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The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis

Prostate Cancer and Prostatic Diseases volume 26, pages 602–613 (2023)Cite this article

Subjects

Abstract

Background

In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients.

Evidence acquisition

Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022.

Summary of evidence

The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models.

Results

Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819–0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram.

Conclusion

The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.

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Fig. 1
Fig. 2: Forest plots of results of risk of bias assessment and the sensitivity and specificity of meta-analysis for included models.
Fig. 3: Forest plots of C-index of radiomics-based MLs.
Fig. 4
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Fig. 8

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. These authors contributed equally: Hao Wang, Zhongyou Xia.

Authors and Affiliations

  1. Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China

    Hao Wang, Zhongyou Xia, Yulai Xu, Jing Sun & Ji Wu

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Conceptualization: ZX; Methodology: HW; Formal analysis and investigation: JS; Writing original draft preparation: HW; Writing review and editing: HW; Supervision: JW, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ji Wu.

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Wang, H., Xia, Z., Xu, Y. et al. The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis 26, 602–613 (2023). https://doi.org/10.1038/s41391-023-00704-z

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