External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study

Objectives The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers’ discriminative performance for pCR prediction. Methods This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. Results Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. Conclusions The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. Key Points • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:33

Enthalten in:

European radiology - 33(2022), 3 vom: 10. Nov., Seite 1906-1917

Sprache:

Englisch

Beteiligte Personen:

Wei, Qiurong [VerfasserIn]
Chen, Zeli [VerfasserIn]
Tang, Yehuan [VerfasserIn]
Chen, Weicui [VerfasserIn]
Zhong, Liming [VerfasserIn]
Mao, Liting [VerfasserIn]
Hu, Shaowei [VerfasserIn]
Wu, Yuankui [VerfasserIn]
Deng, Kan [VerfasserIn]
Yang, Wei [VerfasserIn]
Liu, Xian [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

Themen:

Machine learning
Magnetic resonance imaging
Neoadjuvant therapy
Rectal neoplasm

RVK:

RVK Klassifikation

Anmerkungen:

© The Author(s), under exclusive licence to European Society of Radiology 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

doi:

10.1007/s00330-022-09204-5

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2133979131

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