PFS2 and OS failed.
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Affiliation(s)
- Alex Vils
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Natalia Saltybaeva
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Johannes Kraft
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
| | | | - Dorothee Gramatzki
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Wolfgang Wick
- Neurology Clinic, University Heidelberg Medical School, Heidelberg, Germany
| | - Elisabeth Rushing
- Department of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Guido Reifenberger
- Department of Neuropathology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Michael Weller
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
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40
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Le NQK, Hung TNK, Do DT, Lam LHT, Dang LH, Huynh TT. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med 2021; 132:104320. [PMID: 33735760 DOI: 10.1016/j.compbiomed.2021.104320] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients. METHODS This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant. RESULTS After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset. CONCLUSION The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
| | - Truong Nguyen Khanh Hung
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; Orthopedic and Trauma Department, Cho Ray Hospital, Ho Chi Minh City, 70000, Viet Nam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, 106, Taiwan
| | - Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; Children's Hospital 2, Ho Chi Minh City, 70000, Viet Nam
| | - Luong Huu Dang
- Department of Otolaryngology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 70000, Viet Nam
| | - Tuan-Tu Huynh
- Department of Electrical Engineering, Yuan Ze University, No. 135, Yuandong Road, Zhongli, 320, Taoyuan, Taiwan; Department of Electrical Electronic and Mechanical Engineering, Lac Hong University, No. 10, Huynh Van Nghe Road, Bien Hoa, Dong Nai, 76120, Viet Nam
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Overcast WB, Davis KM, Ho CY, Hutchins GD, Green MA, Graner BD, Veronesi MC. Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors. Curr Oncol Rep 2021; 23:34. [PMID: 33599882 PMCID: PMC7892735 DOI: 10.1007/s11912-021-01020-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET). RECENT FINDINGS Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.
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Affiliation(s)
- Wynton B. Overcast
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd. Room 0663, Indianapolis, IN 46202 USA
| | - Korbin M. Davis
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd. Room 0663, Indianapolis, IN 46202 USA
| | - Chang Y. Ho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Goodman Hall, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Gary D. Hutchins
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E124, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
| | - Mark A. Green
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E124, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
| | - Brian D. Graner
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Goodman Hall, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Michael C. Veronesi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E174, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
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Kinoshita M, Arita H, Takahashi M, Uda T, Fukai J, Ishibashi K, Kijima N, Hirayama R, Sakai M, Arisawa A, Takahashi H, Nakanishi K, Kagawa N, Ichimura K, Kanemura Y, Narita Y, Kishima H. Impact of Inversion Time for FLAIR Acquisition on the T2-FLAIR Mismatch Detectability for IDH-Mutant, Non-CODEL Astrocytomas. Front Oncol 2021; 10:596448. [PMID: 33520709 PMCID: PMC7841010 DOI: 10.3389/fonc.2020.596448] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/30/2020] [Indexed: 11/29/2022] Open
Abstract
The current research tested the hypothesis that inversion time (TI) shorter than 2,400 ms under 3T for FLAIR can improve the diagnostic accuracy of the T2-FLAIR mismatch sign for identifying IDHmt, non-CODEL astrocytomas. We prepared three different cohorts; 94 MRI from 76 IDHmt, non-CODEL Lower-grade gliomas (LrGGs), 33 MRI from 31 LrGG under the restriction of FLAIR being acquired with TI < 2,400 ms for 3T or 2,016 ms for 1.5T, and 112 MRI from 112 patients from the TCIA/TCGA dataset for LrGG. The presence or absence of the “T2-FLAIR mismatch sign” was evaluated, and we compared diagnostic accuracies according to TI used for FLAIR acquisition. The T2-FLAIR mismatch sign was more frequently positive when TI was shorter than 2,400 ms under 3T for FLAIR acquisition (p = 0.0009, Fisher’s exact test). The T2-FLAIR mismatch sign was positive only for IDHmt, non-CODEL astrocytomas even if we confined the cohort with FLAIR acquired with shorter TI (p = 0.0001, Fisher’s exact test). TCIA/TCGA dataset validated that the sensitivity, specificity, PPV, and NPV of the T2-FLAIR mismatch sign to identify IDHmt, non-CODEL astrocytomas improved from 31, 90, 79, and 51% to 67, 94, 92, and 74%, respectively and the area under the curve of ROC improved from 0.63 to 0.87 when FLAIR was acquired with shorter TI. We revealed that TI for FLAIR impacts the T2-FLAIR mismatch sign’s diagnostic accuracy and that FLAIR scanned with TI < 2,400 ms in 3T is necessary for LrGG imaging.
