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Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on 68 Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features

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Abstract

Purpose

To assess the utility of convoluted neural network (CNN) in differentiating clinically significant and insignificant prostate cancer in patients with 68 Ga PSMA PET/CT-targeted prostate biopsy-proven prostate cancer.

Methods

In this retrospective study, 142 patients with clinical suspicion of prostate cancer were evaluated who underwent 68 Ga-PSMA PET/CT imaging followed by 68 Ga-PSMA PET/CT-targeted prostate biopsy from the PSMA-avid prostate lesion. Twenty patients with no PSMA-avid lesions were excluded. Local Image Features Extraction (LifeX) software was used to extract radiomic features (RF) from delayed 68 Ga-PSMA PET/CT images of 122 patients. LifeX failed to extract radiomic features in 24 patients, and the remaining 98 were evaluated. RFs were fed to an in-built CNN of the software for computation and results were achieved. Patients with Gleason Score ≥ 7 on histopathology were labeled clinically significant prostate cancer (csPCa). The diagnostic values of radiomic features were evaluated.

Results

The csPCa was revealed in 69/98 (70.4%) patients, and insignificant PCa was noticed in 29/98 (29.6%) patients. The software extracted 124 RF from the delayed 68 Ga-PSMA PET/CT images. The accuracy of the CNN was 80.7% to differentiate clinically significant and clinically insignificant prostate cancer, with an error percentage (E %) of 19.3%. The sensitivity, specificity, positive predictive, and negative predictive values were 90.3%, 57.7%, 83.6%, and 71.4%, respectively, to detect csPCa.

Conclusion

CNN is a feasible pre-biopsy screening tool for identifying clinically significant prostate cancer and can be used as an adjunct in the initial diagnosis and early treatment planning.

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Data Availability

Data generated and analyzed can be availed from the corresponding author on a reasonable request.

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Author notes

  1. Rajender Kumar and Arivan Ramachandran contributed equally as first authors.

Authors and Affiliations

  1. Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Rajender Kumar, Arivan Ramachandran, Bhagwant Rai Mittal & Harmandeep Singh

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  1. Rajender Kumar
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  2. Arivan Ramachandran
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Contributions

All authors have made a significant contribution in formulating, reviewing, and editing the manuscript.

Corresponding author

Correspondence to Bhagwant Rai Mittal.

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Competing Interests

Rajender Kumar, Arivan Ramachandran, Bhagwant Rai Mittal and Harmandeep Singh declare no competing interests.

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This study was performed in accordance with the ethical standards as laid down in the 2013 Declaration of Helsinki and its later amendments or comparable ethical standards. The institutional review board waived the requirement for written consent.

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Informed consent was obtained from the participant for the study mentioned as a part of the institutional protocol.

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Kumar, R., Ramachandran, A., Mittal, B.R. et al. Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on 68 Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features. Nucl Med Mol Imaging 58, 62–68 (2024). https://doi.org/10.1007/s13139-023-00832-3

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