首页|Nanjing University Reports Findings in Lung Cancer (Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level)
Nanjing University Reports Findings in Lung Cancer (Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Lung Cancer is the subject of a report. According to news originating from Nanjing, People' s Republic of China, by NewsRx correspondents, research stated, "In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involve s the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC." Our news journalists obtained a quote from the research from Nanjing University, "Nevertheless, current methodologies rely on the visual interpretation of tissu e images by proficient pathologists, introducing an error margin of up to 15.6% . In this study, we investigated the utilization of Micro-Raman technique on iso lated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of in filtration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the i ntegration of Raman spectroscopy with advanced deep learning models for simultan eous diagnosis, this approach offers a swift, precise, and clinically relevant m eans of analysis. The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exce ptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively asse ss its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the e stablished CNN model. The results underscored the substantial corrective capacit y of the Micro-Raman technique, revealing a misdiagnosis correction rate exceedi ng 96% in all cases. Ultimately, our discoveries highlight the Mic ro-Raman technique's potential to augment the intraoperative diagnostic precisio n of LADC with varying levels of infiltration. And compared to the traditional S VM model, the CNN model has better generalization ability in diagnosing differen t infiltration levels."
NanjingPeople's Republic of ChinaAsi aAdenocarcinomaCancerDiagnostics and ScreeningHealth and MedicineLung CancerLung Diseases and ConditionsOncology
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