Chinese Academy of Sciences Reports Findings in Liver Cancer (Novel immune class ification based on machine learning of pathological images predicts early recurr ence of hepatocellular carcinoma)
Chinese Academy of Sciences Reports Findings in Liver Cancer (Novel immune class ification based on machine learning of pathological images predicts early recurr ence of hepatocellular carcinoma)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Liver Cance r is the subject of a report. According to news reporting originating from Guang dong, People's Republic of China, by NewsRx correspondents, research stated, "Im mune infiltration within the tumor microenvironment (TME) plays a significant ro le in the onset and progression of hepatocellular carcinoma (HCC). Machine learn ing applied to pathological images offers a practical means to explore the TME a t the cellular level." Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "Our former research employed a transfer learning procedure to adapt a convolutional neural network (CNN) model for cell recognition, which could reco gnize tumor cells, lymphocytes, and stromal cells autonomously and accurately wi thin the images. This study introduces a novel immune classification system base d on the modified CNN model. Patients with HCC from both Beijing Hospital and Th e Cancer Genome Atlas (TCGA) database were included in this study. Additionally, least absolute shrinkage and selection operator (LASSO) analyses, along with lo gistic regression, were utilized to develop a prognostic model. We proposed an i mmune classification based on the percentage of lymphocytes, with a threshold se t at the median lymphocyte percentage. Patients were categorized into high or lo w infiltration subtypes based on whether their lymphocyte percentages were above or below the median, respectively. Patients with different immune infiltration subtypes exhibited varying clinical features and distinct TME characteristics. T he low-infiltration subtype showed a higher incidence of hypertension and fatty liver, more advanced tumor stages, downregulated immune-related genes, and highe r infiltration of immunosuppressive cells. A reliable prognostic model for predi cting early recurrence of HCC based on clinical features and immune classificati on was established. The area under the curve (AUC) of the receiver operating cha racteristic (ROC) curves was 0.918 and 0.814 for the training and test sets, res pectively."
Key words
Guangdong/People's Republic of China/Asia/Cancer/Carcinomas/Cyborgs/Emerging Technologies/Health and Medicine/Li ver Cancer/Machine Learning/Oncology
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