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Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation

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

Abstract

Objective

To evaluate machine learning–based classifiers in detecting clinically significant prostate cancer (PCa) with Prostate Imaging Reporting and Data System (PI-RADS) score 3 lesions.

Methods

We retrospectively enrolled 346 patients with PI-RADS 3 lesions at two institutions. All patients underwent prostate multiparameter MRI (mpMRI) and transperineal MRI-ultrasonography (MRI-US)-targeted biopsy. We collected data on age, pre-biopsy serum prostate-specific antigen (PSA) level, prostate volume (PV), PSA density (PSAD), the location of suspicious PI-RADS 3 lesions, and histopathology results. Four machine learning–based classifiers—logistic regression, support vector machine, eXtreme Gradient Boosting (XGBoost), and random forest—were trained using datasets from Nanjing Drum Tower Hospital. External validation was carried out using datasets from Molinette Hospital.

Results

Among 287 PI-RADS 3 patients, prostate cancer was proven pathologically in 59 (20.6%), and 228 (79.4%) had benign lesions. For 380 PI-RADS 3 lesions, 81 (21.3%) were proven to be PCa and 299 (78.7%) benign. Among four classifiers, the random forest classifier had the best performance in both patient-based and lesion-based datasets, with overall accuracy of 0.713 and 0.860, sensitivity of 0.857 and 0.613, and area under curve (AUC) of 0.771 and 0.832, respectively. In external validation, our best classifiers had an AUC of 0.688 with the best sensitivity (0.870) and specificity (0.500) in the 59 PI-RADS 3 patients in Molinette Hospital dataset.

Conclusions

The machine learning–based random forest classifier provided a reliable probability if a PI-RADS 3 patient was benign.

Key Points

• Machine learning–based classifiers could combine the clinical characteristics with accessible information on image report of PI-RADS 3 patient to generate a probability of malignancy.

• This probability could assist surgeons to make diagnostic decisions with more confidence and higher efficiency.

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Abbreviations

ADC:

Apparent diffusion coefficient

AI:

Artificial intelligence

AP:

Anterior-posterior

AUC:

Area under curve

CDR:

Cancer detection rate

DWI:

Diffusion-weighted imaging

FH:

Foot-head

mpMRI:

Multiparameter MRI

MRI-US:

MRI-ultrasonography

PCa:

Prostate cancer

PI-RADS:

Prostate Imaging Reporting and Data System

PSA:

Prostate-specific antigen

PSAD:

Prostate-specific antigen density

PV:

Prostate volume

RL:

Right-left

ROC:

Receiver operating characteristic

SVM:

Support vector machine

T2W:

T2-weighted

XGBoost:

eXtreme Gradient Boosting

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Funding

This work was supported by grants from the National Natural Science Foundation of China (81602221) and the National Natural Science Foundation of Jiangsu Province (BK20160117).

Author information

Author notes

  1. Yansheng Kan, Qing Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Institute of Urology, Nanjing University, No. 321 Zhongshan Rd, Nanjing, 210008, Jiangsu Province, China

    Yansheng Kan, Qing Zhang, Jiange Hao, Wei Wang, Junlong Zhuang, Jie Gao, Haifeng Huang, Jing Liang, Xiaozhi Zhao & Hongqian Guo

  2. Department of Urology, Molinette Hospital, Città della Salute e della Scienza, University of Turin, Turin, Italy

    Giancarlo Marra, Giorgio Calleris, Marco Oderda & Paolo Gontero

Authors
  1. Yansheng Kan
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  2. Qing Zhang
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  3. Jiange Hao
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  4. Wei Wang
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  5. Junlong Zhuang
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  7. Haifeng Huang
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  9. Giancarlo Marra
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  12. Xiaozhi Zhao
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  14. Hongqian Guo
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Corresponding authors

Correspondence to Xiaozhi Zhao, Paolo Gontero or Hongqian Guo.

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Guarantor

The scientific guarantor of this publication is Hongqian Guo.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Ethical approval

Institutional review board approval was not required because patient data was not so detailed as to raise privacy concerns and the analysis was retrospective.

Informed consent

Written informed consent was waived by the institutional review board.

Methodology

• Retrospective

• Diagnostic study

• Multicenter study

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Appendix

Appendix

XGBoost is based on gradient tree boosting, an algorithm with which new models are created that predict the residuals of prior models and are then added together for the final prediction [17].

In random forest, each tree was developed from a bootstrap sample of the training dataset, and each node was the part that was the best among a haphazard-chosen subset of features. The class predictions created by each tree within the forest were amassed, and the ultimate prediction was based on the lion’s share vote [18, 19].

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Kan, Y., Zhang, Q., Hao, J. et al. Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation. Eur Radiol 30, 6274–6284 (2020). https://doi.org/10.1007/s00330-020-06958-8

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  • DOI: https://doi.org/10.1007/s00330-020-06958-8

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