# Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline

arXiv: https://arxiv.org/abs/1912.08679 · HTML: https://mlsanity.com/paper/1912.08679/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach](https://mlsanity.com/paper/2308.14000/) (2023)
- [Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images](https://mlsanity.com/paper/2405.17483/) (2024)
- [Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation](https://mlsanity.com/paper/2102.10919/) (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)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)

## Abstract (arXiv)

The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset. Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score. Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.