Comparative Study of Multiple Models Based on Baseline T2WI Images for Predicting Pathological Complete Remission of Progressive Rectal Cancer after Neo-adjuvant Chemoradiotherapy

Jingyu YANG; Ning XU; Yutao ZHANG; Fengchang HUANG; Yuanming JIANG; Liang YIN

doi:10.12259/j.issn.2095-610X.S20230512

Volume 44 Issue 5

May 2023

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Article Navigation > Journal of Kunming Medical University > 2023 > 44(5): 117-124

Jingyu YANG, Ning XU, Yutao ZHANG, Fengchang HUANG, Yuanming JIANG, Liang YIN. Comparative Study of Multiple Models Based on Baseline T2WI Images for Predicting Pathological Complete Remission of Progressive Rectal Cancer after Neo-adjuvant Chemoradiotherapy[J]. Journal of Kunming Medical University, 2023, 44(5): 117-124. doi: 10.12259/j.issn.2095-610X.S20230512

Citation:

Jingyu YANG, Ning XU, Yutao ZHANG, Fengchang HUANG, Yuanming JIANG, Liang YIN. Comparative Study of Multiple Models Based on Baseline T2WI Images for Predicting Pathological Complete Remission of Progressive Rectal Cancer after Neo-adjuvant Chemoradiotherapy[J]. Journal of Kunming Medical University, 2023, 44(5): 117-124. doi: 10.12259/j.issn.2095-610X.S20230512

Citation:

Jingyu YANG, Ning XU, Yutao ZHANG, Fengchang HUANG, Yuanming JIANG, Liang YIN. Comparative Study of Multiple Models Based on Baseline T2WI Images for Predicting Pathological Complete Remission of Progressive Rectal Cancer after Neo-adjuvant Chemoradiotherapy[J]. Journal of Kunming Medical University, 2023, 44(5): 117-124. doi: 10.12259/j.issn.2095-610X.S20230512

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Comparative Study of Multiple Models Based on Baseline T2WI Images for Predicting Pathological Complete Remission of Progressive Rectal Cancer after Neo-adjuvant Chemoradiotherapy

doi: 10.12259/j.issn.2095-610X.S20230512

1.
Dept. of Oncology
2.
Dept. of Radiology，The 1st Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650032，China

Received Date: 2023-02-16
Available Online: 2023-05-13
Publish Date: 2023-05-25

Abstract

Abstract

Objective To investigate the predictive effectiveness of different models and the efficacy of baseline T2WI combined with machine learning imaging and to predict the pathological complete remission after the neo-adjuvant chemoradiotherapy （nCRT） in patients with locally advanced rectal cancer （LARC）. Methods A retrospective analysis was conducted on the data of 131 patients with non metastatic advanced rectal cancer from January 2017 to December 2021. All patients underwent the pelvic MRI examination before and after the treatment, received standard nCRT treatment, and then underwent the total mesorectal resection （TME）. AK software （Analysis Kit, GE Healthcare） was used to manually draw the regions of interest （ROI） on the pre-treatment axial T2WI maps, and AK software also extracted the imaging omics features. The imaging omics data were used to build the prediction models by using the support vector machine （SVM）, random forest （RF）, and logistic regression （LR） methods after the the imaging omics features were feature-screened using a two-sample t-test + LASSO regression. The effectiveness of the model prediction was evaluated using the receiver operating characteristic curve （ROC）. Results 26 （19.8%） of the 131 patients had a pathologic complete response （pCR）. The AK software extracted 1308 imaging omics features in total, and after the screening, 12 features were selected for pCR prediction. The SVM model had an AUC, accuracy of 0.8810 and 81.48%, sensitivity and specificity of 90.48% and 50%. The RF model had an AUC, accuracy of 0.7579 and 81.48%, sensitivity and specificity of accuracy 95.24% and 33.33%. The LR model had an AUC, accuracy of 0.9206 and 92.59%, sensitivity and specificity of 95.24% and 83.33%. Conclusion The three machine learning models constructed have the considerable accuracy in predicting complete pathological remission after the concurrent radiotherapy and chemotherapy for locally advanced rectal cancer. Among them, the machine learning model established with the use of logistic regression （LR） method has the higher diagnostic efficiency than other machine learning models, and has the potential to be applied in the clinical practice.
- Locally advanced rectal cancer,
- Neo-adjuvant chemoradiotherapy,
- Radiomic,
- Prediction model

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