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Affiliation(s)
- Manabu Kinoshita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Neurosurgery, Takatsuki General Hospital, Takatsuki, Japan
| | - Masamichi Takahashi
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Takehiro Uda
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Junya Fukai
- Department of Neurological Surgery, Wakayama Medical University, Wakayama, Japan
| | - Kenichi Ishibashi
- Department of Neurosurgery, Osaka City General Hospital, Osaka, Japan
| | - Noriyuki Kijima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryuichi Hirayama
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Mio Sakai
- Department of Diagnostic Radiology, Osaka International Cancer Institute, Osaka, Japan
| | - Atsuko Arisawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hiroto Takahashi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Katsuyuki Nakanishi
- Department of Diagnostic Radiology, Osaka International Cancer Institute, Osaka, Japan
| | - Naoki Kagawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kouichi Ichimura
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, Japan
| | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
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Shi R, Chen W, Yang B, Qu J, Cheng Y, Zhu Z, Gao Y, Wang Q, Liu Y, Li Z, Qu X. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features. Am J Cancer Res 2020; 10:4513-4526. [PMID: 33415015 PMCID: PMC7783758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/17/2020] [Indexed: 06/12/2023] Open
Abstract
There is a critical need for development of improved methods capable of accurately predicting the RAS (KRAS and NRAS) and BRAF gene mutation status in patients with advanced colorectal cancer (CRC). The purpose of this study was to investigate whether radiomics and/or semantic features could improve the detection accuracy of RAS/BRAF gene mutation status in patients with colorectal liver metastasis (CRLM). In this retrospective study, 159 patients who had been diagnosed with CRLM in two hospitals were enrolled. All patients received lung and abdominal contrast-enhanced CT (CECT) scans prior to radiation therapy and chemotherapy. Semantic features were independently assessed by two radiologists. Radiomics features were extracted from the portal venous phase (PVP) of the CT scan for each patient. Seven machine learning algorithms were used to establish three scores based on the semantic, radiomics and the combination of both features. Two semantic and 851 radiomics features were used to predict the mutation status of RAS and BRAF using an artificial neural network method (ANN). This approach performed best out of the seven tested algorithms. We constructed three scores which were based on radiomics, semantic features and the combined scores. The combined score could distinguish between wild-type and mutant patients with an AUC of 0.95 in the primary cohort and 0.79 in the validation cohort. This study proved that the application of radiomics together with semantic features can improve non-invasive assessment of the gene mutation status of RAS (KRAS and NRAS) and BRAF in CRLM.
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Affiliation(s)
- Ruichuan Shi
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Weixing Chen
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences518005, Guangdong, China
| | - Bowen Yang
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Jinglei Qu
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Yu Cheng
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Zhitu Zhu
- Cancer Center, The First Affiliated Hospital of Jinzhou Medical University121001, Liaoning, China
| | - Yu Gao
- Cancer Center, The First Affiliated Hospital of Jinzhou Medical University121001, Liaoning, China
| | - Qian Wang
- Department of Medical Oncology, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University110042, Liaoning, China
| | - Yunpeng Liu
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Zhi Li
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
| | - Xiujuan Qu
- Department of Medical Oncology, The First Hospital of China Medical University110001, Liaoning, China
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University110001, Liaoning, China
- Liaoning Province Clinical Research Center for Cancer110001, Liaoning, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education110001, Liaoning, China
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Le NQK, Do DT, Chiu FY, Yapp EKY, Yeh HY, Chen CY. XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma. J Pers Med 2020; 10:jpm10030128. [PMID: 32942564 PMCID: PMC7563334 DOI: 10.3390/jpm10030128] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/03/2020] [Accepted: 09/09/2020] [Indexed: 02/07/2023] Open
Abstract
Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan;
- Correspondence: (N.Q.K.L.); (C.-Y.C.); Tel.: +886-266-382-736 (ext. 1992) (N.Q.K.L.); Fax: +886-2-2732-1956 (N.Q.K.L.)
| | - Duyen Thi Do
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam;
| | - Fang-Ying Chiu
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan;
| | - Edward Kien Yee Yapp
- Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, #08-04, Innovis, Singapore 138634, Singapore;
| | - Hui-Yuan Yeh
- Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore 639798, Singapore;
| | - Cheng-Yu Chen
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan;
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Correspondence: (N.Q.K.L.); (C.-Y.C.); Tel.: +886-266-382-736 (ext. 1992) (N.Q.K.L.); Fax: +886-2-2732-1956 (N.Q.K.L.)
