Artificial Intelligence Applications in Personalizing Lung Cancer Management: State of the Art and Future Perspectives

Overview

Journal J Thorac Dis

Publisher AME Publishing Company

Specialty Pulmonary Medicine

Date 2024 Nov 18

PMID 39552872

Authors

Filippo Lococo

Galal Ghaly

Sara Flamini

Annalisa Campanella

Marco Chiappetta

Emilio Bria

Emanuele Vita

Giampaolo Tortora

Jessica Evangelista

Carolina Sassorossi

Maria Teresa Congedo

Vincenzo Valentini

Evis Sala

Alfredo Cesario

Stefano Margaritora

Luca Boldrini

Abdelrahman Mohammed

Affiliations

Soon will be listed here.

Abstract

Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare. This dynamic landscape has embraced the incorporation of advanced machine learning and deep learning techniques, particularly artificial intelligence (AI), into the realm of precision medicine. These groundbreaking innovations create fertile ground for the development of AI-based models adept at extracting valuable insights to inform clinical decisions, with the potential to quantitatively interpret patient data and impact overall patient outcomes significantly. In this comprehensive narrative review, a synthesis of various studies is presented, with a specific focus on three core areas aimed at providing clinicians with a practical understanding of AI-based technologies' potential applications in the diagnosis and management of non-small cell lung cancer (NSCLC). The emphasis is placed on methods for diagnosing malignancy in lung lesions, approaches to predicting histology and other pathological characteristics, and methods for predicting NSCLC gene mutations. The review culminates in a discussion of current trends and future perspectives within the domain of AI-based models, all directed toward enhancing patient care and outcomes in NSCLC. Furthermore, the review underscores the synthesis of diverse studies, accentuating AI applications in NSCLC diagnosis and management. It concludes with a forward-looking discussion on current trends and future perspectives, highlighting the LANTERN Study as a pioneering force set to elevate patient care and outcomes to unprecedented levels.

Citing Articles

Editorial: Artificial intelligence and imaging for oncology.

Zhou Y, Li Z, Yadav P Front Oncol. 2025; 15:1547968.

PMID: 39980540 PMC: 11839417. DOI: 10.3389/fonc.2025.1547968.

References

Ladbury C, Amini A, Govindarajan A, Mambetsariev I, Raz D, Massarelli E . Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med. 2023; 4(2):100933. PMC: 9975283. DOI: 10.1016/j.xcrm.2023.100933. View

Li Y, Chen D, Wu X, Yang W, Chen Y . A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations. J Thorac Dis. 2022; 13(12):7006-7020. PMC: 8743410. DOI: 10.21037/jtd-21-806. View

Khoo C, Rogers T, Fellowes A, Bell A, Fox S . Molecular methods for somatic mutation testing in lung adenocarcinoma: EGFR and beyond. Transl Lung Cancer Res. 2015; 4(2):126-41. PMC: 4384221. DOI: 10.3978/j.issn.2218-6751.2015.01.10. View

Moitra D, Mandal R . Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network. J Digit Imaging. 2020; 33(4):895-902. PMC: 7522151. DOI: 10.1007/s10278-020-00337-x. View

Alduais Y, Zhang H, Fan F, Chen J, Chen B . Non-small cell lung cancer (NSCLC): A review of risk factors, diagnosis, and treatment. Medicine (Baltimore). 2023; 102(8):e32899. PMC: 11309591. DOI: 10.1097/MD.0000000000032899. View

Acs B, Rantalainen M, Hartman J . Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020; 288(1):62-81. DOI: 10.1111/joim.13030. View

Zhang Y, Jiang B, Zhang L, Greuter M, de Bock G, Zhang H . Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening. Curr Med Imaging. 2021; 18(3):327-334. DOI: 10.2174/1573405617666210806125953. View

Zhang Y, Jin M, Li J, Kong X . Identifying circulating miRNA biomarkers for early diagnosis and monitoring of lung cancer. Biochim Biophys Acta Mol Basis Dis. 2020; 1866(10):165847. DOI: 10.1016/j.bbadis.2020.165847. View

Nair A, Screaton N, Holemans J, Jones D, Clements L, Barton B . The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol. 2017; 28(1):226-234. PMC: 5717117. DOI: 10.1007/s00330-017-4903-z. View

10.

Yamamoto S, Korn R, Oklu R, Migdal C, Gotway M, Weiss G . ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology. 2014; 272(2):568-76. DOI: 10.1148/radiol.14140789. View

11.

Chung K, Jacobs C, Scholten E, Goo J, Prosch H, Sverzellati N . Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?. Radiology. 2017; 284(1):264-271. DOI: 10.1148/radiol.2017161624. View

12.

Christe A, Leidolt L, Huber A, Steiger P, Szucs-Farkas Z, Roos J . Lung cancer screening with CT: evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels. Eur J Radiol. 2013; 82(12):e873-8. DOI: 10.1016/j.ejrad.2013.08.026. View

13.

Wang S, Wang T, Yang L, Yang D, Fujimoto J, Yi F . ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine. 2019; 50:103-110. PMC: 6921240. DOI: 10.1016/j.ebiom.2019.10.033. View

14.

Coudray N, Ocampo P, Sakellaropoulos T, Narula N, Snuderl M, Fenyo D . Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018; 24(10):1559-1567. PMC: 9847512. DOI: 10.1038/s41591-018-0177-5. View

15.

Ohno Y, Aoyagi K, Yaguchi A, Seki S, Ueno Y, Kishida Y . Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT. Radiology. 2020; 296(2):432-443. DOI: 10.1148/radiol.2020191740. View

16.

Travis W, Brambilla E, Riely G . New pathologic classification of lung cancer: relevance for clinical practice and clinical trials. J Clin Oncol. 2013; 31(8):992-1001. DOI: 10.1200/JCO.2012.46.9270. View

17.

Liu Y, Kim J, Qu F, Liu S, Wang H, Balagurunathan Y . CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma. Radiology. 2016; 280(1):271-80. PMC: 4934516. DOI: 10.1148/radiol.2016151455. View

18.

Sakamoto T, Furukawa T, Lami K, Pham H, Uegami W, Kuroda K . A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res. 2020; 9(5):2255-2276. PMC: 7653145. DOI: 10.21037/tlcr-20-591. View

19.

Joy Mathew C, David A, Joy Mathew C . Artificial Intelligence and its future potential in lung cancer screening. EXCLI J. 2021; 19:1552-1562. PMC: 7783473. DOI: 10.17179/excli2020-3095. View

20.

Setio A, Traverso A, de Bel T, Berens M, Bogaard C, Cerello P . Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal. 2017; 42:1-13. DOI: 10.1016/j.media.2017.06.015. View