首页|Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Re ports Findings in Esophageal Cancer (Raman spectroscopy for esophageal tumor dia gnosis and delineation using machine learning and the portable Raman spectromete r)
Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Re ports Findings in Esophageal Cancer (Raman spectroscopy for esophageal tumor dia gnosis and delineation using machine learning and the portable Raman spectromete r)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Esophageal Cancer is the subject of a report. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Esophage al cancer is one of the leading causes of cancer-related deaths worldwide. The i dentification of residual tumor tissues in the surgical margin of esophageal can cer is essential for the treatment and prognosis of cancer patients." Our news journalists obtained a quote from the research from the Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, "But the current diagnostic methods, either pathological frozen section or paraffin section exam ination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular infor mation with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tum or tissue in surgically resected specimens. We tested five machine learning-base d classification methods, including k-Nearest Neighbors, Adaptive Boosting, Rand om Forest, Principal Component Analysis-Linear Discriminant Analysis, and Suppor t Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 % ) in classifying the esophageal tumor and normal tissues. The portable Raman sys tem demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on th is, we finally achieve successful Raman visualization of tumor boundaries on sur gical margin specimens, and the Raman measurement time is less than 5 min."
ShanghaiPeople's Republic of ChinaAs iaCancerCyborgsDiagnostics and ScreeningEmerging TechnologiesEsophagea l CancerGastroenterologyHealth and MedicineMachine LearningOncology
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