Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [68Ga]Ga-PSMA-11 PET images

Jake Kendrick, Roslyn J. Francis, Ghulam Mubashar Hassan, Pejman Rowshan Farzad, Jeremy SL Ong, Michael C. McCarthy, Sweeka Alexander, Martin Ebert

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose
This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model.
Methods
One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen’s kappa statistic.
Results
Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96–7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23–11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen’s k = 0.31).
Conclusion
RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease.


Original languageEnglish
Pages (from-to)4077-4086
Number of pages10
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume50
Issue number13
Early online date8 Aug 2023
DOIs
Publication statusPublished - Nov 2023

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