The machine learning technique developed by Dr Takemura and team could distinguish tumor tissue from healthy tissue in ex vivo images of resected tumors, with 86% accuracy. Credit: Hiroshi Takemura from Tokyo University of Science Read Time: Tumors can be damaging to surrounding blood vessels and tissues even if they're benign. If they're malignant, they're aggressive and sneaky, and often irrevocably damaging. In the latter case, early detection is key to treatment and recovery. But such detection can sometimes require advanced imaging technology, beyond what is available commercially today. For instance, some tumors occur deep inside organs and tissues, covered by a mucosal layer, which makes it difficult for scientists to directly observe them with standard methods like endoscopy (which inserts a small camera into a patient's body via a thin tube) or reach them during biopsies. In particular, gastrointestinal stromal tumors (GISTs)--typically found in the stomach and the small intestines--require demanding techniques that are very time-consuming and prolong the diagnosis. Now, to improve GIST diagnosis, Drs. Daiki Sato, Hiroaki Ikematsu, and Takeshi Kuwata from the National Cancer Center Hospital East in Japan, Dr. Hideo Yokota from the RIKEN Center for Advanced Photonics, Japan, and Drs. Toshihiro Takamatsu and Kohei Soga from Tokyo University of Science, Japan, led by Dr. Hiroshi Takemura, have developed a technology that uses near-infrared hyperspectral imaging (NIR-HSI) along with machine learning. Their findings are published in Nature's