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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

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.

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Abbreviations

ADC:

Apparent diffusion coefficient maps

AUC:

Area under the curve

csPCa:

Clinically significant prostate cancer

DRE:

Digital rectum exam results

ePLND:

Extended pelvic lymph node dissection

GLCM:

Gray-Level Cooccurrence Matrix

GLDM:

Gray Level Dependence Matrix

GLRLM:

Gray-Level Run Length Matrix

GLSZM:

Gray-level Size Zone Matrix

IRM:

Integrative radiomics model

LNI:

Lymph node invasion

mpMRI:

Multiparametric magnetic resonance imaging

NGTDM:

Neighboring Gray Tone Difference Matrix

NPV:

Negative predictive value

PCa:

Prostate cancer

PI-RADS:

Prostate Imaging Reporting and Data System

PPV:

Positive predictive value

PSA:

Prostate specific antigen

PSAD:

Prostate specific antigen density

ROC:

Receiver operating characteristic

SFFS:

Sequential Floating Forwarding Selection

T2WI:

T2-weighted images

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Funding

This work was supported by the National Institutes of Health (NIH) R01-CA248506 and funds from the Integrated Diagnostics Program, Department of Radiological Sciences &; Pathology, David Geffen School of Medicine at UCLA.

Author information

Authors and Affiliations

  1. Radiological Sciences, University of California – Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA

    Haoxin Zheng, Qi Miao, Yongkai Liu, Sohrab Afshari Mirak, Melina Hosseiny, Steven S. Raman & Kyunghyun Sung

  2. Computer Science, University of California – Los Angeles, Los Angeles, CA, 90095, USA

    Haoxin Zheng & Fabien Scalzo

  3. Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, 110001, Liaoning Province, China

    Qi Miao

  4. Seaver College, Pepperdine University, Malibu, CA, 90263, USA

    Fabien Scalzo

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  1. Haoxin Zheng
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Corresponding author

Correspondence to Qi Miao.

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Guarantor

The scientific guarantor of this publication is Kyunghyun Sung.

Conflict of interest

The authors declare no competing interests.

Statistics and biometry

Haoxin Zheng, one of the authors, has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board

Ethics approval

The study was performed in compliance with the United States Health Insurance Portability and Accountability Act (HIPAA) of 1996 and was approved by the institutional review board (IRB) with a waiver of the requirement for informed consent.

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• performed at one institution

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Zheng, H., Miao, Q., Liu, Y. et al. Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer. Eur Radiol 32, 5688–5699 (2022). https://doi.org/10.1007/s00330-022-08625-6

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  • DOI: https://doi.org/10.1007/s00330-022-08625-6

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