Sara Merkaj

BACKGROUND: The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor. PURPOSE: We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging. DATA SOURCES: Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science. STUDY SELECTION: Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria. DATA ANALYSIS: We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines. DATA SYNTHESIS: Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles. LIMITATIONS: The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias. CONCLUSIONS: While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

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.

Aims Long QT syndrome type 2 (LQT2) is caused by mutations in the gene KCNH2, encoding the hERG ion channel. Clinically, mild and severe phenotypes are associated with this cardiac channelopathy, complicating efforts to predict patient risk. The location of the mutation within KCNH2 contributes to this variable disease manifestation. Here we determined whether such phenotypic differences could be detected in cardiomyocytes derived from isogenic human induced pluripotent stem cells (hiPSCs) genetically edited to harbour a range of KCNH2 mutations. Methods and Results The hiPSC lines heterozygous for missense mutations either within the pore or tail region of the ion channel were generated using CRISPR-Cas9 editing and subsequently differentiated to cardiomyocytes (hiPSC-CMs) for functional assessment. Electrophysiological analysis confirmed the mutations prolonged the action potentials and field potentials of the hiPSC-CMs, with differences detected between the pore and tail region mutations when measured as paced 2D monolayers. This was also reflected in the cytosolic Ca²⁺ transients and contraction kinetics of the different lines. Pharmacological blocking of the hERG channel in the hiPSC-CMs also revealed that mutations in the pore-loop region conferred a greater susceptibility to arrhythmic events. Conclusion These findings establish that subtle phenotypic differences related to the location of the KCNH2 mutation in LQT2 patients are reflected in hiPSC-CMs under genetically controlled conditions. Moreover, the results validate hiPSC-CMs as a strong candidate for evaluating the underlying severity of individual KCNH2 mutations in humans which could ultimately facilitate patient risk stratification. Translational perspective Clinical management of patients diagnosed with cardiac channelopathy diseases such as LQT2 is complicated by the variable disease phenotypes observed among mutation carriers, creating challenges for diagnosis, risk stratification and treatment. The genotype of the patient contributes to this clinical heterogeneity, with the influence of the mutation’s location within KCNH2 on a patient’s risk of a cardiac event being an example. Here we demonstrate that under stringently controlled genetic and experimental conditions, hiPSC-CMs are able to reflect these subtle genotype-phenotype differences, thereby providing new opportunities to stratify and potentially lessen sudden cardiac death risk amongst KCNH2 mutation carriers.

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.