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European Journal of Nuclear Medicine and Molecular Imaging

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01-04-2022 | Prostate Cancer | Original Article

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on ⁶⁸ Ga-PSMA-11 PET/CT in patients with primary prostate cancer

Authors: Zhilong Yi, Siqi Hu, Xiaofeng Lin, Qiong Zou, MinHong Zou, Zhanlei Zhang, Lei Xu, Ningyi Jiang, Yong Zhang

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 5/2022

Abstract

Purpose

⁶⁸ Ga-PSMA PET/CT has high specificity and sensitivity for the detection of both intraprostatic tumor focal lesions and metastasis. However, approximately 10% of primary prostate cancer are invisible on PSMA-PET (exhibit no or minimal uptake). In this work, we investigated whether machine learning-based radiomics models derived from PSMA-PET images could predict invisible intraprostatic lesions on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer.

Methods

In this retrospective study, patients with or without prostate cancer who underwent ⁶⁸ Ga-PSMA PET/CT and presented negative on PSMA-PET image at either of two different institutions were included: institution 1 (between 2017 and 2020) for the training set and institution 2 (between 2019 and 2020) for the external test set. Three random forest (RF) models were built using selected features extracted from standard PET images, delayed PET images, and both standard and delayed PET images. Then, subsequent tenfold cross-validation was performed. In the test phase, the three RF models and PSA density (PSAD, cut-off value: 0.15 ng/ml/ml) were tested with the external test set. The area under the receiver operating characteristic curve (AUC) was calculated for the models and PSAD. The AUCs of the radiomics model and PSAD were compared.

Results

A total of 64 patients (39 with prostate cancer and 25 with benign prostate disease) were in the training set, and 36 (21 with prostate cancer and 15 with benign prostate disease) were in the test set. The average AUCs of the three RF models from tenfold cross-validation were 0.87 (95% CI: 0.72, 1.00), 0.86 (95% CI: 0.63, 1.00), and 0.91 (95% CI: 0.69, 1.00), respectively. In the test set, the AUCs of the three trained RF models and PSAD were 0.903 (95% CI: 0.830, 0.975), 0.856 (95% CI: 0.748, 0.964), 0.925 (95% CI:0.838, 1.00), and 0.662 (95% CI: 0.510, 0.813). The AUCs of the three radiomics models were higher than that of PSAD (0.903, 0.856, and 0.925 vs. 0.662, respectively; P = .007, P = .045, and P = .005, respectively).

Conclusion

Random forest models developed by ⁶⁸ Ga-PSMA-11 PET-based radiomics features were proven useful for accurate prediction of invisible intraprostatic lesion on ⁶⁸ Ga-PSMA-11 PET in patients with primary prostate cancer and showed better diagnostic performance compared with PSAD.

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Title: Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer
Authors: Zhilong Yi
Siqi Hu
Xiaofeng Lin
Qiong Zou
MinHong Zou
Zhanlei Zhang
Lei Xu
Ningyi Jiang
Yong Zhang
Publication date: 01-04-2022
Publisher: Springer Berlin Heidelberg
Keywords: Prostate Cancer
Prostate Cancer
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 5/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05631-6

2024 ASCO Annual Meeting

Springer Medicine

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on ⁶⁸ Ga-PSMA-11 PET/CT in patients with primary prostate cancer

Abstract

Purpose

Methods

Results

Conclusion

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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