Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer
Abstract Objectives To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) in patients with Rectal cancer (RC). Method...
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BMC
2025-05-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01692-3 |
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| author | Jing Sun Pu-Yeh Wu Fangmin Shen Xingfa Chen Jieqiong She Mingcong Luo Feifei Feng Dechun Zheng |
| author_facet | Jing Sun Pu-Yeh Wu Fangmin Shen Xingfa Chen Jieqiong She Mingcong Luo Feifei Feng Dechun Zheng |
| author_sort | Jing Sun |
| collection | DOAJ |
| description | Abstract Objectives To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) in patients with Rectal cancer (RC). Methods In this retrospective study, 176 and 31 patients with RC who underwent multiparametric MRI from two centers were enrolled in the primary and external validation cohorts, respectively. Clinical factors, including sex, primary tumor site, CEA level, and CA199 level were assessed. A clinical feature (CF) model was first developed by multivariate logistic regression, then two residual network DL models were constructed based on multiparametric MRI of primary cancer with or without CF incorporation. Finally, the SLM prediction models were validated by 5-fold cross-validation and external validation. The performance of the models was evaluated by decision curve analysis (DCA) and receiver operating characteristic (ROC) analysis. Results Among three SLM prediction models, the Combined DL model integrating primary tumor MRI and basic clinical data achieved the best performance (AUC = 0.887 in primary study cohort; AUC = 0.876 in the external validation cohort). In the primary study cohort, the CF model, MRI DL model, and Combined DL model achieved AUCs of 0.816 (95% CI: 0.750, 0.881), 0.788 (95% CI: 0.720, 0.857), and 0.887 (95% CI: 0.834, 0.940) respectively. In the external validation cohort, the CF model, DL model without CF, and DL model with CF achieved AUCs of 0.824 (95% CI: 0.664, 0.984), 0.662 (95% CI: 0.461, 0.863), and 0.876 (95% CI: 0.728, 1.000), respectively. Conclusion The combined DL model demonstrates promising potential to predict SLM in patients with RC, thereby making individualized imaging test strategies. Clinical relevance statement Accurate synchronous liver metastasis (SLM) risk stratification is important for treatment planning and prognosis improvement. The proposed DL signature may be employed to better understand an individual patient’s SLM risk, aiding in treatment planning and selection of further imaging examinations to personalize clinical decisions. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-9acd4d1ed332443f812081ef9ca0a9c8 |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-9acd4d1ed332443f812081ef9ca0a9c82025-08-20T02:34:19ZengBMCBMC Medical Imaging1471-23422025-05-0125111010.1186/s12880-025-01692-3Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancerJing Sun0Pu-Yeh Wu1Fangmin Shen2Xingfa Chen3Jieqiong She4Mingcong Luo5Feifei Feng6Dechun Zheng7Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalGE HealthcareDepartment of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiation Oncology, Fujian Medical University Union HospitalDepartment of Radiology, Fujian Medical University Union HospitalDepartment of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalAbstract Objectives To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) in patients with Rectal cancer (RC). Methods In this retrospective study, 176 and 31 patients with RC who underwent multiparametric MRI from two centers were enrolled in the primary and external validation cohorts, respectively. Clinical factors, including sex, primary tumor site, CEA level, and CA199 level were assessed. A clinical feature (CF) model was first developed by multivariate logistic regression, then two residual network DL models were constructed based on multiparametric MRI of primary cancer with or without CF incorporation. Finally, the SLM prediction models were validated by 5-fold cross-validation and external validation. The performance of the models was evaluated by decision curve analysis (DCA) and receiver operating characteristic (ROC) analysis. Results Among three SLM prediction models, the Combined DL model integrating primary tumor MRI and basic clinical data achieved the best performance (AUC = 0.887 in primary study cohort; AUC = 0.876 in the external validation cohort). In the primary study cohort, the CF model, MRI DL model, and Combined DL model achieved AUCs of 0.816 (95% CI: 0.750, 0.881), 0.788 (95% CI: 0.720, 0.857), and 0.887 (95% CI: 0.834, 0.940) respectively. In the external validation cohort, the CF model, DL model without CF, and DL model with CF achieved AUCs of 0.824 (95% CI: 0.664, 0.984), 0.662 (95% CI: 0.461, 0.863), and 0.876 (95% CI: 0.728, 1.000), respectively. Conclusion The combined DL model demonstrates promising potential to predict SLM in patients with RC, thereby making individualized imaging test strategies. Clinical relevance statement Accurate synchronous liver metastasis (SLM) risk stratification is important for treatment planning and prognosis improvement. The proposed DL signature may be employed to better understand an individual patient’s SLM risk, aiding in treatment planning and selection of further imaging examinations to personalize clinical decisions. Clinical trial number Not applicable.https://doi.org/10.1186/s12880-025-01692-3Diagnostic modelRectal cancerDeep learning radiomicsMagnetic resonance imagingSynchronous liver metastases |
| spellingShingle | Jing Sun Pu-Yeh Wu Fangmin Shen Xingfa Chen Jieqiong She Mingcong Luo Feifei Feng Dechun Zheng Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer BMC Medical Imaging Diagnostic model Rectal cancer Deep learning radiomics Magnetic resonance imaging Synchronous liver metastases |
| title | Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| title_full | Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| title_fullStr | Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| title_full_unstemmed | Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| title_short | Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| title_sort | deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer |
| topic | Diagnostic model Rectal cancer Deep learning radiomics Magnetic resonance imaging Synchronous liver metastases |
| url | https://doi.org/10.1186/s12880-025-01692-3 |
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