Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study
Abstract Background Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients. Methods This retrospective study enrolled 321 rectal cancer patien...
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BMC
2025-06-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-03050-3 |
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| author | Qiong Ma Runqi Meng Ruiting Li Ling Dai Fu Shen Jie Yuan Danqi Sun Manman Li Caixia Fu Rong Li Feng Feng Yonggang Li Tong Tong Yajia Gu Yiqun Sun Dinggang Shen |
| author_facet | Qiong Ma Runqi Meng Ruiting Li Ling Dai Fu Shen Jie Yuan Danqi Sun Manman Li Caixia Fu Rong Li Feng Feng Yonggang Li Tong Tong Yajia Gu Yiqun Sun Dinggang Shen |
| author_sort | Qiong Ma |
| collection | DOAJ |
| description | Abstract Background Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients. Methods This retrospective study enrolled 321 rectal cancer patients (training set: 212; internal testing set: 53; external testing set: 56) who directly received total mesorectal excision from five hospitals between March 2014 to April 2021. A multitask deep learning model was developed to simultaneously predict recurrence/metastasis and disease-free survival (DFS). The model integrated clinicopathologic data and multiparametric magnetic resonance imaging (MRI) images including diffusion kurtosis imaging (DKI), without performing tumor segmentation. The receiver operating characteristic (ROC) curve and Harrell’s concordance index (C-index) were used to evaluate the predictive performance of the proposed model. Results The deep learning model achieved good discrimination capability of recurrence/metastasis, with area under the curve (AUC) values of 0.885, 0.846, and 0.797 in the training, internal testing and external testing sets, respectively. Furthermore, the model successfully predicted DFS in the training set (C-index: 0.812), internal testing set (C-index: 0.794), and external testing set (C-index: 0.733), and classified patients into significantly distinct high- and low-risk groups (p < 0.05). Conclusions The multitask deep learning model, incorporating clinicopathologic data and multiparametric MRI, effectively predicted both recurrence/metastasis and survival for patients with rectal cancer. It has the potential to be an essential tool for risk stratification, and assist in making individualized treatment decisions. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-ca3d3f133a474b0c8b9fe8e0aa857098 |
| institution | OA Journals |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-ca3d3f133a474b0c8b9fe8e0aa8570982025-08-20T02:05:13ZengBMCBMC Medical Informatics and Decision Making1472-69472025-06-0125111510.1186/s12911-025-03050-3Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective studyQiong Ma0Runqi Meng1Ruiting Li2Ling Dai3Fu Shen4Jie Yuan5Danqi Sun6Manman Li7Caixia Fu8Rong Li9Feng Feng10Yonggang Li11Tong Tong12Yajia Gu13Yiqun Sun14Dinggang Shen15Department of Radiology, Fudan University Shanghai Cancer CenterSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityDepartment of Radiology, Fudan University Shanghai Cancer CenterSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityDepartment of Radiology, Changhai Hospital, The Navy Medical UniversityDepartment of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiology, First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Nantong Tumor Hospital, Nantong UniversityMR Application Development, Siemens Shenzhen Magnetic Resonance Ltd.Department of Radiology, Fudan University Shanghai Cancer CenterDepartment of Radiology, Nantong Tumor Hospital, Nantong UniversityDepartment of Radiology, First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Fudan University Shanghai Cancer CenterDepartment of Radiology, Fudan University Shanghai Cancer CenterDepartment of Radiology, Fudan University Shanghai Cancer CenterSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityAbstract Background Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients. Methods This retrospective study enrolled 321 rectal cancer patients (training set: 212; internal testing set: 53; external testing set: 56) who directly received total mesorectal excision from five hospitals between March 2014 to April 2021. A multitask deep learning model was developed to simultaneously predict recurrence/metastasis and disease-free survival (DFS). The model integrated clinicopathologic data and multiparametric magnetic resonance imaging (MRI) images including diffusion kurtosis imaging (DKI), without performing tumor segmentation. The receiver operating characteristic (ROC) curve and Harrell’s concordance index (C-index) were used to evaluate the predictive performance of the proposed model. Results The deep learning model achieved good discrimination capability of recurrence/metastasis, with area under the curve (AUC) values of 0.885, 0.846, and 0.797 in the training, internal testing and external testing sets, respectively. Furthermore, the model successfully predicted DFS in the training set (C-index: 0.812), internal testing set (C-index: 0.794), and external testing set (C-index: 0.733), and classified patients into significantly distinct high- and low-risk groups (p < 0.05). Conclusions The multitask deep learning model, incorporating clinicopathologic data and multiparametric MRI, effectively predicted both recurrence/metastasis and survival for patients with rectal cancer. It has the potential to be an essential tool for risk stratification, and assist in making individualized treatment decisions. Clinical trial number Not applicable.https://doi.org/10.1186/s12911-025-03050-3Deep learningRectal cancerMultimodal dataPrognostic prediction |
| spellingShingle | Qiong Ma Runqi Meng Ruiting Li Ling Dai Fu Shen Jie Yuan Danqi Sun Manman Li Caixia Fu Rong Li Feng Feng Yonggang Li Tong Tong Yajia Gu Yiqun Sun Dinggang Shen Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study BMC Medical Informatics and Decision Making Deep learning Rectal cancer Multimodal data Prognostic prediction |
| title | Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study |
| title_full | Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study |
| title_fullStr | Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study |
| title_full_unstemmed | Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study |
| title_short | Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study |
| title_sort | multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer a multicenter retrospective study |
| topic | Deep learning Rectal cancer Multimodal data Prognostic prediction |
| url | https://doi.org/10.1186/s12911-025-03050-3 |
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