Kyle J. Lafata

Yuqi Wang, Aarzu Gupta, David Carpenter et al.

TodevelopanovelUncertaintyQuantification (UQ) framework to estimate the uncertainty of patient survival models in the absence of ground truth, we developed and evaluated our approach based on a dataset of 1383 patients treated with stereotactic radiosurgery (SRS) for brain metastases between January 2015 and December 2020. Our motivating hypothesis is that a time-to-event prediction of a test patient on inference is more certain given a higher feature-space-similarity to patients in the training set. Therefore, the uncertainty for a particular patient-of-interest is represented by the concordance index between a patient similarity rank and a prediction similarity rank. Model uncertainty was defined as the increased percentage of the max uncertainty-constrained-AUC compared to the model AUC. We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR). Our results show that all models had the lowest uncertainty on ICP (2.21%) and the highest uncertainty (17.28%) on ICPD. OS models demonstrated high variation in uncertainty performance, where NMTLR had the lowest uncertainty(1.96%)and CSF had the highest uncertainty (14.29%). In conclusion, our method can estimate the uncertainty of individual patient survival modeling results. As expected, our data empirically demonstrate that as model uncertainty measured via our technique increases, the similarity between a feature-space and its predicted outcome decreases.

Kaouther Mouheb, Mobina Ghojogh Nejad, Lavsen Dahal et al.

Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability. We propose CLAP, a novel Conditional LAtent Point-diffusion model that combines geometric deep learning with denoising diffusion models to enhance 3D representations of the large intestine. Given point clouds sampled from segmentation masks, we employ a hierarchical variational autoencoder to learn both global and local latent shape representations. Two conditional diffusion models operate within this latent space to refine the organ shape. A pretrained surface reconstruction model is then used to convert the refined point clouds into meshes. CLAP achieves substantial improvements in shape modeling accuracy, reducing Chamfer distance by 26% and Hausdorff distance by 36% relative to the initial suboptimal shapes. This approach offers a robust and extensible solution for high-fidelity organ modeling, with potential applicability to a wide range of anatomical structures.

Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal et al.

Background: Development of artificial intelligence (AI) models for lung cancer screening requires large, well-annotated low-dose computed tomography (CT) datasets and rigorous performance benchmarks. Purpose: To create a reproducible benchmarking resource leveraging the Duke Lung Cancer Screening (DLCS) and multiple public datasets to develop and evaluate models for nodule detection and classification. Materials & Methods: This retrospective study uses the DLCS dataset (1,613 patients; 2,487 nodules) and external datasets including LUNA16, LUNA25, and NLST-3D. For detection, MONAI RetinaNet models were trained on DLCS (DLCS-De) and LUNA16 (LUNA16-De) and evaluated using the Competition Performance Metric (CPM). For nodule-level classification, we compare five strategies: pretrained models (Models Genesis, Med3D), a self-supervised foundation model (FMCB), and ResNet50 with random initialization versus Strategic Warm-Start (ResNet50-SWS) pretrained with detection-derived candidate patches stratified by confidence. Results: For detection on the DLCS test set, DLCS-De achieved sensitivity 0.82 at 2 false positives/scan (CPM 0.63) versus LUNA16-De (0.62, CPM 0.45). For external validation on NLST-3D, DLCS-De (sensitivity 0.72, CPM 0.58) also outperformed LUNA16-De (sensitivity 0.64, CPM 0.49). For classification across multiple datasets, ResNet50-SWS attained AUCs of 0.71 (DLCS; 95% CI, 0.61-0.81), 0.90 (LUNA16; 0.87-0.93), 0.81 (NLST-3D; 0.79-0.82), and 0.80 (LUNA25; 0.78-0.82), matching or exceeding pretrained/self-supervised baselines. Performance differences reflected dataset label standards. Conclusion: This work establishes a standardized benchmarking resource for lung cancer AI research, supporting model development, validation, and translation. All code, models, and data are publicly released to promote reproducibility.

Fakrul Islam Tushar, Lavsen Dahal, Cindy McCabe et al.

