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Review
Progress of MRI in predicting of the tumor response after neoadjuvant chemoradiotherapy for rectal cancer
LIU Dan  ZHANG Shengchao 

Cite this article as: Liu D, Zhang SC. Progress of MRI in predicting of the tumor response after neoadjuvant chemoradiotherapy for rectal cancer[J]. Chin J Magn Reson Imaging, 2022, 13(9): 163-166. DOI:10.12015/issn.1674-8034.2022.09.039.


[Abstract] Because patients with locally advanced rectal cancer cannot directly remove the lesions, after neoadjuvant chemoradiotherapy (nCRT), some people have a more sensitive response, and a complete tumor response will occur. Therefore, local resection or "watch and wait" approach is expected to replace surgical resection for such patients, thereby preserving the patient's anus and avoiding unnecessary surgical complications. Therefore, a noninvasive and reliable evaluation method is needed to determine tumor response after nCRT. MRI plays a crucial role in the initial staging of rectal cancer and in reassessing tumor response to nCRT. At present, the evaluation methods mainly include conventional MRI, functional MRI and MRI based on artificial intelligence prediction model. This paper comprehensively elaborated the research progress of the above three evaluation methods in predicting tumor response after neoadjuvant therapy for locally advanced rectal cancer.
[Keywords] rectal cancer;neoadjuvant chemoradiotherapy;tumor response;pathological complete response;magnetic resonance imaging;functional magnetic resonance imaging;artificial intelligence prediction model;review

LIU Dan1   ZHANG Shengchao2*  

1 Shanxi Medical University, Taiyuan 030001, China

2 Department of MRI, Taiyuan Second People's Hospital, Taiyuan 030002, China

*Zhang SC, E-mail: tyzsc163@qq.com

Conflicts of interest   None.

Received  2022-05-05
Accepted  2022-08-26
DOI: 10.12015/issn.1674-8034.2022.09.039
Cite this article as: Liu D, Zhang SC. Progress of MRI in predicting of the tumor response after neoadjuvant chemoradiotherapy for rectal cancer[J]. Chin J Magn Reson Imaging, 2022, 13(9): 163-166.DOI:10.12015/issn.1674-8034.2022.09.039

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