CO:12:4 | Predicting 12-month clinical relapse in Takayasu arteritis using [18F]FDG PET and convolutional neural networks

Alessandro Tomelleri¹, Carolina Bezzi², Sara Resta², Corrado Campochiaro¹, Federico Fallanca², Elena Baldissera¹, Samuele Ghezzo², Nicola Farina¹, Arturo Chiti², Paola Mapelli², Marco Matucci-Cerinic¹, Maria Picchio², Lorenzo Dagna¹. | ¹IRCCS Ospedale San Raffaele, Unità di Immunologia Reumatologia Allergologia e Malattie Rare, Milano; ²IRCCS Ospedale San Raffaele, Unità di Medicina Nucleare, Milano, Italy

Background. Takayasu arteritis (TAK) is a large-vessel vasculitis with fluctuating disease activity and a lack of validated biomarkers for relapse prediction. While ^[18F]FDG PET provides diagnostic sensitivity, its prognostic role remains debated. This study aimed to assess whether convolutional neural networks (CNNs) applied to baseline ^[18F]FDG PET scans can predict clinical relapses within 12 months in patients with TAK.

Methods. We retrospectively analysed ^[18F]FDG PET scans acquired from patients with TAK between 2013 and 2023 at a tertiary centre. All scans were harmonised using a liver-based standardisation method to correct inter-scanner variability. Clinical relapse was defined as recurrence of TAK-related signs or symptoms, based on blinded chart review. CNNs were trained using four architectures: Baseline model (4 convolutional layers), Residual bottleneck model (3 blocks), and two models retrieved from the MONAI library – ResNet18 and DenseNet121. The dataset was split into training, validation, and test sets. To address class imbalance, we applied class weighting, early stopping, and 10-fold subsampling of the majority class. Data augmentation techniques included RandAffine and RandAxisFlip. Performance was evaluated using accuracy, class-specific precision and recall, specificity, and normalised Matthews correlation coefficient (nMCC).

Results. A total of 306 scans from 74 patients with TAK (median age: 47 years [range: 14–75]; 62 females) were included. Sixty-four scans (21%) were followed by clinical relapse within 12 months. Of these, 25 were correctly identified by blinded nuclear medicine readers. The Baseline model-1 achieved the best overall performance with 82% accuracy, 85% precision for class 0, 62% precision for class 1, 38% recall, 94% specificity, and 70% nMCC. DenseNet121-2 yielded 81% accuracy, 100% precision for class 1 (relapse), 8% recall, 100% specificity, and 62% nMCC, maintaining 100% precision and specificity in the subsampled test set. Performance metrics for all models are summarised in Figure 1. Compared to standard visual PET interpretation, which correctly identified fewer than 40% of relapses, CNN-based models significantly improved predictive performance (Figure 2).

Conclusions. This is the first study applying CNN-based ^[18F]FDG PET analysis to predict 12-month clinical relapse in patients with TAK, involving one of the largest PET datasets in this disease. Despite the retrospective design and class imbalance, CNNs demonstrated strong performance in identifying those likely to remain in remission. These findings support the use of imaging-based computational tools to inform treatment tapering and follow-up strategies in patients with TAK.