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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Radiotherapy | Research

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

Authors: Yong Huang, Xiaoyu Huang, Anling Wang, Qiwei Chen, Gong Chen, Jingya Ye, Yaru Wang, Zhihui Qin, Kai Xu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

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.

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Title: Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion
Authors: Yong Huang
Xiaoyu Huang
Anling Wang
Qiwei Chen
Gong Chen
Jingya Ye
Yaru Wang
Zhihui Qin
Kai Xu
Publication date: 01-12-2023
Publisher: BioMed Central
Keyword: Radiotherapy
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02339-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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