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Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy

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Abstract

Purpose

In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy.

Methods

Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872–0.975) and 0.898 (0.819–0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS).

Conclusions

The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS.

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Abbreviations

LARC:

Locally advanced rectal cancer

TME:

Total mesorectal excision

pCR:

Pathological complete response

TRG:

Tumor response grading

DWI:

Diffusion-weighted imaging

CE-T1WI:

Contrast-enhanced T1-weighted imaging

VOI:

Volume of interest

mRMR:

Minimum redundancy maximum relevance feature selection

LASSO:

Least absolute shrinkage and selection operator

CEA:

Carcinoembryonic antigen

CA199:

Carbohydrate antigen 199

CRM:

Circumferential resection margin

EMVI:

Extramural vascular invasion

Rad-score:

Radiomics score

PFS:

Progression free survival

CI:

Confidence interval

ICC:

Intraclass correlation coefficient

TNM:

Tumor-node-metastasis

LR:

Logistic regression

RF:

Random forest

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Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China

    Xuejun Liu, Bin Hu, Yuanxiang Gao, Jingjing Chen & Jie Li

  2. Department of Ultrasound, Qingdao Women and Children Hospital, Qingdao, Shandong, China

    Jia Wang

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Wang, J., Liu, X., Hu, B. et al. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy. Abdom Radiol 46, 1805–1815 (2021). https://doi.org/10.1007/s00261-020-02846-3

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