# Highly accurate model for prediction of lung nodule malignancy with CT scans

arXiv: https://arxiv.org/abs/1802.01756 · HTML: https://mlsanity.com/paper/1802.01756/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 14 of 14)

- [The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study](https://mlsanity.com/paper/2409.04368/) (2024)
- [CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction](https://mlsanity.com/paper/2206.14903/) (2022)
- [LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model](https://mlsanity.com/paper/2304.04814/) (2023)
- [Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)](https://mlsanity.com/paper/2112.13553/) (2021)
- [The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification](https://mlsanity.com/paper/2108.05386/) (2021)
- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [Deep Learning Neural Network for Lung Cancer Classification: Enhanced Optimization Function](https://mlsanity.com/paper/2208.06353/) (2022)
- [Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images](https://mlsanity.com/paper/2407.02625/) (2024)
- [Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)](https://mlsanity.com/paper/2408.15355/) (2024)
- [Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT](https://mlsanity.com/paper/2103.03931/) (2021)
- [Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans](https://mlsanity.com/paper/2103.14537/) (2021)
- [LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering](https://mlsanity.com/paper/2505.06370/) (2025)
- [Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization](https://mlsanity.com/paper/2508.08452/) (2025)
- [Disentangling Lung-Cancer CT/LDCT AI: A Systematic Evidence Map of Clinical Tasks, Evidence Chains, and Translational Gaps](https://mlsanity.com/paper/2609.35240/) (2026)

## Abstract (arXiv)

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX.