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European Radiology
Published in: European Radiology 8/2022

03-03-2022 | Prostate Cancer | Imaging Informatics and Artificial Intelligence

Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer

Authors: Haoxin Zheng, Qi Miao, Yongkai Liu, Sohrab Afshari Mirak, Melina Hosseiny, Fabien Scalzo, Steven S. Raman, Kyunghyun Sung

Published in: European Radiology | Issue 8/2022

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Abstract

Objective

To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach.

Methods

An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model’s performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher’s exact test.

Results

Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846–0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05).

Conclusion

The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND.

Key Points

• The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features.
• With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.
Appendix
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Metadata
Title
Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer
Authors
Haoxin Zheng
Qi Miao
Yongkai Liu
Sohrab Afshari Mirak
Melina Hosseiny
Fabien Scalzo
Steven S. Raman
Kyunghyun Sung
Publication date
03-03-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08625-6

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