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Updated: Sep 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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CAAFE-ResNet: A ResNet With Channel Attention-Augmented Feature Extraction for Prognostic Assessment in Rectal

Qing Lu¹, Jiaojiao Zhang¹, Qianwen Xue²

¹College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, China.

IET Systems Biology

|August 28, 2025

Summary

This summary is machine-generated.

A new deep learning model, CAAFE-ResNet18*, enhances magnetic resonance imaging (MRI) analysis for rectal cancer. This AI approach improves early identification of treatment response in locally advanced rectal cancer (LARC) patients.

Keywords:

biomedical MRI feature extraction medical image processing neural nets

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Area of Science:

Oncology
Radiology
Artificial Intelligence

Background:

Magnetic resonance imaging (MRI) is crucial for staging and evaluating rectal cancer.
Accurate prediction of treatment response is vital for optimizing patient management.

Purpose of the Study:

To introduce an innovative deep learning model, CAAFE-ResNet18*, for enhanced rectal cancer MRI analysis.
To improve the early identification of treatment response (complete response vs. non-response) in locally advanced rectal cancer (LARC).

Main Methods:

Development of the CAAFE-ResNet18* model, incorporating a feature extraction and complementation module (CAAFE).
CAAFE utilizes a multiscale dilated convolution parallel architecture and channel attention mechanism (CAM) for multilevel information fusion.
The model was tested on rectal cancer MRI data.

Main Results:

CAAFE-ResNet18* significantly outperformed existing convolutional neural network (CNN) backbone networks and state-of-the-art (SOTA) models.
The model demonstrated superior feature representation and overall performance in analyzing rectal cancer MR images.
Early identification of complete response (CR) and non-response (NR) to therapy was facilitated.

Conclusions:

The CAAFE-ResNet18* model offers a promising advancement in rectal cancer imaging analysis.
This AI-driven approach can aid clinicians in predicting treatment outcomes earlier.
Improved patient stratification and personalized treatment strategies for LARC are potential benefits.