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Open AccessPerspective

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

1
Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
2
Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal
3
Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal
4
Institute for Research and Innovation in Health of the University of Porto, i3S, 4200-135 Porto, Portugal
5
Institute of Molecular Pathology and Immunology of the University of Porto, IPATIMUP, 4200-135 Porto, Portugal
6
Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
7
Department of Engineering, University of Trás-os-Montes and Alto Douro, UTAD, 5001-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(1), 118; https://doi.org/10.3390/jcm10010118
Received: 22 November 2020 / Revised: 28 December 2020 / Accepted: 28 December 2020 / Published: 31 December 2020
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. View Full-Text
Keywords: lung cancer assessment; tumour characterisation; personalised medicine; computer-aided decision; computed tomography analysis lung cancer assessment; tumour characterisation; personalised medicine; computer-aided decision; computed tomography analysis
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MDPI and ACS Style

Pereira, T.; Freitas, C.; Costa, J.L.; Morgado, J.; Silva, F.; Negrão, E.; de Lima, B.F.; da Silva, M.C.; Madureira, A.J.; Ramos, I.; Hespanhol, V.; Cunha, A.; Oliveira, H.P. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. J. Clin. Med. 2021, 10, 118. https://doi.org/10.3390/jcm10010118

AMA Style

Pereira T, Freitas C, Costa JL, Morgado J, Silva F, Negrão E, de Lima BF, da Silva MC, Madureira AJ, Ramos I, Hespanhol V, Cunha A, Oliveira HP. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. Journal of Clinical Medicine. 2021; 10(1):118. https://doi.org/10.3390/jcm10010118

Chicago/Turabian Style

Pereira, Tania; Freitas, Cláudia; Costa, José L.; Morgado, Joana; Silva, Francisco; Negrão, Eduardo; de Lima, Beatriz F.; da Silva, Miguel C.; Madureira, António J.; Ramos, Isabel; Hespanhol, Venceslau; Cunha, António; Oliveira, Hélder P. 2021. "Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images" J. Clin. Med. 10, no. 1: 118. https://doi.org/10.3390/jcm10010118

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