# Beyond Size and Growth: Rethinking Lung Cancer Screening with AI Based Nodule Detection and Diagnosis

arXiv: https://arxiv.org/abs/2512.00281 · HTML: https://mlsanity.com/paper/2512.00281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 112 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [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)
- [Multivariate Confidence Calibration for Object Detection](https://mlsanity.com/paper/2004.13546/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [nnDetection: A Self-configuring Method for Medical Object Detection](https://mlsanity.com/paper/2106.00817/) (2021)
- [FreezeOut: Accelerate Training by Progressively Freezing Layers](https://mlsanity.com/paper/1706.04983/) (2017)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans](https://mlsanity.com/paper/2301.12291/) (2023)
- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)

## Abstract (arXiv)

Early detection of malignant lung nodules remains constrained by size and growth based screening criteria, often delaying diagnosis. We present an integrated AI system that jointly performs nodule detection and malignancy assessment directly at the nodule level from low dose CT scans, within a unified CADe/CADx framework. Unlike conventional pipelines separating detection and diagnosis, our approach targets malignant nodules directly, redefining evaluation at the point where clinical decisions are made. To address limitations in dataset scale and explainability, the system consists of a Large Ensemble Model (LEM) combining ensembles of shallow deep learning and feature based models. It was trained and evaluated on 25,709 scans with 69,449 annotated nodules, with external validation on an independent cohort. It achieved an AUC of 0.98 internally and 0.945 externally, outperforming all growth based metrics, Lung RADS size based triage, European volume and VDT based screening criteria, radiologists, and leading AI models. The model maintains high sensitivity at low false positive rates, excels for small and early stage cancers, and enables malignancy assessment up to one year earlier than radiologists for indeterminate and slow growing nodules. This approach has the potential to streamline lung cancer screening workflows and support earlier, more actionable clinical decision making.