Towards Lung Cancer Staging via Μultipositional Radiomics and Machine Learning

Dimitris Fotopoulos, Dimitris Filos, Ekaterini Xinou, Ioanna Chouvarda

2023

Abstract

This work addresses lung cancer diagnosis, and more specifically disease staging, as a major clinical challenge, crucial for further treatment decisions. The procedure is currently performed by experts based on clinical and medical imaging data and is time consuming and costly. Within INCISIVE, an EU-funded research project which aims to develop a pan-European federated image repository for cancer and implement Artificial Intelligence (AI) tools for clinical practice, clinical challenges have been identified that can be supported by AI in medical imaging data to facilitate accurate diagnosis and support treatment planning. The support and automation of lung cancer staging has been identified as a priority among the INCISIVE clinical challenges. In this scope, we propose a method to automatically classify between the group that represents disease stages I and II (low severity), vs the group that includes stages III and IV (severe). Tumour-Node-Metastasis system is used as a reference for staging. Based on lung CT image series with tumour and lung volume segmentation, we calculate and harmonise radiomics features and we propose the combination of tumour and lung lobes radiomics features towards improving the classification performance. Having a rich feature set as a basis, several combinations of feature selection and classification methods are tested and compared. Multiple repetitions of cross-validation and external testing splits are used to reach robust manner. The proposed method is trained and tested on an integrated dataset comprised of two open datasets (the NSCLC-Radiomics and the NSCLC-Radiogenomics dataset from the Cancer Imaging Archive). It achieves average Precision and Recall of 77.5% and 78.7% respectively, which could be further improved with a more extensive training set. Therefore, this can be the basis for a prioritisation tool regarding lung cancer cases and detailed staging/treatment decisions.

Paper Citation

in Harvard Style

Fotopoulos D., Filos D., Xinou E. and Chouvarda I. (2023). Towards Lung Cancer Staging via Μultipositional Radiomics and Machine Learning. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 317-324. DOI: 10.5220/0011781500003414

in Bibtex Style

@conference{biosignals23,
author={Dimitris Fotopoulos and Dimitris Filos and Ekaterini Xinou and Ioanna Chouvarda},
title={Towards Lung Cancer Staging via Μultipositional Radiomics and Machine Learning},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={317-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011781500003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Towards Lung Cancer Staging via Μultipositional Radiomics and Machine Learning
SN - 978-989-758-631-6
AU - Fotopoulos D.
AU - Filos D.
AU - Xinou E.
AU - Chouvarda I.
PY - 2023
SP - 317
EP - 324
DO - 10.5220/0011781500003414
PB - SciTePress