Background While there are innumerable Machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature. Methods We performed a systematic literature review on four databases. Of 11,727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD. Results We found that while many articles were published on ML based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice. Conclusion In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.
PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.
PURPOSE Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volumetric segmentations in neuroradiology clinical workflow. Material: 1990 patients from Yale Radiation Oncology Registry (2012-2019) were identified. Segmentations were performed using a UNETR algorithm trained on BRaTS 2021 and an internal dataset of manually segmented tumors. Segmentations were validated by a board-certified neuro-radiologist and natively embedded PyRadiomics in PACS was used for feature extraction. RESULTS In 7 Months (05/2021 - 08/2021, 03/2022 - 05/2022) segmentations and annotations were performed in 835 patients (322 female, 467 male, 46 unknown, mean age 53 yrs). Dataset includes 275 Grade 4 Gliomas (54 Grade 3, 100 Grade 2, 31 Grade 1, 375 unknown). Molecular subtypes include IDH (113 mutated, 498 wildtype, 2 Equivocal, 222 unknown), 1p/19q (87 deleted or co-deleted, 122 intact, 626 unknown), MGMT promotor (182 methylated, 95 partially methylated, 275 unmethylated, 283 unknown), EGFR (76 amplified, 177 not amplified, 582 unknown), ATRX (40 mutated, 157 retained, 638 unknown), Ki-67 (616 known, 219 unknown) and p53 (549 known, 286 unknown). Classification of gliomas between grade 3/4 and grade 1/2, yielded AUC of 0.85. CONCLUSION We developed a method for incorporation of volumetric segmentation, feature extraction, and classification that is easily incorporated into neuroradiology workflow. These tools allowed us to annotate over 100 gliomas per month, thus establishing a proof of concept for rapid development of annotated imaging database for AI applications.
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