# 3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis

arXiv: https://arxiv.org/abs/2207.13830 · HTML: https://mlsanity.com/paper/2207.13830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction](https://mlsanity.com/paper/1802.03584/) (2018)
- [Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning](https://mlsanity.com/paper/1912.13335/) (2019)
- [ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification](https://mlsanity.com/paper/2010.15417/) (2020)
- [Lung Nodule Classification using Deep Local-Global Networks](https://mlsanity.com/paper/1904.10126/) (2019)
- [Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification](https://mlsanity.com/paper/2106.15778/) (2021)
- [Gated-Dilated Networks for Lung Nodule Classification in CT scans](https://mlsanity.com/paper/1901.00120/) (2019)

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

- [Beyond Size and Growth: Rethinking Lung Cancer Screening with AI Based Nodule Detection and Diagnosis](https://mlsanity.com/paper/2512.00281/) (2025)

## Abstract (arXiv)

Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ's surface is developed, and coupled with an automatic extraction of morphological features given by the distribution of mean curvature and mesh energy. An XGBoost supervised classifier is then trained and tested on the 3D-morphomics to predict the pathological states. This framework is applied to the prediction of the malignancy of lung's nodules. On a subset of NLST database with malignancy confirmed biopsy, using 3D-morphomics only, the classification model of lung nodules into malignant vs. benign achieves 0.964 of AUC. Three other sets of classical features are trained and tested, (1) clinical relevant features gives an AUC of 0.58, (2) 111 radiomics gives an AUC of 0.976, (3) radiologist ground truth (GT) containing the nodule size, attenuation and spiculation qualitative annotations gives an AUC of 0.979. We also test the Brock model and obtain an AUC of 0.826. Combining 3D-morphomics and radiomics features achieves state-of-the-art results with an AUC of 0.978 where the 3D-morphomics have some of the highest predictive powers. As a validation on a public independent cohort, models are applied to the LIDC dataset, the 3D-morphomics achieves an AUC of 0.906 and the 3D-morphomics+radiomics achieves an AUC of 0.958, which ranks second in the challenge among deep models. It establishes the curvature distributions as efficient features for predicting lung nodule malignancy and a new method that can be applied directly to arbitrary computer aided diagnosis task.