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Caranfil E, Lami K, Uegami W, Fukuoka J. Artificial Intelligence and Lung Pathology. Adv Anat Pathol 2024; 31:344-351. [PMID: 38780094 DOI: 10.1097/pap.0000000000000448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.
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Affiliation(s)
- Emanuel Caranfil
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Wataru Uegami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
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Abbaker N, Minervini F, Guttadauro A, Solli P, Cioffi U, Scarci M. The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review. Front Oncol 2024; 14:1347464. [PMID: 38414748 PMCID: PMC10897973 DOI: 10.3389/fonc.2024.1347464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
Objectives To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.
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Affiliation(s)
- Namariq Abbaker
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
| | - Fabrizio Minervini
- Division of Thoracic Surgery, Luzerner Kantonsspital, Lucern, Switzerland
| | - Angelo Guttadauro
- Division of Surgery, Università Milano-Bicocca and Istituti Clinici Zucchi, Monza, Italy
| | - Piergiorgio Solli
- Division of Thoracic Surgery, Policlinico S. Orsola-Malpighi, Bologna, Italy
| | - Ugo Cioffi
- Department of Surgery, University of Milan, Milan, Italy
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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Lee SH, Jang HJ. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology. Clin Mol Hepatol 2022; 28:754-772. [PMID: 35443570 PMCID: PMC9597228 DOI: 10.3350/cmh.2021.0394] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023] Open
Abstract
Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.
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Affiliation(s)
- Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul, Korea,Corresponding author : Hyun-Jong Jang Department of Physiology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea Tel: +82-2-2258-7274, Fax: +82-2-532-9575, E-mail:
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A Survey on Deep Learning for Precision Oncology. Diagnostics (Basel) 2022; 12:diagnostics12061489. [PMID: 35741298 PMCID: PMC9222056 DOI: 10.3390/diagnostics12061489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
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Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review. Biomedicines 2022; 10:biomedicines10061237. [PMID: 35740259 PMCID: PMC9219996 DOI: 10.3390/biomedicines10061237] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 02/04/2023] Open
Abstract
Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.
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Murchan P, Ó’Brien C, O’Connell S, McNevin CS, Baird AM, Sheils O, Ó Broin P, Finn SP. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics (Basel) 2021; 11:1406. [PMID: 34441338 PMCID: PMC8393642 DOI: 10.3390/diagnostics11081406] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.
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Affiliation(s)
- Pierre Murchan
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland; (P.M.); (C.Ó.); (C.S.M.)
| | - Cathal Ó’Brien
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland; (P.M.); (C.Ó.); (C.S.M.)
- Department of Histopathology, St James’s Hospital, P.O. Box 580, James’s Street, D08 X4RX Dublin, Ireland
| | - Shane O’Connell
- School of Mathematics, Statistics, and Applied Mathematics, National University of Ireland Galway, H91 TK33 Galway, Ireland; (S.O.); (P.Ó.B.)
| | - Ciara S. McNevin
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland; (P.M.); (C.Ó.); (C.S.M.)
- Department of Medical Oncology, St James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Anne-Marie Baird
- School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, D02 A440 Dublin, Ireland; (A.-M.B.); (O.S.)
| | - Orla Sheils
- School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, D02 A440 Dublin, Ireland; (A.-M.B.); (O.S.)
| | - Pilib Ó Broin
- School of Mathematics, Statistics, and Applied Mathematics, National University of Ireland Galway, H91 TK33 Galway, Ireland; (S.O.); (P.Ó.B.)
| | - Stephen P. Finn
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland; (P.M.); (C.Ó.); (C.S.M.)
- Department of Histopathology, St James’s Hospital, P.O. Box 580, James’s Street, D08 X4RX Dublin, Ireland
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