Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Pereira, T (Author) Other; Freitas, C (Author) Other; Costa, JL (Author) Other; Morgado, J (Author) Other; Silva, F (Author) Other; Negrao, E (Author) Other; de Lima, BF (Author) Other; da Silva, MC (Author) Other; António Madureira (Author) FMUP; Ramos, I (Author) Other; Hespanhol V (Author) FMUP; Cunha, A (Author) Other; Oliveira, HP (Author) FCUP

Journal

Title: Journal of Clinical Medicine

Vol. 17

Final page: 118

ISSN: 2077-0383

Publisher: MDPI

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 21 Citations

Other information

Authenticus ID: P-00T-840

DOI: 10.3390/jcm10010118

Abstract (EN): Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10