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• Clinical Article •
MRI radiomics models in rectal cancer to predict pathological complete response of nCRT: Evaluation of different approaches
QIN Siyuan  LU Siyi  WANG Qizheng  ZHANG Enlong  WANG Yuxia  PENG Ran  WANG Hao  LANG Ning 

Cite this article as: Qin SY, Lu SY, Wang QZ, et al. MRI radiomics models in rectal cancer to predict pathological complete response of nCRT: Evaluation of different approaches[J]. Chin J Magn Reson Imaging, 2022, 13(11): 82-87, 114. DOI:10.12015/issn.1674-8034.2022.11.015.


[Abstract] Objective To explore the value of different pre-treatment MRI radiomics models in predicting the pathological complete response (pCR) of neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC).Materials and Methods Seventy-six cases of patients diagnosed with LARC who underwent radical resection after nCRT in Peking University Third Hospital from January 2013 to December 2020 were retrospectively collected. According to the postoperative pathological results, they were divided into pCR group (n=38) and non-pCR group (n=38). The volume of interest (VOI) of lesion, rectal segment and of mesangial fat were segmented based on pre-treatment high-resolution T2WI sequence, and the radiomics features were extracted and screened. Classifiers including logistics regression (LR), quadratic discriminant analysis (QDA), support vector machine (SVM) were used to establish the radiomics models of lesions, rectal segments, mesangial fat, combine1 (lesions+mesangial fat), combine2 (rectal segments+mesangial fat), were used to evaluate the performance of different models and select the best model by using the receiver operating characteristic (ROC) curve. The 5-fold cross-validation was used for model testing, training, and selection.Results There were 6, 7, 7, 8, and 7 features used to establish the models of lesion, rectal segment, mesangial fat, combine1, and combine2, respectively. Among the 15 models, LR model based on lesion+mesangial fat features had the best performance. Its area under the curve (AUC), F1 score, sensitivity, specificity, accuracy and 95% confidence interval (CI) of the above indicators were 0.857 (0.647-1.000), 81.2% (59.5%-96.0%), 78.2% (36.7%-95.5%), 86.4% (47.0%-98.7%) and 82.3% (66.3%-95.7%), respectively.Conclusions Different pre-treatment MRI radiomics models can predict pCR after nCRT in LARC patients noninvasively, and the LR model based on lesion+mesangial fat featuresperforms the best.
[Keywords] locally advanced rectal cancer；magnetic resonance imaging；radiomics；neoadjuvant chemoradiotherapy；pathological complete response

QIN Siyuan1   LU Siyi2   WANG Qizheng1   ZHANG Enlong3   WANG Yuxia4   PENG Ran4   WANG Hao4   LANG Ning1*  

1 Department of Radiology, Peking University Third Hospital, Beijing 100191, China

2 Department of General Surgery, Peking University Third Hospital, Beijing 100191, China

3 Department of Radiology, Peking University International Hospital, Beijing 102206, China

4 Department of Radiotherapy, Cancer Center, Peking University Third Hospital, Beijing 100191, China

Lang N, E-mail: 13501241339@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81971578).
Received  2022-07-06
Accepted  2022-11-07
DOI: 10.12015/issn.1674-8034.2022.11.015
Cite this article as: Qin SY, Lu SY, Wang QZ, et al. MRI radiomics models in rectal cancer to predict pathological complete response of nCRT: Evaluation of different approaches[J]. Chin J Magn Reson Imaging, 2022, 13(11): 82-87, 114. DOI:10.12015/issn.1674-8034.2022.11.015.

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