Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

The FDA has authorized the marketing of Paige Prostate, an AI-based software platform to help pathologists identify prostate cancer when they review slide images from prostate biopsies.1 The standard biopsy review process involves the pathologist examining digitally scanned slide images from prostate biopsies to find areas that are suspicious for cancer. Paige Prostate provides a supplementary assessment of the image and locates the area with the highest probability of harboring cancer. The pathologist can then examine this specific area further if they did not identify it on their initial assessment. "Pathologists examine biopsies of tissue suspected for diseases, such as prostate cancer, every day. Identifying areas of concern on the biopsy image can help pathologists make a diagnosis that informs the appropriate treatment," Tim Stenzel, MD, PhD, director of the Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health, stated in a press release.

Diagnosing cancer is about to get more accurate, with the help of artificial intelligence. Pathologists have diagnosed diseases in more or less the same way for the past 100 years, by laboring over a microscope reviewing biopsy samples on little glass slides. Working almost robotically, they sift through millions of normal cells to identify just a few diseased ones. The task is tedious and prone to human error. But now, scientists and engineers have created a technique that uses artificial intelligence (AI) and can differentiate cancer cells from normal cells almost as well as a top-notch pathologist.

At the International Symposium on Biomedical Imaging in Prague, a Harvard-based artificial intelligence system won the Camelyon16 challenge, a competition comprised of participants introducing their individual AI systems and its ability to facilitate automated lymph node metastasis diagnosis. Referred to as PathAl, the computing system identifies cancerous cells through a mechanism referred to as deep learning--an algorithmic technique that accumulates copious amounts of unstructured data and organizes it into clusters, before analyzing it for patterns. Deep learning is predominately utilized in speech recognition systems like Apple's Siri and Microsoft's Cortana. According to one of the challenge's organizers, Jeroen van der Laak of Radboud University Medical Center in Netherlands, the technology featured in the competition went "way beyond" his expectations, as the AI's accuracy proved strikingly close to that of human beings. In addition, van der Laak said AI technology has the propensity to intrinsically redefine the way histopathological images are handled in the medical community.

At the International Symposium on Biomedical Imaging in Prague this past April, a Harvard-based artificial intelligence system won the Camelyon16 challenge, a competition comprised of participants introducing their individual AI system and its ability to facilitate automated lymph node metastasis diagnosis. Referred to as PathAl, the computing system identifies cancerous cells through deep learning--an algorithmic technique that accumulates copious amounts of unstructured data and organizes it into clusters before analyzing it for patterns. Deep learning is predominately used in speech recognition systems like Apple's Siri and Microsoft's Cortana. According to one of the challenge's organizers, Jeroen van der Laak of Radboud University Medical Center in Netherlands, the technology featured in the competition went "way beyond" his expectations, as the AI's accuracy proved strikingly close to that of human beings. In addition, van der Laak said AI technology has the propensity to intrinsically redefine the way histopathological images are handled in the medical community.

Diagnosing cancer is about to get more accurate, with the help of artificial intelligence. Pathologists have diagnosed diseases in more or less the same way for the past 100 years, by laboring over a microscope reviewing biopsy samples on little glass slides. Working almost robotically, they sift through millions of normal cells to identify just a few diseased ones. The task is tedious and prone to human error. But now, scientists and engineers have created a technique that uses artificial intelligence (AI) and can differentiate cancer cells from normal cells almost as well as a top-notch pathologist.