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BackgroundWhile 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.MethodsWe performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD.ResultsWe 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.ConclusionsIn 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.

Background Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery. Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response (HeR) to treatment after Gamma Knife (GK). Methods We selected a group of brain metastases patients treated with GK at our institution. We used a PACS-integrated LTT to track the treatment response of each lesion after first GK intervention to maximally seven diagnostic follow-up scans. We evaluated the efficiency of this tool by comparing the number of clicks necessary to complete this task with and without the tool and examined the prevalence of HeR in treatment. Results A cohort of eighty patients was selected and 494 lesions were measured and tracked longitudinally for a mean follow-up time of 374 days after first GK. Use of LTT significantly decreased number of necessary clicks. 81.7% of patients had HeR to treatment at the end of follow-up. The prevalence increased with increasing number of lesions. Conclusions Lesions in a single patient often differ in their response to treatment, highlighting the importance of individual lesion size assessments for further treatment planning. PACS-integrated lesion tracking enables efficient lesion surveillance workflow and specific and objective result reports to treating clinicians.

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.

PURPOSE Stereotactic radiosurgery (SRS) has become the mainstay to treat BM. Follow-up MRI provides important information on lesion treatment response and guides future therapy planning. Volumetric measurements of BM have shown promise over traditional uni- and two-dimensional measurements in more accurate and repeatable assessment. However, routine clinical use has yet to be achieved because the workflow is laborious. In previous work, we developed a PACS-integrated deep learning algorithm for automatic high- and low-grade glioma 3D segmentation. In this work, we applied this U-Net to segment BM on pre- and post-Gamma Knife (GK) MRI and evaluated the performance. METHODS 10 pre- and post-GK studies were autosegmented in five randomly selected patients (melanoma n= 3, breast n= 2). The glioma trained algorithm segmented the “Whole Tumor” (tumor core+peritumoral edema on T2w-FLAIR) and “Tumor Core” (CE tumor core+necrosis on SPGR). The AI generated segmentation was then revised as needed by a board-certified neuroradiologist and the dice-similarity-coefficient (DSC) between the revised and automatic volumetric segmentations were calculated. RESULTS Four patients had multicentric (2-4 BM) lesions. The mean± SD DSC for Whole Tumor and Tumor Core were 0.92±0.06 and 0.46±0.30 for pretreatment, 0.84±0.09 and 0.41±0.25 for posttreatment BM, respectively. The tool detected lesions with a sensitivity of 45% (5/11) for pretreatment and 50% (3/6) for posttreatment lesions. Three pretreatment and all posttreatment lesions that were not detected by the autosegmentation tool showed a very faint hyperintense peritumoral edema in T2w-FLAIR. CONCLUSION Volumetric segmentation of edema on FLAIR using the glioma-trained segmentation algorithm on pre- and post-GK BM did not require major adjustment of segmentation if it detects the lesion. On the other hand, with low sensitivity of lesion detection and low DSC for enhancing component, dedicated training of the algorithm on annotated BM data will be needed.

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

PURPOSE Machine learning (ML) technologies have demonstrated highly accurate prediction of glioma grade, though it is unclear which methods and algorithms are superior. We have conducted a systematic review of the literature in order to identify the ML applications most promising for future research and clinical implementation. MATERIALS AND METHODS A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Screening of publications was done in Covidence, and TRIPOD was used for bias assessment. RESULTS The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles published since 1995 met the criteria for our study. 79% of the articles were published between 2018 and 2020. The average glioma prediction accuracy of the highest performing model in each study was 90% (range: 53% to 100%). The most common algorithm used for cML studies was Support Vector Machine (SVM) and for DL studies was Convolutional Neural Network (CNN). BRATS and TCIA datasets were used in 47% of the studies, with the average patient number of study datasets being 186 (range: 23 to 662). The average number of features used in machine learning prediction was 55 (range: 2 to 580). Classical machine learning (cML) was the primary machine learning model in 68% of studies, with deep learning (DL) used in 32%. CONCLUSIONS Using multimodal sequences in ML methods delivers significantly higher grading accuracies than single sequences. Potential areas of improvement for ML glioma grade prediction studies include increasing sample size, incorporating molecular subtypes, and validating on external datasets.

