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• Clinical Article •
Predicting the efficacy of neoadjuvant therapy for locally advanced rectal cancer based on 3.0 T MRI and comparing the effectiveness of multiple classifiers
HU Hongbo  ZHAO Sheng  JIANG Hao  JIANG Huijie  LIN Xue  ZHANG Ying 

Cite this article as: HU H B, ZHAO S, JIANG H, et al. Predicting the efficacy of neoadjuvant therapy for locally advanced rectal cancer based on 3.0 T MRI and comparing the effectiveness of multiple classifiers[J]. Chin J Magn Reson Imaging, 2023, 14(11): 77-83. DOI:10.12015/issn.1674-8034.2023.11.013.


[Abstract] Objective 3.0 T MRI data has clinical value in evaluating the efficacy of neoadjuvant therapy for locally advanced rectal cancer (LARC), but the comparison between multiple machine learning models has not been explored. We will compare the efficacy of four commonly used machine learning methods in evaluating the clinical value of neoadjuvant chemoradiotherapy (nCRT) for LARC.Materials and Methods A total of 160 LARC patients who were diagnosed and confirmed by pathological examination at the Second Affiliated Hospital of Harbin Medical University from September 2021 to January 2023, underwent nCRT. They were divided into a training set and a validation set in an 8∶2 ratio. Establish four classifier models: support vector machine (SVM), naive Bayes (NB), convolutional neural networks (CNN) and neural network (NN), and use DeLong test to compare the differences in receiver operating characteristic (ROC) curves. Evaluate and compare the diagnostic performance of four classifiers.Results There was no statistically significant difference in age and gender between the two groups of patients (P＞0.05). Nine features related to treatment efficacy grouping were obtained through least absolute shrinkage and selection operator (LASSO), and there were differences between pathological complete response (pCR) non-pathological complete response (non-pCR) groups, but the differences were not statistically significant (P＞0.05). The area under the ROC curve of SVM in the training set is 0.9150, which indicates the most significant evaluation of the efficacy of nCRT and chemotherapy.Conclusions Based on the texture features of high-resolution T2WI MRI, SVM, NB, NN, and CNN classifier models can be used to evaluate the effectiveness of colorectal cancer nCRT treatment. SVM classifier models have the best diagnostic performance, and imaging omics based on high-resolution T2WI can evaluate the effectiveness of nCRT treatment in LARC patients.
[Keywords] rectal cancer；magnetic resonance imaging；imaging omics；machine learning；support vector machine

HU Hongbo1   ZHAO Sheng1   JIANG Hao1   JIANG Huijie1*   LIN Xue1   ZHANG Ying2  

1 Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China

2 Department of Medical Genetics, Medicine Basic sciences, Harbin Medical University, Harbin 150086, China

Corresponding author: JIANG H J, E-mail: jhjemail@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 62171167).
Received  2023-05-08
Accepted  2023-08-09
DOI: 10.12015/issn.1674-8034.2023.11.013
Cite this article as: HU H B, ZHAO S, JIANG H, et al. Predicting the efficacy of neoadjuvant therapy for locally advanced rectal cancer based on 3.0 T MRI and comparing the effectiveness of multiple classifiers[J]. Chin J Magn Reson Imaging, 2023, 14(11): 77-83. DOI:10.12015/issn.1674-8034.2023.11.013.

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