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Chen Y, Lu T, Zhang Y, Li H, Xu J, Li M. Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases. Abdom Radiol (NY) 2024; 49:2585-2594. [PMID: 39034308 DOI: 10.1007/s00261-024-04492-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/27/2024] [Accepted: 07/06/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM). METHODS Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression. RESULTS Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001). CONCLUSION The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.
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Affiliation(s)
- Yazheng Chen
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Tao Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Yongchang Zhang
- Department of Radiology, Chengdu Seventh People's Hospital, Chengdu, 610213, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Hangzhou Deepwise & League of PHD Technology Co., Ltd, Hangzhou, China
| | - Mou Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China.
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Li S, Li Z, Huang X, Zhang P, Deng J, Liu X, Xue C, Zhang W, Zhou J. CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3494-3506. [PMID: 35895118 DOI: 10.1007/s00261-022-03616-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 02/07/2023]
Abstract
The histopathological growth patterns (HGPs) of liver metastases (LMs) are independently associated with the long-term prognosis of the primary tumor, with different HGPs predicting different patient outcomes and clinical treatment decisions. Non-invasive imaging biomarkers for stratification of HGPs are beneficial for treatment monitoring, evaluation of efficacy, and prognosis prediction of LMs. This review describes the state of research regarding computed tomography (CT), magnetic resonance imaging (MRI), and radiomics imaging biomarkers for LM-HGPs; discusses the advantages of CT, MRI, and radiomics for classification of LM-HGPs; and provides a reference for the stratification of LM-HGPs. Finally, the difficulties and deficiencies of CT, MRI, and radiomics in LM-HGP research are summarized along with the proposed directions for future research.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Zhengxiao Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoyu Huang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.,Second Clinical School, Lanzhou University, Lanzhou, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China. .,Second Clinical School, Lanzhou University, Lanzhou, China. .,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China. .,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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