Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma

The proposed ultrasound-based ensemble model offers a non-invasive, reproducible method to predict PD-1 blockade response in HCC, effectively integrating imaging and clinical data to enhance predictive accuracy and reveal potential molecular mediators of therapeutic efficacy. We developed an advanced automated predictive model that synergistically integrates ultrasound imaging with clinical indicators through ensemble learning methodology. This innovative model employs state-of-the-art deep learning architectures, specifically optimized convolutional neural networks, to accurately predict therapeutic response to PD-1 blockade in patients with unresectable hepatocellular carcinoma.

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Publication Details

Journal
BMC Gastroenterology
Published
2025-12-07
DOI
https://doi.org/10.1186/s12876-025-04512-8
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma

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BMC Gastroenterology
Radiomics and Machine Learning in Medical Imaging
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Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma

Chao Liang, Jian Ren, Jin-Kai Feng, Chui Kong, Shuqun Cheng, Lin Gong, Fuxiang Zhang, Yi Qian, Dong Jiang, Yan‐Jun Xiang, Zong‐Han Liu, Liheng Liu, Wenjing Wei, Hongkun Zhou, Yiran Li, Boyang Zang, Kang Wang
article en

Abstract

The proposed ultrasound-based ensemble model offers a non-invasive, reproducible method to predict PD-1 blockade response in HCC, effectively integrating imaging and clinical data to enhance predictive accuracy and reveal potential molecular mediators of therapeutic efficacy. We developed an advanced automated predictive model that synergistically integrates ultrasound imaging with clinical indicators through ensemble learning methodology. This innovative model employs state-of-the-art deep learning architectures, specifically optimized convolutional neural networks, to accurately predict therapeutic response to PD-1 blockade in patients with unresectable hepatocellular carcinoma.

BMC GastroenterologyVol. 26(1)
Second Military Medical University (CN), Fudan University (CN), Shanghai University of Traditional Chinese Medicine (CN), People's Liberation Army 401 Hospital (CN), Yueyang Hospital (CN), Eastern Hepatobiliary Surgery Hospital (CN), First Hospital of Jiaxing (CN), Tsinghua University (CN)
Openalex Percentile: Top 46%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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