BACKGROUND: Intraoperative identification of cancerous tissue is fundamental during oncological surgical or endoscopic procedures. This relies on visual assessment supported by histopathological evaluation, implying a longer operative time. Hyperspectral imaging (HSI), a contrast-free and contactless imaging technology, provides spatially resolved spectroscopic analysis, with the potential to differentiate tissue at a cellular level. However, HSI produces "big data", which is impossible to directly interpret by clinicians. We hypothesize that advanced machine learning algorithms (convolutional neural networks-CNNs) can accurately detect colorectal cancer in HSI data. METHODS: In 34 patients undergoing colorectal resections for cancer, immediately after extraction, the specimen was opened, the tumor-bearing section was exposed and imaged using HSI. Cancer and normal mucosa were categorized from histopathology. A state-of-the-art CNN was developed to automatically detect regions of colorectal cancer in a hyperspectral image. Accuracy was validated with three levels of cross-validation (twofold, fivefold, and 15-fold). RESULTS: 32 patients had colorectal adenocarcinomas confirmed by histopathology (9 left, 11 right, 4 transverse colon, and 9 rectum). 6 patients had a local initial stage (T1-2) and 26 had a local advanced stage (T3-4). The cancer detection performance of the CNN using 15-fold cross-validation showed high sensitivity and specificity (87% and 90%, respectively) and a ROC-AUC score of 0.95 (considered outstanding). In the T1-2 group, the sensitivity and specificity were 89% and 90%, respectively, and in the T3-4 group, the sensitivity and specificity were 81% and 93%, respectively. CONCLUSIONS: Automatic colorectal cancer detection on fresh specimens using HSI, using a properly trained CNN is feasible and accurate, even with small datasets, regardless of the local tumor extension. In the near future, this approach may become a useful intraoperative tool during oncological endoscopic and surgical procedures, and may result in precise and non-destructive optical biopsies to support objective and consistent tumor-free resection margins.
BACKGROUND: Intraoperative identification of cancerous tissue is fundamental during oncological surgical or endoscopic procedures. This relies on visual assessment supported by histopathological evaluation, implying a longer operative time. Hyperspectral imaging (HSI), a contrast-free and contactless imaging technology, provides spatially resolved spectroscopic analysis, with the potential to differentiate tissue at a cellular level. However, HSI produces "big data", which is impossible to directly interpret by clinicians. We hypothesize that advanced machine learning algorithms (convolutional neural networks-CNNs) can accurately detect colorectal cancer in HSI data. METHODS: In 34 patients undergoing colorectal resections for cancer, immediately after extraction, the specimen was opened, the tumor-bearing section was exposed and imaged using HSI. Cancer and normal mucosa were categorized from histopathology. A state-of-the-art CNN was developed to automatically detect regions of colorectal cancer in a hyperspectral image. Accuracy was validated with three levels of cross-validation (twofold, fivefold, and 15-fold). RESULTS: 32 patients had colorectal adenocarcinomas confirmed by histopathology (9 left, 11 right, 4 transverse colon, and 9 rectum). 6 patients had a local initial stage (T1-2) and 26 had a local advanced stage (T3-4). The cancer detection performance of the CNN using 15-fold cross-validation showed high sensitivity and specificity (87% and 90%, respectively) and a ROC-AUC score of 0.95 (considered outstanding). In the T1-2 group, the sensitivity and specificity were 89% and 90%, respectively, and in the T3-4 group, the sensitivity and specificity were 81% and 93%, respectively. CONCLUSIONS: Automatic colorectal cancer detection on fresh specimens using HSI, using a properly trained CNN is feasible and accurate, even with small datasets, regardless of the local tumor extension. In the near future, this approach may become a useful intraoperative tool during oncological endoscopic and surgical procedures, and may result in precise and non-destructive optical biopsies to support objective and consistent tumor-free resection margins.
Authors: Hamed Akbari; Luma V Halig; David M Schuster; Adeboye Osunkoya; Viraj Master; Peter T Nieh; Georgia Z Chen; Baowei Fei Journal: J Biomed Opt Date: 2012-07 Impact factor: 3.170
Authors: John Y K Lee; Steve S Cho; Walter Stummer; Janos L Tanyi; Alexander L Vahrmeijer; Eben Rosenthal; Barbara Smith; Eric Henderson; David W Roberts; Amy Lee; Constantinos G Hadjipanayis; Jeffrey N Bruce; Jason G Newman; Sunil Singhal Journal: J Biomed Opt Date: 2019-12 Impact factor: 3.170
Authors: Boris Jansen-Winkeln; Manuel Barberio; Claire Chalopin; Katrin Schierle; Michele Diana; Hannes Köhler; Ines Gockel; Marianne Maktabi Journal: Cancers (Basel) Date: 2021-02-25 Impact factor: 6.575