New Israeli AI tech can detect cancerous biomarkers in real time

The new technology can drastically speed up detection time for patients.

Artificial intelligence (AI) models developed by Sheba Medical Center and the Imagene precision-oncology-diagnosis company in Tel Aviv have been used to detect cancerous biomarkers in real-time from a biopsy image alone.

Identifying gene alterations is key for improving patient care and guiding targeted therapeutic decisions. Lung cancer, resulting mostly from smoking, is the most common cancer and accounts for some 1.76 million deaths per year worldwide. Non-small cell lung cancer (NSCLC) comprises 85% of all lung cancers and is typically diagnosed at advanced stages.

The researchers have just published their findings in the Modern Pathology journal. It appears under the title “Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms.”

ALK and ROS1 gene fusions are well-established key players in NSCLC. The National Cancer Center Network (NCCN) guidelines recommend broad molecular profiling using comprehensive next-generation sequencing (NGS). This presents several obstacles including insufficient tissue for testing, poor quality of DNA or RNA, sequencing failure and high turnaround time that can span between two weeks to even six weeks – a time that lung cancer patients do not have as statistics show that 10% to 20% of lung cancer patients will die within one to three months of diagnosis.

AI is currently being intensively researched in the field of pathology, yet only few examples have shown superior results in terms of performance. “Our results demonstrate the advantages that image-based AI solutions have in the molecular pathology domain, by enabling fast and accurate biomarker detection and overcoming limitations encountered when using traditional lab methods,” the team said.[…]