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Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds

  • Oncology Imaging
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Abstract

Background

PET-based radiomics features could predict the biological characteristics of primary prostate cancer (PCa). However, the optimal thresholds to predict the biological characteristics of PCa are unknown. This study aimed to compare the predictive power of 18F-PSMA-1007 PET radiomics features at different thresholds for predicting multiple biological characteristics.

Methods

One hundred and seventy-three PCa patients with complete preoperative 18F-PSMA-1007 PET examination and clinical data before surgery were collected. The prostate lesions' volumes of interest were semi-automatically sketched with thresholds of 30%, 40%, 50%, and 60% maximum standardized uptake value (SUVmax). The radiomics features were respectively extracted. The prediction models of Gleason score (GS), extracapsular extension (ECE), and vascular invasion (VI) were established using the support vector machine. The performance of models from different thresholding regions was assessed using receiver operating characteristic curve and confusion matrix-derived indexes.

Results

For predicting GS, the 50% SUVmax model showed the best predictive performance in training (AUC, 0.82 [95%CI 0.74–0.88]) and testing cohorts (AUC, 0.80 [95%CI 0.66–0.90]). For predicting ECE, the 40% SUVmax model exhibit the best predictive performance (AUC, 0.77 [95%CI 0.68–0.84] and 0.77 [95%CI 0.63–0.88]). As for VI, the 50% SUVmax model had the best predictive performance (AUC, 0.74 [95%CI 0.65–0.82] and 0.74 [95%CI 0.56–0.82]).

Conclusion

The 18F-1007-PSMA PET-based radiomics features at 40–50% SUVmax showed the best predictive performance for multiple PCa biological characteristics evaluation. Compared to the single PSA model, radiomics features may provide additional benefits in predicting the biological characteristics of PCa.

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Funding

This study was supported by the Wenzhou Major Program of Science and Technology Innovation (Grant No. ZY2020012), the Health Foundation for Creative Talents in Zhejiang Province, China (Grant No. 2016), the Project Foundation for the College Young and Middle-aged Academic Leader of Zhejiang Province, China (Grant No. 2017), and Basic Research Project of Wenzhou (Grant No. Y2020164).

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  1. Kun Tang and Yunjun Yang have contributed equally to this work.

Authors and Affiliations

  1. The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Fei Yao, Shuying Bian & Kehua Pan

  2. The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Dongqin Zhu, Kun Tang & Yunjun Yang

  3. The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Yaping Yuan & Zhifang Pan

  4. Renji College, Wenzhou Medical University, Wenzhou, 325000, China

    Xianghao Feng

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SB, DZ, YY, KP, ZP, XF and KT. The first draft of the manuscript was written by FY and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kun Tang or Yunjun Yang.

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Yao, F., Bian, S., Zhu, D. et al. Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds. Radiol med 127, 1170–1178 (2022). https://doi.org/10.1007/s11547-022-01541-1

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