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Balaji E V, Kumar N, Satarker S, Nampoothiri M. Zinc as a plausible epigenetic modulator of glioblastoma multiforme. Eur J Pharmacol 2020; 887:173549. [PMID: 32926916 DOI: 10.1016/j.ejphar.2020.173549] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/26/2020] [Accepted: 09/09/2020] [Indexed: 01/04/2023]
Abstract
Glioblastoma Multiforme (GBM) is an aggressive brain tumor (WHO grade 4 astrocytoma) with unknown causes and is associated with a reduced life expectancy. The available treatment options namely radiotherapy, surgery and chemotherapy have failed to improve life expectancy. Out of the various therapeutic approaches, epigenetic therapy is one of the most studied. Epigenetic therapy is involved in the effective treatment of GBM by inhibiting DNA methyltransferase, histone deacetylation and non-coding RNA. It also promotes the expression of the tumor suppressor gene and is involved in the suppression of the oncogene. Various targets are being studied to implement proper epigenetic regulation to control GBM effectively. Zinc is one of the micronutrients which is considered to maintain epigenetic regulation by promoting the proper DNA folding, protecting genetic material from the oxidative damage and controlling the enzyme activation involved in the epigenetic regulation. Here, we are discussing the importance of zinc in regulating the epigenetic modifications and assessing its role in glioblastoma research. The discussion also highlights the importance of artificial intelligence using epigenetics for envisaging the glioma progression, diagnosis and its management.
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Affiliation(s)
- Vignesh Balaji E
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nitesh Kumar
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Sairaj Satarker
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Madhavan Nampoothiri
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India.
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Qian J, Herman MG, Brinkmann DH, Laack NN, Kemp BJ, Hunt CH, Lowe V, Pafundi DH. Prediction of MGMT Status for Glioblastoma Patients Using Radiomics Feature Extraction From 18F-DOPA-PET Imaging. Int J Radiat Oncol Biol Phys 2020; 108:1339-1346. [PMID: 32634544 DOI: 10.1016/j.ijrobp.2020.06.073] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 06/15/2020] [Accepted: 06/28/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is associated with improved treatment response and survival in patients with glioblastoma (GB), but the necessary pathologic specimen can be nondiagnostic. In this study, we assessed whether radiomics features from pretreatment 18F-DOPA positron emission tomography (PET) imaging could be used to predict pathologic MGMT status. METHODS AND MATERIALS This study included 86 patients with newly diagnosed GB, split into 3 groups (training, validating, and predicting). We performed a radiomics analysis on 18F-DOPA PET images by extracting features from 2 tumor-based contours: a "Gold" contour of all abnormal uptake per expert nuclear medicine physician and a high-grade glioma (HGG) contour based on a tumor-to-normal hemispheric ratio >2.0, representing the most aggressive components. Feature selection was performed by comparing the weighted feature importance and filtering with bivariate analysis. Optimization of model parameters was explored using grid search with selected features. The stability of the model with increasing input features was also investigated for model robustness. The model predictions were then applied by comparing the overall survival probability of the patients with GB and unknown MGMT status versus those with known MGMT status. RESULTS A radiomics signature was constructed to predict MGMT methylation status. Using features extracted from HGG contour alone with a random forest model, we achieved 80% ± 10% accuracy for 95% confidence level in predicting MGMT status. The prediction accuracy was not improved with the addition of the Gold contour or with more input features. The model was applied to the patients with unknown MGMT methylation status. The prediction results are consistent with what is expected using overall survival as a surrogate. CONCLUSIONS This study suggests that 3 features from radiomics modeling of 18F-DOPA PET imaging can predict MGMT methylation status with reasonable accuracy. These results could provide valuable therapeutic guidance for patients in whom MGMT testing is inconclusive or nondiagnostic.
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Affiliation(s)
- Jing Qian
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Michael G Herman
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | | | - Nadia N Laack
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Bradley J Kemp
- Department of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Val Lowe
- Department of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota
| | - Deanna H Pafundi
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida.
