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<p>This study evaluated whether a CE-marked deep-learning-based web platform (PSMA-DLwp) maintains a per-patient detection rate (DR) comparable to that of an experienced nuclear medicine physician (ENMP) for identifying prostate cancer (PCa) foci using [<sup>18</sup>F]-piflufolastat PET/CT in patients with first biochemical recurrence. Secondary objectives included per-region DR and the correlation of SUVmax and tumor burden assessments between the ENMP and PSMA-DLwp. Methods: A retrospective analysis was conducted using data from the phase III PYTHON study, which compared [<sup>18</sup>F]-piflufolastat PET/CT to [<sup>18</sup>F]-fluoromethylcholine PET/CT in patients with first biochemical recurrence of PCa. Both ENMP and PSMA-DLwp independently reviewed [<sup>18</sup>F]-piflufolastat PET/CT scans. Concordance in DRs, regional analysis, and correlation of semi-quantitative parameters (SUVmax, tumor burden) were assessed. Discordant cases were re-evaluated by the ENMP to assess potential improvements provided by PSMA-DLwp. Results: Out of 192 evaluable scans, per-patient DRs were concordant in 75% of cases, with no statistically significant difference between ENMP and PSMA-DLwp (<i>p</i> = 0.11). Per-region analysis for T and N staging showed no significant DR differences (<i>p</i> = 0.72 and <i>p</i> = 0.30, respectively). PSMA-DLwp identified more foci in distant regions (M1a, M1b), but these were not statistically significant. Strong positive correlations were observed between ENMP and PSMA-DLwp for SUVmax (⍴<sub>s</sub>=0.85; <i>p</i> &lt; 0.0001) and tumor burden (⍴<sub>s</sub>=0.89; <i>p</i> &lt; 0.0001). In 8% of discordant cases, PSMA-DLwp improved the ENMP’s initial assessment. Conclusion: The PSMA-DLwp achieves DRs comparable to an expert reader and reliably quantifies tumor burden and SUVmax, supporting its use as an adjunct in the interpretation of [<sup>18</sup>F]-piflufolastat PET/CT for PCa. Its integration into clinical workflows can enhance diagnostic consistency and efficiency, particularly in high-volume or complex cases, thereby supporting more informed clinical decision-making.</p>

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Deep-learning-based software versus experienced nuclear medicine physician to read [18F]-piflufolastat used for PSMA-PET/CT imaging in prostate cancer patients presenting with first biochemical recurrence: A comparative study of detection rate

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Mathieu Gauthé,
  • Sona Balogova

摘要

This study evaluated whether a CE-marked deep-learning-based web platform (PSMA-DLwp) maintains a per-patient detection rate (DR) comparable to that of an experienced nuclear medicine physician (ENMP) for identifying prostate cancer (PCa) foci using [18F]-piflufolastat PET/CT in patients with first biochemical recurrence. Secondary objectives included per-region DR and the correlation of SUVmax and tumor burden assessments between the ENMP and PSMA-DLwp. Methods: A retrospective analysis was conducted using data from the phase III PYTHON study, which compared [18F]-piflufolastat PET/CT to [18F]-fluoromethylcholine PET/CT in patients with first biochemical recurrence of PCa. Both ENMP and PSMA-DLwp independently reviewed [18F]-piflufolastat PET/CT scans. Concordance in DRs, regional analysis, and correlation of semi-quantitative parameters (SUVmax, tumor burden) were assessed. Discordant cases were re-evaluated by the ENMP to assess potential improvements provided by PSMA-DLwp. Results: Out of 192 evaluable scans, per-patient DRs were concordant in 75% of cases, with no statistically significant difference between ENMP and PSMA-DLwp (p = 0.11). Per-region analysis for T and N staging showed no significant DR differences (p = 0.72 and p = 0.30, respectively). PSMA-DLwp identified more foci in distant regions (M1a, M1b), but these were not statistically significant. Strong positive correlations were observed between ENMP and PSMA-DLwp for SUVmax (⍴s=0.85; p < 0.0001) and tumor burden (⍴s=0.89; p < 0.0001). In 8% of discordant cases, PSMA-DLwp improved the ENMP’s initial assessment. Conclusion: The PSMA-DLwp achieves DRs comparable to an expert reader and reliably quantifies tumor burden and SUVmax, supporting its use as an adjunct in the interpretation of [18F]-piflufolastat PET/CT for PCa. Its integration into clinical workflows can enhance diagnostic consistency and efficiency, particularly in high-volume or complex cases, thereby supporting more informed clinical decision-making.