ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles

• Published in: British journal of cancer Vol. 131; no. 5; pp. 883 - 894
• Main Authors: Li, Tiandong, Sun, Guiying, Ye, Hua, Song, Caijuan, Shen, Yajing, Cheng, Yifan, Zou, Yuanlin, Fang, Zhaoyang, Shi, Jianxiang, Wang, Keyan, Dai, Liping, Wang, Peng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.09.2024; Nature Publishing Group
• Subjects: 631/250/580; 631/67/1857; 692/53/2421; Aged; Autoantibodies; Autoantibodies - blood; Autoantibodies - immunology; Biomarkers, Tumor - blood; Biomedical and Life Sciences; Biomedicine; Cancer Research; Drug Resistance; Early Detection of Cancer - methods; Enzyme-linked immunosorbent assay; Epidemiology; Esophageal cancer; Esophageal carcinoma; Esophageal Neoplasms - blood; Esophageal Neoplasms - diagnosis; Esophageal Neoplasms - immunology; Esophageal Squamous Cell Carcinoma - blood; Esophageal Squamous Cell Carcinoma - diagnosis; Esophageal Squamous Cell Carcinoma - immunology; Female; HSF1 protein; Humans; Learning algorithms; Machine Learning; Male; Medical diagnosis; Middle Aged; Molecular Medicine; Oncology; Prediction models; Proteomes; Public health; ROC Curve; SNAP-25 protein; Squamous cell carcinoma; Support Vector Machine; Support vector machines

Background Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection. Methods The study encompassed three phases: discovery, ve...