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Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment

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Abstract

Purpose

To develop and test radiomics models based on manually corrected or automatically gained masks on ADC maps for pelvic lymph node metastasis (PLNM) prediction in patients with prostate cancer (PCa).

Methods

A primary cohort of 474 patients with PCa who underwent prostate mpMRI were retrospectively enrolled for PLNM prediction between January 2017 and January 2020. They were then randomly split into training/validation (n = 332) and test (n = 142) groups for model development and internal testing. Four radiomics models were developed using four masks (manually corrected/automatic prostate gland and PCa lesion segmentation) based on the ADC maps using the primary cohort. Another cohort of 128 patients who underwent radical prostatectomy (RP) with extended pelvic lymph node dissection (ePLND) for PCa was used as the testing cohort between February 2020 and October 2021. The performance of the models was evaluated in terms of discrimination and clinical usefulness using the area under the curve (AUC) and decision curve analysis (DCA). The optimal radiomics model was further compared with Memorial Sloan Kettering Cancer Center (MSKCC) and Briganti 2017 nomograms, and PI-RADS assessment.

Results

17 (13.28%) Patients with PLNM were included in the testing cohort. The radiomics model based on the mask of automatically segmented prostate obtained the highest AUC among the four radiomics models (0.73 vs. 0.63 vs. 0.70 vs. 0.56). Briganti 2017, MSKCC nomograms, and PI-RADS assessment-yielded AUCs of 0.69, 0.71, and 0.70, respectively, and no significant differences were found compared with the optimal radiomics model (P = 0.605–0.955).

Conclusion

The radiomics model based on the mask of automatically segmented prostate offers a non-invasive method to predict PLNM for patients with PCa. It shows comparable accuracy to the current MKSCC and Briganti nomograms.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The codes used for the model development are available from the corresponding author on reasonable request.

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Funding

This study was supported by Capital’s Funds for Health Improvement and Research (2020-2-40710).

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Authors and Affiliations

  1. Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing, 100034, China

    Xiang Liu, Zhaonan Sun, Xiaodong Zhang & Xiaoying Wang

  2. Beijing Smart Tree Medical Technology Co. Ltd., No. 24, Huangsi Street, Xicheng District, Beijing, 100011, China

    Xiangpeng Wang & Yaofeng Zhang

Authors
  1. Xiang Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XL and ZS. XL performed manual annotation under the supervision of XW. XW, YZ, and XZ performed data interpretation and statistical analysis. The first draft of the manuscript was written by XL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaoying Wang.

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Conflict of interest

Xiangpeng Wang and Yaofeng Zhang are from a medical technical corporation provided technical support for model development. The authors declare that they have no competing interests.

Ethical approval

This study was approved by the Local Institutional Ethical Committee.

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The need for written informed consent was waived.

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Liu, X., Wang, X., Zhang, Y. et al. Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment. Abdom Radiol 47, 3327–3337 (2022). https://doi.org/10.1007/s00261-022-03583-5

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