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Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer

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Abstract

Purpose

To investigate and validate the potential role of a radiomics signature in predicting the side-specific probability of extracapsular extension (ECE) of prostate cancer (PCa).

Procedures

The preoperative magnetic resonance imaging data of 238 prostatic samples from 119 enrolled PCa patients were retrospectively assessed. The samples with were randomized in a two-to-one ratio into training (n = 74) and validation (n = 45) datasets. The radiomics features were derived from T2-weighted images (T2WIs). The optimal radiomics features were identified from the least absolute shrinkage and selection operator (LASSO) logistic regression model and were used to construct a predictive radiomics signature via dimension reduction and selection approaches. The association between the radiomics signatures and pathological ECE status was explored. Receiver operating characteristic (ROC) analysis was used to assess the discriminatory ability of the signature. The calibration performance and clinical usefulness of the radiomics signature were subsequently assessed by calibration curve and decision curve analyses.

Results

The proposed radiomics signature that incorporated 17 selected radiomics features was significantly associated with pathological ECE outcomes (P < 0.001) in both the training and validation datasets. The constructed model displayed good discrimination, with areas under the curve (AUC) of 0.906 (95 % confidence interval (CI), 0.847, 0.948) and 0.821 (95 % CI, 0.726, 0.894) for the training and validation datasets, respectively, and had a good calibration performance. The clinical utility of this model was confirmed through decision curve analysis.

Conclusions

The radiomics signature based on T2WIs showed the potential to predict the side-specific probability of pathological ECE status and can facilitate the preoperative individualized predictions for PCa patients.

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Funding

This study is funded by the Interdisciplinary Clinical Research Project of Peking University First Hospital (grant number 2017CR21).

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Authors and Affiliations

  1. Department of Radiology, Peking University First Hospital, 8 Xishiku Street, Xicheng District, Beijing, 100034, China

    Shuai Ma, Huihui Xie, Huihui Wang, Jiejin Yang, Chao Han, Xiaoying Wang & Xiaodong Zhang

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  1. Shuai Ma
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Ma, S., Xie, H., Wang, H. et al. Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer. Mol Imaging Biol 22, 711–721 (2020). https://doi.org/10.1007/s11307-019-01405-7

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