# Virtual Patients, Real Gains: Digital Twin-Based Simulated CT for Multitask Lung Nodule Analysis

arXiv: https://arxiv.org/abs/2502.21187 · HTML: https://mlsanity.com/paper/2502.21187/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [MAISI: Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2409.11169/) (2024)
- [VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging](https://mlsanity.com/paper/2406.05285/) (2024)
- [Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection](https://mlsanity.com/paper/2404.11221/) (2024)
- [The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Data](https://mlsanity.com/paper/2308.09730/) (2023)
- [Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography](https://mlsanity.com/paper/2203.03074/) (2022)

## Built on by (most cited first, 1 of 1)

- [Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT](https://mlsanity.com/paper/2601.19380/) (2026)

## Abstract (arXiv)

AI-based lung cancer screening is constrained by scarce, annotated CT data, particularly for rare nodule presentations. We investigate whether physics-based, anatomy-informed simulated CT can improve AI performance across three lung-nodule tasks: detection, segmentation, and malignancy classification. Using the Virtual Lung Screening Trial framework, we generated 174 digital human twins (XCAT3), embedded 512 procedurally controlled nodules (X-Lesions, 4-30 mm), and simulated CT (DukeSim) under two scanner configurations, yielding 1,044 annotated scans. Combined with clinical data, these trained models for detection (MONAI), segmentation (VISTA3D, nnU-Net), and classification (Med3D), evaluated on external test sets. Detection sensitivity at 1 FP/scan rose from 0.37 to 0.56 (p < 0.001); segmentation improved modestly (Dice 0.61 to 0.64, 0.66 to 0.69); classification AUC rose from 0.78 to 0.87 (p < 0.001). Physics-based virtual imaging trials can help address data scarcity in medical AI.