End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.

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Publication Details

Journal
npj Precision Oncology
Published
2025-08-15
DOI
https://doi.org/10.1038/s41698-025-01077-3
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study

Weidao Chen, Nan Hong, Dawei Wang, Junwen Zhong et al.
1 citations
npj Precision Oncology
Radiomics and Machine Learning in Medical Imaging
1.36
article

End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study

Weidao Chen, Nan Hong, Dawei Wang, Junwen Zhong, Ruize Yu, Ping Yin, Ke Liu, Xia Liu, Ying Liu, Tianyu Zhang, Chao Sun, R. Chen, Lin Lu, Yang Liu
article en
1 citations

Abstract

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.

npj Precision OncologyVol. 9(1)
Memorial Sloan Kettering Cancer Center (US), Guangxi Medical University (CN), Peking University (CN), InferVision (China) (CN), First Affiliated Hospital of GuangXi Medical University (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Peking University Third Hospital (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China, Peking University, Peking University People's Hospital
Quality Education
Openalex Percentile: Top 17%
Radiomics and Machine Learning in Medical Imaging
1.36
DOIOpen Access PDFOpenAlex
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