ArticleBMC medical informatics and decision making2023
Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Development and Validation of a Coronary Computed Tomography Angiography-Based Radiomics-Integrated Model for Noninvasive Detection of Left Atrial Appendage Thrombus in Atrial Fibrillation.Journal of the American Heart Association · 2026Article
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses.Journal of multidisciplinary healthcare · 2025Article
- Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era.World journal of gastroenterology · 2024Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis 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.
methodsA 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.
resultsThe 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).
conclusionsThe 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.
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