Haolin Huang

The deployment of PET tracers faces a cold-start dilemma in two common scenarios: when a novel tracer enters early clinical use with scarce healthy controls (HCs) and no diagnostic labels, and when an established tracer is introduced at a new site or resource-limited region where normative references have yet to be accumulated. Existing anomaly detection methods require large training cohorts or operate only on two-dimensional slices, failing to address the joint demands of extreme data scarcity and volumetric reasoning. We propose TracerAD, a training-free few-shot framework for three-dimensional brain PET anomaly detection that requires only a handful of HC scans and no model training. TracerAD extracts frozen vision foundation model features from registered brain volumes and detects pathological deviations at three spatial scales: coordinate-level focal detection (SAFD), atlas-constrained regional matching (RCSM), and graph-based inter-regional metabolic reasoning (TAMR), producing a clinically interpretable 3D anomaly heatmap. Evaluated on an amyloid PET dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) under 1- to 10-shot settings, TracerAD achieves competitive detection performance without any training or fine-tuning, offering a practical tool for accelerating PET tracer deployment across diverse clinical settings.

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude threshold transfer and cross-site comparability. We present a unified, anatomically guided deep-learning framework that harmonizes multi-tracer PET-MRI to PET-CT. The model learns CT-anchored attenuation representations with a Vision Transformer Autoencoder, aligns MRI features to CT space via contrastive objectives, and performs attention-guided residual correction. In paired same-day scans (N = 70; amyloid, tau, FDG), cross-platform bias fell by >80% while preserving inter-regional biological topology. The framework generalized zero-shot to held-out tracers (18F-florbetapir; 18F-FP-CIT) without retraining. Multicentre validation (N = 420; three sites, four vendors) reduced amyloid Centiloid discrepancies from 23.6 to 4.1 (within PET-CT test-retest precision) and aligned tau SUVR thresholds. These results enable platform-agnostic diagnostic cutoffs and reliable longitudinal monitoring when patients transition between modalities, establishing a practical route to scalable, radiation-sparing quantitative PET in therapeutic workflows.

The dopamine transporter (DAT) imaging such as 11C-CFT PET has shown significant superiority in diagnosing Parkinson’s Disease (PD). However, most hospitals have no access to DAT imaging but instead turn to the commonly used 18F-FDG PET, which may not show major abnormalities of PD at visual analysis and thus hinder the performance of computer-aided diagnosis (CAD). To tackle this challenge, we propose a Metabolism-aware Anomaly Detection (MetaAD) framework to highlight abnormal metabolism cues of PD in 18F-FDG PET scans. MetaAD converts the input FDG image into a synthetic CFT image with healthy patterns, and then reconstructs the FDG image by a reversed modality mapping. The visual differences between the input and reconstructed images serve as indicators of PD metabolic anomalies. A dual-path training scheme is adopted to prompt the generators to learn an explicit normal data distribution via cyclic modality translation while enhancing their abilities to memorize healthy metabolic characteristics. The experiments reveal that MetaAD not only achieves superior performance in visual interpretability and anomaly detection for PD diagnosis, but also shows effectiveness in assisting supervised CAD methods. Our code is available at https://github.com/MedAIerHHL/MetaAD.

Background: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. Purpose: To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. Study Type: Retrospective. Population: One hundred ninety-one patients (median age, 65 years; age range, 54–73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. Field Strength/Sequence: 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. Assessment: Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. Statistical Tests: Student’s t-tests, DeLong’s tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan–Meier plots, SHapley Additive exPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. Results: The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792–0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell’s concordance index (0.804; 95% CI: 0.749–0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Data Conclusion: Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. Evidence Level: 3 Technical Efficacy: Stage 3

Purpose: To evaluate a new radiomics strategy that incorporates intratumoral and peritumoral features extracted from lung CT images with ensemble learning for pretreatment prediction of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Methods: A total of 105 patients (47 LUSC and 58 LUAD) with pretherapy CT scans were involved in this retrospective study, and were divided into training (n = 73) and testing (n = 32) cohorts. Seven categories of radiomics features involving 3078 metrics in total were extracted from the intra- and peritumoral regions of each patient’s CT data. Student’s t tests in combination with three feature selection methods were adopted for optimal features selection. An ensemble classifier was developed using five common machine learning classifiers with these optimal features. The performance was assessed using both training and testing cohorts, and further compared with that of Visual Geometry Group-16 (VGG-16) deep network for this predictive task. Results: The classification models developed using optimal feature subsets determined from intratumoral region and peritumoral region with the ensemble classifier achieved mean area under the curve (AUC) of 0.87, 0.83 in the training cohort and 0.66, 0.60 in the testing cohort, respectively. The model developed by using the optimal feature subset selected from both intra- and peritumoral regions with the ensemble classifier achieved great performance improvement, with AUC of 0.87 and 0.78 in both cohorts, respectively, which are also superior to that of VGG-16 (AUC of 0.68 in the testing cohort). Conclusions: The proposed new radiomics strategy that extracts image features from the intra- and peritumoral regions with ensemble learning could greatly improve the diagnostic performance for the histological subtype stratification in patients with NSCLC.