Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion

• Published in: BMC medical informatics and decision making Vol. 23; no. 1; pp. 1 - 15
• Main Authors: Huang, Yong, Huang, Xiaoyu, Wang, Anling, Chen, Qiwei, Chen, Gong, Ye, Jingya, Wang, Yaru, Qin, Zhihui, Xu, Kai
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.10.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Advanced machine learning and health-related multi-omics data; Aged patients; Analysis; Biochemistry; Blood; Blood tests; Cancer therapies; Care and treatment; Chemoradiotherapy; Chemotherapy; Clinical medicine; Computed tomography; Concurrent chemoradiotherapy; CT imaging; Data integration; Decision making; Diagnosis; Elongation; Esophageal cancer; Esophageal carcinoma; Esophageal squamous cell carcinoma; Health Informatics; Hospitals; Individualized treatment decision model; Information Systems and Communication Service; Inoperable elder patients; Learning algorithms; Lymphatic system; Machine learning; Management of Computing and Information Systems; Medical examination; Medical imaging; Medical prognosis; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Methods; Modal data; Nomograms; Older people; Patient outcomes; Patients; Prediction models; Prognosis; Radiation therapy; Radiomics; Radiotherapy; Risk factors; Risk groups; Skewness; Squamous cell carcinoma; Survival; Tumors; China; Individualized treatment decision model; Esophageal squamous cell carcinoma; Inoperable elder patients; Radiotherapy; Concurrent chemoradiotherapy
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02339-5

Background This research aimed to develop a model for individualized treatment decision-making in inoperable elderly patients with esophageal squamous cell carcinoma (ESCC) using machine learning methods and multi-modal data. Methods A total of 189 inoperable elderly ESCC patients aged 65 or older who underwent concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) were included. Multi-task learning models were created using machine learning techniques to analyze multi-modal data, including pre-treatment CT images, clinical information, and blood test results. Nomograms were constructed to predict the objective response rate (ORR) and progression-free survival (PFS) for different treatment strategies. Optimal treatment plans were recommended based on the nomograms. Patients were stratified into high-risk and low-risk groups using the nomograms, and survival analysis was performed using Kaplan–Meier curves. Results The identified risk factors influencing ORR were histologic grade (HG), T stage and three radiomic features including original shape elongation, first-order skewness and original shape flatness, while risk factors influencing PFS included BMI, HG and three radiomic features including high gray-level run emphasis, first-order minimum and first-order skewness. These risk factors were incorporated into the nomograms as independent predictive factors. PFS was substantially different between the low-risk group (total score ≤ 110) and the high-risk group (total score > 110) according to Kaplan–Meier curves ( P  < 0.05). Conclusions The developed predictive models for ORR and PFS in inoperable elderly ESCC patients provide valuable insights for predicting treatment efficacy and prognosis. The nomograms enable personalized treatment decision-making and can guide optimal treatment plans for inoperable elderly ESCC patients.