18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients

Hongyue Zhao; Zhehao Lyu; Yexin Su; Huiting Zhang; Zi-Yue Zhang; Peng Xu; Zhongwei Lv; Peng Fu

doi:10.1007/s00261-025-05282-3

November 11, 2025Abdominal RadiologyOpen Access

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients

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Key Points

This research aims to develop and evaluate a deep learning model for predicting lymphovascular invasion in colorectal cancer.
Used a 2.5D ensemble deep learning model
Analyzed 18F-FDG PET images
Focused on colorectal cancer patients
The model performed well in predicting lymphovascular invasion
Demonstrated its potential as a tool for precision medical support in CRC

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Implication

Ensemble deep learning model predicts lymphovascular invasion in colorectal cancer, indicating improved precision medicine support.

Abstract

The 2.5D ensemble DL model based on ¹⁸F-FDG PET images performed well for the prediction of LVI in CRC, proving its potential as a precision medical support tool for CRC patients.

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Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/69252e9ec0ce034ddc3565c2 https://doi.org/10.1007/s00261-025-05282-3

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18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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