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Crisi G, Filice S. Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach. J Neuroimaging 2020; 30:458-462. [PMID: 32374045 DOI: 10.1111/jon.12724] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND PURPOSE This study aims to investigate whether radiomic quantitative image features (IFs) from perfusion dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) retain sufficient strength to predict O6-methylguanine-DNA methyltransferase promoter methylation (MGMT_pm) in newly diagnosed glioblastoma (GB) patients. METHODS We retrospectively reviewed the perfusion DSC-MRI of 59 patients with GB. Patients were classified into three groups: (1) unmethylated if MGMT_pm ≤ 9% (UM); (2) intermediate-methylated if MGMT_pm ranged between 10% and 29% (IM); (3) methylated if MGMT_pm ≥ 30% (M). A total of 92 quantitative IFs were obtained from relative cerebral blood volume and relative cerebral blood flow maps. The Mann-Whitney U-test was applied to assess whether there were statistical differences in IFs between patient groups. Those IFs showing significant difference between two patient groups were termed relevant IFs (rIFs). rIFs were uploaded to a machine learning model to predict the MGMT_pm. RESULTS No rIFs were found between UM and IM groups. Fourteen rIFs were found among UM-M, IM-M, and (UM + IM)-M groups. We built a multilayer perceptron deep learning model that classified patients as belonging to UM + IM and M group. The model performed well with 75% sensitivity, 85% specificity, and an area under the receiver-operating curve of .84. CONCLUSION rIFs from perfusion DSC-MRI are potential biomarkers in GBs with a ≥30% MGMT_pm. Otherwise, unmethylated and intermediate-methylated GBs lack of rIFs. Five of 14 rIFs show sufficient strength to build an accurate prediction model of MGMT_pm.
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Affiliation(s)
- Girolamo Crisi
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
| | - Silvano Filice
- Medical Physics Unit, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
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48
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Okita Y, Shofuda T, Kanematsu D, Yoshioka E, Kodama Y, Mano M, Kinoshita M, Nonaka M, Fujinaka T, Kanemura Y. The association between 11C-methionine uptake, IDH gene mutation, and MGMT promoter methylation in patients with grade II and III gliomas. Clin Radiol 2020; 75:622-628. [PMID: 32321646 DOI: 10.1016/j.crad.2020.03.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 03/23/2020] [Indexed: 01/08/2023]
Abstract
AIM To evaluate the association between 11C-methionine positron-emission tomography (11C-methionine PET) findings, isocitrate dehydrogenase (IDH) gene mutation, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with grade II and III gliomas. MATERIALS AND METHODS Data were collected from 40 patients with grade II and III gliomas who underwent both magnetic resonance imaging (MRI) and 11C-methionine PET as part of their pre-surgical examination. IDH mutation was examined via DNA sequencing, and MGMT promoter methylation via quantitative methylation-specific polymerase chain reaction (PCR). RESULTS A threshold of MGMT promoter methylation of 1% was significantly associated with tumour/normal tissue (T/N) ratio. The T/N ratio in samples with MGMT promoter methylation ≥1% was higher than that in samples with MGMT promoter methylation <1%, and the difference was statistically significant (p=0.011). Reliable prediction of MGMT promoter methylation (<1% versus ≥1%) was possible using the T/N ratio under the receiver operator characteristic (ROC) curve with a sensitivity and specificity of 75% each (cut-off value=1.6: p=0.0226, area under the ROC curve [AUC]=0.76172). Conversely, the T/N ratio had no association with IDH mutation (p=0.6). The ROC curve revealed no reliable prediction of IDH mutation using the T/N ratio (p=0.606, AUC=0.60577). CONCLUSION 11C-methionine PET parameters can predict MGMT promoter methylation but not IDH mutation status. 11C-methionine uptake may have limited potential to reflect DNA methylation processes in grade II and III gliomas.
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Affiliation(s)
- Y Okita
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan; Department of Neurosurgery, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan.
| | - T Shofuda
- Division of Stem Cell Research, Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - D Kanematsu
- Division of Regenerative Medicine, Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - E Yoshioka
- Division of Stem Cell Research, Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - Y Kodama
- Division of Pathology Network, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe City, 650-0017, Japan; Department of Central Laboratory and Surgical Pathology, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - M Mano
- Department of Central Laboratory and Surgical Pathology, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - M Kinoshita
- Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan; Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - M Nonaka
- Department of Neurosurgery, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan
| | - T Fujinaka
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
| | - Y Kanemura
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan; Division of Regenerative Medicine, Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka City, 540-0006, Japan
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