PURPOSE Monitoring metastatic disease to the brain is laborious and time-consuming, especially in the setting of multiple metastases and when performed manually. Response assessment in brain metastases based on maximal unidimensional diameter as per the RANO-BM guideline is commonly performed1, however, accurate volumetric lesion estimates can be crucial for clinical decision-making2 and enhance outcome prediction3. We propose a deep learning (DL)-based auto-segmentation approach with the potential for improvement of time-efficiency, reproducibility and robustness against inter-rater variability. Materials and METHODS We retrospectively retrieved 259 patients with a total number of 916 lesions from our institutional database from 2014 - 2021. Patients with prior history of local radiation therapy or surgery were excluded. Manually generated trainee segmentations were revised and adjusted by a board-certified radiologist and served as ground truth for evaluation of segmentation accuracy. Model performance was tested via dice-similarity-coefficient (DSC). Volumetric measurements were then obtained within our PACS-integrated workflow on Visage 7 (Visage Imaging, Inc., San Diego, CA) at the click of one button. RESULTS For model training and evaluation, a train-test split of 90:10 on patient-level was performed (n= 234:25 (Patients), n= 861:55 (Lesions). A DL-algorithm (nnUNet) was incrementally trained on 10 batches of 23 patients. The DSC of the U-Net gradually increased throughout the training process and heuristically reached a plateau of 0.85. The sensitivity of the algorithm was 83% with detection of 46 out of 55 lesions in the testing dataset. The lesions that were not detected by the algorithm were below 5 mm in size. The false positive rate was 8% (n=4/50). CONCLUSION Our study demonstrates the feasibility of PACS-based integration of automatized segmentation workflows of brain metastases. An incremental-training approach is recommended to adapt DL algorithms to specific hospital settings.

This is an openly licensed framework that can be used by educators and students to construc jam, conferences and events, fully online or blended, using creative and active learning approaches with professionals and/or students in higher education or other educational settings. The ideas for the Open Jam Jar are based on research and practice of the #creativeHE community, the related annual #creativeHE jam and inspired and shaped further by the Global Culture Jam (GCJ) which took place at Manchester Metropolitan University in the United Kingdom in June 2021 and attracted over 1400 sign-ups from different parts of the world. Global Culture Jam is a collaborative project that aims to bring together students and staff to explore and celebrate cross-cultural learning, teaching and living through the following strands: community, sustainability, curriculum and creativity. These were defined and agreed with the team of collaborators taking into consideration strategic institutional goals, aspirations and as well as wider higher education conversations and areas of focus and exploration that presented rich opportunities to bring diverse views and perspectives together that would be of value for organisers, the institution participants from different parts of the world and the wider academic community. The team intends to share lessons learnt from the GCJ and make the Open Jam Jar framework available to others to re-use, adapt and re-package to create their own Jam event using the Open Jam Jar framework we have developed.

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models—including data sources, external validation, and glioma grade classification methods —are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

PURPOSE Reporting guidelines are crucial in model development studies to ensure the quality, transparency and objectivity of reporting. While machine learning (ML) models have proven themselves effective in predicting glioma grade, their potential use can only be determined if they are clearly and comprehensively reported. Reporting quality has not yet been evaluated for ML glioma grade prediction studies, to our knowledge. We measured published literature against the TRIPOD Statement, a checklist of items considered essential for the reporting of diagnostic studies. MATERIALS AND METHODS A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Publications were screened in Covidence and scored against the 27 items in the TRIPOD Statement that were relevant and applicable. RESULTS The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles met the criteria for our study. The mean adherence rate to TRIPOD was 44.4% (range: 22.2% - 66.7%), with poor reporting adherence in categories including abstract (0%), model performance (0%), title (1.2%), justification of sample size (2.3%), full model specification (2.3%), participant demographics and missing data (7%). Studies had high reporting adherence in categories including results interpretation (100%), background (98.8%), study design/source of data (96.5%), and objectives (95.3%). CONCLUSION Existing publications on the use of ML in glioma grade prediction have a low overall quality of reporting. Improvements can be made in the reporting of titles and abstracts, justification of sample size, and model specification and performance.