This study aimed to develop and validate deep learning models using [¹⁸F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [¹⁸F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [¹⁸F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.

Tumor-infiltrating lymphocytes (TILs) are key immune biomarkers associated with prognosis and treatment response in early-stage breast cancer (BC), particularly in the triple-negative subtype. This study aimed to evaluate whether [18F]FDG PET/CT imaging and routine cell blood count (CBC)-derived biomarkers can serve as non-invasive surrogates for TILs, using machine-learning models. We retrospectively analyzed 358 patients with biopsy-proven early-stage invasive BC who underwent pre-treatment [18F]FDG PET/CT imaging. PET-derived biomarkers were extracted from the primary tumor, lymph nodes, and lymphoid organs (spleen and bone marrow). CBC-derived biomarkers included neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). TILs were assessed histologically and categorized as low (0-10%), intermediate (11-59%), or high (≥ 60%). Correlations were assessed using Spearman's rank coefficient, and classification and regression models were built using several machine-learning algorithms. Tumor SUVmax and tumor SUVmean showed the highest correlation with TIL levels (ρ = 0.29 and 0.30 respectively, p < 0.001 for both), but overall associations between TILs and PET or CBC-derived biomarkers were weak. No CBC-derived biomarker showed significant correlation or discriminative performance. Machine-learning models failed to predict TIL levels with satisfactory accuracy (maximum balanced accuracy = 0.66). Lymphoid organ metrics (SLR, BLR) and CBC-derived parameters did not significantly enhance predictive value. In this study, neither [18F]FDG PET/CT nor routine CBC-derived biomarkers reliably predict TILs levels in early-stage BC. This observation was made in presence of potential scanner-related variability and for a restricted set of usual PET metrics. Future models should incorporate more targeted imaging approaches, such as immunoPET, to non-invasively assess immune infiltration with higher specificity and improve personalized treatment strategies.

Dynamic Positron Emission Tomography (PET) scans offer rich spatiotemporal data for detecting malignancies, but their high-dimensionality and noise pose significant challenges. We introduce a novel framework, the Equivariant Spatiotemporal Transformer with MDL-Guided Feature Selection (EST-MDL), which integrates group-theoretic symmetries, Kolmogorov complexity, and Minimum Description Length (MDL) principles. By enforcing spatial and temporal symmetries (e.g., translations and rotations) and leveraging MDL for robust feature selection, our model achieves improved generalization and interpretability. Evaluated on three realworld PET datasets--LUNG-PET, BRAIN-PET, and BREAST-PET--our approach achieves AUCs of 0.94, 0.92, and 0.95, respectively, outperforming CNNs, Vision Transformers (ViTs), and Graph Neural Networks (GNNs) in AUC, sensitivity, specificity, and computational efficiency. This framework offers a robust, interpretable solution for malignancy detection in clinical settings.

Positron emission tomography-computed tomography (PET-CT) is recommended for response evaluation in aggressive large B-cell lymphoma (LBCL) but cannot detect minimal residual disease (MRD). Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for real-time disease monitoring. This study evaluated longitudinal ctDNA monitoring as an MRD marker in LBCL. In this prospective, single-center study, 14 newly diagnosed LBCL patients receiving first-line immunochemotherapy underwent frequent longitudinal blood sampling. A 53-gene targeted sequencing panel quantified ctDNA and evaluated its kinetics, correlating it with clinical parameters and PET-CT, including total metabolic tumor volume (TMTV) calculated using AI-based analysis via RECOMIA. Baseline ctDNA was detected in 11 out of 14 patients (79%), with a median variant allele frequency of 6.88% (interquartile range: 1.19-10.20%). ctDNA levels correlated significantly with TMTV (ρ = 0.91, p < 0.0001) and lactate dehydrogenase. Circulating tumor DNA kinetics, including after one treatment cycle, mirrored PET-CT metabolic changes and identified relapsing or refractory cases. This study demonstrates ctDNA-based MRD monitoring in LBCL using a fixed targeted assay with an analytical sensitivity of at least 10-3. The kinetics of ctDNA reflects the clinical course and PET-CT findings, underscoring its complementary potential to PET-CT.

In the last decade, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment and improved prognosis. However, severe checkpoint inhibitor-induced liver injury (CHILI), which can lead to treatment discontinuation or death, occurs in up to 18% of the patients. The aim of this study is to evaluate the value of PET/CT radiomics analysis for the detection of CHILI. Patients with CHILI grade 2 or higher who underwent liver function tests and liver biopsy were retrospectively included. Minors, patients with cognitive impairments, and patients with viral infections were excluded from the study. The patients' liver and spleen were contoured on the anonymized PET/CT imaging data, followed by radiomics feature extraction. Principal component analysis (PCA) and Bonferroni corrections were used for statistical analysis and exploration of radiomics features related to CHILI. Sixteen patients were included and 110 radiomics features were extracted from PET images. Liver PCA-5 showed significance as well as one associated feature but did not remain significant after Bonferroni correction. Spleen PCA-5 differed significantly between CHILI and non-CHILI patients even after Bonferroni correction, possibly linked to the higher metabolic function of the spleen in autoimmune diseases due to the recruitment of immune cells. This pilot study identified statistically significant differences in PET-derived radiomics features of the spleen and observable changes in the liver on PET/CT scans before and after the onset of CHILI. Identifying these features could aid in diagnosing or predicting CHILI, potentially enabling personalized treatment. Larger multicenter prospective studies are needed to confirm these findings and develop automated detection methods.

