Work

Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients

Year: 2025

Type: article

Abstract: Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulating treatment plans. Despite the widespread attention to pathology in the treatment of HCC patients, a large amount of information contained in pathological images is often overlooked. Methods We retrospectively collected clinical data and pathological slide images from (a) 331 HCC patients at Qingdao University Affiliated Hospital between January 2013 and December 2016 and (b) 180 HCC patients from The Cancer Genome Atlas (TCGA). After data screening, precise quantification of various cell types was achieved using QuPath software. Key factors related to the survival prognosis of pathologically confirmed HCC patients were identified through Cox regression and neural network models, and potential therapeutic targets were screened. Results Our study showed that tumour-infiltrating lymphocytes (TILs) had a protective effect. We quantified the TILs index by machine learning and built a neural network model to predict the prognostic risk of patients (ROC = 0.836 for training set ROC validation set). 95% CI [0.7688–0.896], and there was a significant difference in prognosis in the high-low risk group predicted by the model (p = 2.6e−18, HR = 0.18, 95% CI [0.12–0.27], and TNFSF4 was identified as a possible immunotherapy target. Conclusion This study included a total of 511 patients, divided into a training cohort of 331 cases (from Qingdao University Hospital between January 2013 and December 2016) and a validation cohort of 180 cases (TCGA). The results revealed that tumor-infiltrating lymphocytes (TILs) have a protective effect and successfully predicted the survival risk of liver cancer patients using machine learning and neural network technology. The discovery of TNFSF4 provides a new potential target for immunotherapy.

Source: PeerJ

DOI: 10.7717/peerj.19351