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Li L, Xiao F, Wang S, Kuang S, Li Z, Zhong Y, Xu D, Cai Y, Li S, Chen J, Liu Y, Li J, Li H, Xu H. Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis. Sci Rep 2024; 14:16031. [PMID: 38992201 PMCID: PMC11239670 DOI: 10.1038/s41598-024-66653-2] [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: 12/31/2023] [Accepted: 07/03/2024] [Indexed: 07/13/2024] Open
Abstract
O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, "non-enhanced, and edematous" areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709-0.963) and 0.739 (0.581-0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.
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Affiliation(s)
- Lanqing Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shouchao Wang
- Department of Radiology, Sir Run Run Shaw Hospital (SRRSH) of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shengyu Kuang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhiqiang Li
- Department of Neurosurgery&Brain Glioma Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yahua Zhong
- Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yuxiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sirui Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jun Chen
- Wuhan GE Healthcare, Wuhan, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junjie Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 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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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 2024:10.1007/s00330-024-10594-x. [PMID: 38308012 DOI: 10.1007/s00330-024-10594-x] [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: 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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Texture Feature-Based Machine Learning Classification on MRI Image for Sepsis-Associated Encephalopathy Detection: A Pilot Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:6403556. [PMID: 36778786 PMCID: PMC9911249 DOI: 10.1155/2023/6403556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/21/2022] [Accepted: 12/26/2022] [Indexed: 02/05/2023]
Abstract
Objective The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone. Method Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model. Results The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set. Conclusion The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone.
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A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas. J Clin Med 2022; 11:jcm11133802. [PMID: 35807084 PMCID: PMC9267404 DOI: 10.3390/jcm11133802] [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: 06/06/2022] [Revised: 06/18/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.
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Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022; 43:153-169. [PMID: 35339256 PMCID: PMC8961005 DOI: 10.1053/j.sult.2022.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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Affiliation(s)
- Muhammad Afridi
- School of Osteopathic Medicine, Rowan University, Stratford, NJ
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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Huang WY, Wen LH, Wu G, Pang PP, Ogbuji R, Zhang CC, Chen F, Zhao JN. Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis. Cancer Sci 2021; 112:2835-2844. [PMID: 33932065 PMCID: PMC8253278 DOI: 10.1111/cas.14918] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/05/2021] [Accepted: 04/08/2021] [Indexed: 12/26/2022] Open
Abstract
This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross‐sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast‐enhanced T1, T2, and T2 FLAIR. Each sequence’s Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.
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Affiliation(s)
- Wei-Yuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China.,Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Ling-Hua Wen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Pei-Pei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Richard Ogbuji
- Department of Neurosurgery, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chao-Cai Zhang
- Department of Neurosurgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jian-Nong Zhao
- Department of Neurosurgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
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