Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index

Abstract

Purpose Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. Methods A total of 168 patients from three centers were divided into training, validation, and test groups. Manual annotations of skeletal lesions in [18F]fluoride PET/CT scans were used to train a CNN. The AI model was evaluated in 26 patients and compared to segmentations by physicians and to a SUV 15 threshold. PET index representing the percentage of skeletal volume taken up by lesions was estimated. Results There was no case in which all readers agreed on prevalence of lesions that the AI model failed to detect. PET index by the AI model correlated moderately strong to physician PET index (mean r = 0.69). Threshold PET index correlated fairly with physician PET index (mean r = 0.49). The sensitivity for lesion detection was 65–76% for AI, 68–91% for physicians, and 44–51% for threshold depending on which physician was considered reference. Conclusion It was possible to develop an AI-based model for automated assessment of PET/CT skeletal tumor burden. The model’s performance was superior to using a threshold and provides fully automated calculation of whole-body skeletal tumor burden. It could be further developed to apply to different radiotracers. Objective scan evaluation is a first step toward developing a PET/CT imaging biomarker for PCa skeletal metastases.

Artificial intelligence in skeletal metastasis imaging

“…This RCT proves the concept that quantifiable information extracted from an otherwise subjectively interpreted imaging study has profound prognostic value. Hoping to create a PET/CT counterpart of aBSI, Lindgren Belal et al improved on the PET15 index and developed a deep learning-based PET index as an imaging biomarker that assesses the whole-body skeletal tumor burden in prostate cancer patients to predict the course of the disease and influence clinical decision-making [54] . A total of 168 patients were included.…”

Les radiotraceurs autres que le PSMA-ligand pour l’imagerie TEP du cancer de prostate

“…Apolo et al ont montré que le nombre de lésions à la TEP/TDM [ 18 F]NaF initiale était associé à l'OS (p = 0,017) [59]. Dans le futur, les outils d'intelligence artificielle pourront être une aide au médecin pour réduire la variabilité inter-observateur (la subjectivité) dans l'analyse des images et réduire le temps nécessaire à la quantification de la charge tumorale totale au niveau du squelette [60].…”

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI‐based analysis of PSMA PET‐CT images

“…The interpretation relies on visual analysis, so it is subject to inter‐ and intra‐observer variability (Fanti et al, 2017). Artificial intelligence (AI) can help with the standardization of image interpretation, act as a second opinion to nuclear medicine physicians, possibly quantify the PSMA‐positive tumour burden before [ 177 Lu]Lu‐PSMA treatment, and evaluate the treatment response in consecutive PSMA PET‐CT scans (Lindgren Belal et al, 2024).…”