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The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the diagnostic accuracy of [18F]-DCFPyL PET/MRI radiomics for the prediction of pathological grade group in prostate cancer (PCa) in therapy-naïve patients.

Methods

Patients with confirmed or suspected PCa, who underwent [18F]-DCFPyL PET/MRI (n = 105), were included in this retrospective analysis of two prospective clinical trials. Radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines. Histopathology obtained from systematic and targeted biopsies of the PET/MRI-detected lesions was the reference standard. Histopathology patterns were dichotomized as ISUP GG 1–2 vs. ISUP GG ≥ 3 categories. Different single-modality models were defined for feature extraction, including PET- and MRI-derived radiomic features. The clinical model included age, PSA, and lesions’ PROMISE classification. Single models, as well as different combinations of them, were generated to calculate their performances. A cross-validation approach was used to evaluate the internal validity of the models.

Results

All radiomic models outperformed the clinical models. The best model for grade group prediction was the combination of PET + ADC + T2w radiomic features, showing sensitivity, specificity, accuracy, and AUC of 0.85, 0.83, 0.84, and 0.85, respectively. The MRI-derived (ADC + T2w) features showed sensitivity, specificity, accuracy, and AUC of 0.88, 0.78, 0.83, and 0.84, respectively. PET-derived features showed 0.83, 0.68, 0.76, and 0.79, respectively. The baseline clinical model showed 0.73, 0.44, 0.60, and 0.58, respectively. The addition of the clinical model to the best radiomic model did not improve the diagnostic performance. The performances of MRI and PET/MRI radiomic models as per the cross-validation scheme yielded an accuracy of 0.80 (AUC = 0.79), whereas clinical models presented an accuracy of 0.60 (AUC = 0.60).

Conclusion

The combined [18F]-DCFPyL PET/MRI radiomic model was the best-performing model and outperformed the clinical model for pathological grade group prediction, indicating a complementary value of the hybrid PET/MRI model for non-invasive risk stratification of PCa. Further prospective studies are required to confirm the reproducibility and clinical utility of this approach.

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Data availability

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

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Funding

Study supported by Toronto General Toronto Western Hospital Foundation (NCT03535831) and Mount Sinai Hospital-University Health Network Academic Medical Organization Innovation Fund 2017–2019 (NCT 03149861); Toronto, ON, Canada.

Author information

Author notes

  1. Adriano Basso Dias Seyed Ali Mirshahvalad contributed equally to this study.

Authors and Affiliations

  1. Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women’s College Hospital; University of Toronto, Toronto, ON, Canada

    Adriano Basso Dias, Seyed Ali Mirshahvalad, Claudia Ortega, Sangeet Ghai, Kartik Jhaveri, Ur Metser, Masoom Haider & Patrick Veit-Haibach

  2. Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Nathan Perlis

  3. Department of Radiation Oncology, Princess Margaret Cancer Center, University Health Network & University of Toronto, Toronto, ON, Canada

    Alejandro Berlin

  4. Laboratory Medicine Program, University Health Network, Toronto, ON, Canada

    Theodorus van der Kwast

  5. Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada

    Lisa Avery

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  1. Adriano Basso Dias
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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Adriano Basso Dias and Seyed Ali Mirshahvalad. Statistical analysis was performed by Lisa Avery. The first draft of the manuscript was written by Adriano Basso Dias, Seyed Ali Mirshahvalad, and Patrick Veit-Haibach and all authors commented on previous versions of the manuscript. Supervision was performed by Patrick Veit-Haibach. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Adriano Basso Dias.

Ethics declarations

Ethical approval

This was an ethics review board-approved, retrospective analysis of two prospective clinical trials (ClinicalTrials.gov Identifiers: NCT03535831 and NCT03149861).

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Competing interests

Alejandro Berlin: Participation on a DataSafety Monitoring Board or Advisory Board from Princess Margaret Cancer Center—University Health Network. Ur Metser: Consulting fees from POINT Biopharm; chair, Ontario PET Steering Committee and Ontario Health-Cancer Care Ontario. Patrick Veit-Haibach: Grants from Siemens Healthineers; support for attending meetings and/or travel from Siemens Healthineers; PET Center of Excellence committee of the Society of Nuclear Medicine member. The other authors have no relevant financial or non-financial interests to disclose.

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Basso Dias, A., Mirshahvalad, S.A., Ortega, C. et al. The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer. Eur J Nucl Med Mol Imaging 50, 2167–2176 (2023). https://doi.org/10.1007/s00259-023-06136-0

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