External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images

• Published in: Frontiers in medicine 2023-02, Vol.10, p.1133269-1133269
• Main Authors: Ghezzo, Samuele, Mongardi, Sofia, Bezzi, Carolina, Samanes Gajate, Ana Maria, Preza, Erik, Gotuzzo, Irene, Baldassi, Francesco, Jonghi-Lavarini, Lorenzo, Neri, Ilaria, Russo, Tommaso, Brembilla, Giorgio, De Cobelli, Francesco, Scifo, Paola, Mapelli, Paola, Picchio, Maria
• Format: Article
• Language: English
• Subjects: convolutional neural network; external validation; Medicine; prostate cancer; PSMA; segmentation
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2023.1133269

IntroductionState of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. MethodsEighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. ResultsWhen compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring). DiscussionIn conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.