AI models for lung cancer screening are limited by data scarcity, impacting generalizability and clinical applicability. Generative models address this issue but are constrained by training data variability. We introduce SYN-LUNGS, a framework for generating high-quality 3D CT images with detailed annotations. SYN-LUNGS integrates XCAT3 phantoms for digital twin generation, X-Lesions for nodule simulation (varying size, location, and appearance), and DukeSim for CT image formation with vendor and parameter variability. The dataset includes 3,072 nodule images from 1,044 simulated CT scans, with 512 lesions and 174 digital twins. Models trained on clinical + simulated data outperform clinical only models, achieving 10% improvement in detection, 2-9% in segmentation and classification, and enhanced synthesis. By incorporating anatomy-informed simulations, SYN-LUNGS provides a scalable approach for AI model development, particularly in rare disease representation and improving model reliability.

Michael E. Garcia-Alcoser, Mobina GhojoghNejad, Fakrul Islam Tushar et al.

Purpose: This study aims to evaluate the effectiveness of large language models (LLMs) in automating disease annotation of CT radiology reports. We compare a rule-based algorithm (RBA), RadBERT, and three lightweight open-weight LLMs for multi-disease labeling of chest, abdomen, and pelvis (CAP) CT reports. Materials and Methods: This retrospective study analyzed 40,833 CT reports from 29,540 patients, with 1,789 CAP reports manually annotated across three organ systems. External validation was conducted using the CT-RATE dataset. Three open-weight LLMs were tested with zero-shot prompting. Performance was evaluated using Cohen's Kappa and micro/macro-averaged F1 scores. Results: In 12,197 Duke CAP reports from 8,854 patients, Llama-3.1 8B and Gemma-3 27B showed the highest agreement ($κ$ median: 0.87). On the manually annotated set, Gemma-3 27B achieved the top macro-F1 (0.82), followed by Llama-3.1 8B (0.79), while the RBA scored lowest (0.64). On the CT-RATE dataset (lungs/pleura only), Llama-3.1 8B performed best (0.91), with Gemma-3 27B close behind (0.89). Performance differences were mainly due to differing labeling practices, especially for lung atelectasis. Conclusion: Lightweight LLMs outperform rule-based methods for CT report annotation and generalize across organ systems with zero-shot prompting. However, binary labels alone cannot capture the full nuance of report language. LLMs can provide a flexible, efficient solution aligned with clinical judgment and user needs.

Lavsen Dahal, Mobina Ghojoghnejad, Dhrubajyoti Ghosh et al.

Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of computational phantoms face limitations, particularly in terms of sample size and diversity. Insufficient representation of the population hampers accurate assessment of imaging technologies across different patient groups. Traditionally, the more realistic computational phantoms were created by manual segmentation, which is a laborious and time-consuming task, impeding the expansion of phantom libraries. This study presents a framework for creating realistic computational phantoms using a suite of automatic segmentation models and performing three forms of automated quality control on the segmented organ masks. The result is the release of over 2500 new computational phantoms, so-named XCAT3.0 after the ubiquitous XCAT computational construct. This new formation embodies 140 structures and represents a comprehensive approach to detailed anatomical modeling. The developed computational phantoms are formatted in both voxelized and surface mesh formats. The framework is combined with an in-house CT scanner simulator to produce realistic CT images. The framework has the potential to advance virtual imaging trials, facilitating comprehensive and reliable evaluations of medical imaging technologies. Phantoms may be requested at https://cvit.duke.edu/resources/. Code, model weights, and sample CT images are available at https://xcat-3.github.io/.

Yuqi Wang, Jacob A. Macdonald, Katelyn R. Morgan et al.

Spleen volumetry is primarily associated with patients suffering from chronic liver disease and portal hypertension, as they often have spleens with abnormal shapes and sizes. However, manually segmenting the spleen to obtain its volume is a time-consuming process. Deep learning algorithms have proven to be effective in automating spleen segmentation, but a suitable dataset is necessary for training such algorithms. To our knowledge, the few publicly available datasets for spleen segmentation lack confounding features such as ascites and abdominal varices. To address this issue, the Duke Spleen Data Set (DSDS) has been developed, which includes 109 CT and MRI volumes from patients with chronic liver disease and portal hypertension. The dataset includes a diverse range of image types, vendors, planes, and contrasts, as well as varying spleen shapes and sizes due to underlying disease states. The DSDS aims to facilitate the creation of robust spleen segmentation models that can take into account these variations and confounding factors.