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Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer

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Abstract

Purpose

To establish and validate an integrated model incorporating multiregional magnetic resonance imaging (MRI) radiomics features and clinical factors to predict tumor deposits (TDs) preoperatively in resectable rectal cancer (RC).

Methods

This study retrospectively included 148 resectable RC patients [TDs+ (n = 45); TDs− (n = 103)] from August 2016 to August 2022, who were divided randomly into a testing cohort (n = 45) and a training cohort (n = 103). Radiomics features were extracted from the volume of interest on T2-weighted images (T2WI) and diffusion-weighted images (DWI) from pretreatment MRI. Model construction was performed after feature selection. Finally, five classification models were developed by support vector machine (SVM) algorithm to predict TDs in resectable RC using the selected clinical factor, single-regional radiomics features (extracted from primary tumor), and multiregional radiomics features (extracted from the primary tumor and mesorectal fat). Receiver-operating characteristic (ROC) curve analysis was employed to assess the discrimination performance of the five models. The AUCs of five models were compared by DeLon’s test.

Results

The training and testing cohorts included 31 (30.1%) and 14 (31.1%) patients with TDs, respectively. The AUCs of multiregional radiomics, single-regional radiomics, and the clinical models for predicting TDs were 0.839, 0.765, and 0.793, respectively. An integrated model incorporating multiregional radiomics features and clinical factors showed good predictive performance for predicting TDs in resectable RC (AUC, 0.931; 95% CI, 0.841–0.988), which demonstrated superiority over clinical model (P = 0.016), the single-regional radiomics model (P = 0.042), and the multiregional radiomics model (P = 0.025).

Conclusion

An integrated model combining multiregional MRI radiomic features and clinical factors can improve prediction performance for TDs and guide clinicians in implementing treatment plans individually for resectable RC patients.

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Acknowledgments

We thank Jibo Jia (Department of Radiology, the Third People’s Hospital of Kunshan, No. 615 Zizhu road, Kunshan, Jiangsu Province 215003, China) for providing some suggestions on the data processing software used in our research.

Funding

This work was supported by Gusu health talent project of Suzhou (GSWS2020003)

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China

    Feiwen Feng, Yuanqing Liu, Jiayi Bao, Rong Hong, Su Hu & Chunhong Hu

  2. Institute of Medical Imaging, Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China

    Su Hu & Chunhong Hu

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  1. Feiwen Feng
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  2. Yuanqing Liu
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Contributions

FWF and CHH participated in the conception/ design. YQL and JYB participated in the provision of study material or patients. JYB and RH participated in the collection and/or assembly of data. FWF and YQL participated in the data analysis and interpretation. FWF participated in the manuscript writing. SH and CHH participated in the manuscript revision. All authors contributed to final approval of manuscript.

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Correspondence to Su Hu or Chunhong Hu.

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Feng, F., Liu, Y., Bao, J. et al. Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer. Abdom Radiol 48, 3310–3321 (2023). https://doi.org/10.1007/s00261-023-04013-w

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