This study proposes a deep learning-based framework for automated segmentation of brain regions and classification of amyloid positivity using positron emission tomography (PET) images alone, without the need for structural MRI or CT. A 3D U-Net architecture with four layers of depth was trained and validated on a dataset of 200 F18-florbetapir amyloid-PET scans, with an 130/20/50 train/validation/test split. Segmentation performance was evaluated using Dice similarity coefficients across 30 brain regions, with scores ranging from 0.45 to 0.88, demonstrating high anatomical accuracy, particularly in subcortical structures. Quantitative fidelity of PET uptake within clinically relevant regions. Precuneus, prefrontal cortex, gyrus rectus, and lateral temporal cortex was assessed using normalized root mean square error, achieving values as low as 0.0011. Furthermore, the model achieved a classification accuracy of 0.98 for amyloid positivity based on regional uptake quantification, with an area under the ROC curve (AUC) of 0.99. These results highlight the model's potential for integration into PET only diagnostic pipelines, particularly in settings where structural imaging is not available. This approach reduces dependence on coregistration and manual delineation, enabling scalable, reliable, and reproducible analysis in clinical and research applications. Future work will focus on clinical validation and extension to diverse PET tracers including C11 PiB and other F18 labeled compounds.

Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological disorders. In this work, we focus on Bayesian flow networks (BFNs), a novel class of generative models, which have not yet been applied to medical imaging or anomaly detection. BFNs combine the strength of diffusion frameworks and Bayesian inference. We introduce AnoBFN, an extension of BFNs for UAD, designed to: i) perform conditional image generation under high levels of spatially correlated noise, and ii) preserve subject specificity by incorporating a recursive feedback from the input image throughout the generative process. We evaluate AnoBFN on the challenging task of Alzheimer's disease-related anomaly detection in FDG PET images. Our approach outperforms other state-of-the-art methods based on VAEs (beta-VAE), GANs (f-AnoGAN), and diffusion models (AnoDDPM), demonstrating its effectiveness at detecting anomalies while reducing false positive rates.

Though ultra-low dose (ULD) imaging offers notable benefits, its widespread clinical adoption faces challenges. Long-axial field-of-view (LAFOV) PET/CT systems are expensive and scarce, while artificial intelligence (AI) shows great potential but remains largely limited to specific systems and is not yet widely used in clinical practice. However, integrating AI techniques and technological advancements into ULD imaging is helping bridge the gap between standard axial field-of-view (SAFOV) and LAFOV PET/CT systems. This paper offers an initial evaluation of ULD capabilities using one of the latest SAFOV PET/CT device. A patient injected with 16.4 MBq ¹⁸F-FDG underwent a local protocol consisting of a dynamic acquisition (first 30 min) of the abdominal section and a static whole body 74 min post-injection on a GE Omni PET/CT. From the acquired images we computed the dosimetry and compared clinical output from kidney function and brain uptake to kidney model and normal databases, respectively. The effective PET dose for this patient was 0.27 ± 0.01 mSv and the absorbed doses were 0.56 mGy, 0.89 mGy and 0.20 mGy, respectively to the brain, heart, and kidneys. The recorded kidney concentration closely followed the kidney model, matching the increase and decrease in activity concentration over time. Normal values for the z-score were observed for the brain uptake, indicating typical brain function and activity patterns consistent with healthy individuals. The signal to noise ration obtained in this study (13.1) was comparable to the LAFOV reported values. This study shows promising capabilities of ultra-low-dose imaging in SAFOV PET devices, previously deemed unattainable with SAFOV PET imaging.

Diffusion models have shown great promise in medical image denoising and reconstruction, but their application to Positron Emission Tomography (PET) imaging remains limited by tracer-specific contrast variability and high computational demands. In this work, we proposed an anatomical prior-guided PET image reconstruction method based on diffusion models, inspired by the deep diffusion image prior (DDIP) framework. The proposed method alternated between diffusion sampling and model fine-tuning guided by the PET sinogram, enabling the reconstruction of high-quality images from various PET tracers using a score function pretrained on a dataset of another tracer. To improve computational efficiency, the half-quadratic splitting (HQS) algorithm was adopted to decouple network optimization from iterative PET reconstruction. The proposed method was evaluated using one simulation and two clinical datasets. For the simulation study, a model pretrained on [$^{18}$F]FDG data was tested on amyloid-negative PET data to assess out-of-distribution (OOD) performance. For the clinical-data validation, ten low-dose [$^{18}$F]FDG datasets and one [$^{18}$F]Florbetapir dataset were tested on a model pretrained on data from another tracer. Experiment results show that the proposed PET reconstruction method can generalize robustly across tracer distributions and scanner types, providing an efficient and versatile reconstruction framework for low-dose PET imaging.

The integration of artificial intelligence (AI) into [¹⁸F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology. Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.