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Bardoni C, Spaggiari L, Bertolaccini L. Artificial intelligence in lung cancer. ANNALS OF TRANSLATIONAL MEDICINE 2024; 12:79. [PMID: 39118944 PMCID: PMC11304431 DOI: 10.21037/atm-22-2918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 01/12/2024] [Indexed: 08/10/2024]
Affiliation(s)
- Claudia Bardoni
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
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Chapla D, Chorya HP, Ishfaq L, Khan A, Vr S, Garg S. An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review. Cureus 2024; 16:e66688. [PMID: 39268329 PMCID: PMC11390952 DOI: 10.7759/cureus.66688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 08/11/2024] [Indexed: 09/15/2024] Open
Abstract
Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.
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Affiliation(s)
- Deep Chapla
- Medicine, Jiangsu University, Zhenjiang, CHN
| | | | - Lyluma Ishfaq
- Medicine, Directorate of Health Services Kashmir, Srinagar, IND
| | - Afrasayab Khan
- Internal Medicine, Central Michigan University College of Medicine, Saginaw, USA
| | - Subrahmanyan Vr
- Internal Medicine Pediatrics, Armed Forces Medical College, Pune, IND
| | - Sheenam Garg
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
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Hardavella G, Frille A, Chalela R, Sreter KB, Petersen RH, Novoa N, de Koning HJ. How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape? Eur Respir Rev 2024; 33:230232. [PMID: 38925794 PMCID: PMC11216686 DOI: 10.1183/16000617.0232-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/16/2024] [Indexed: 06/28/2024] Open
Abstract
INTRODUCTION Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice. METHODS This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article. RESULTS Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems. CONCLUSIONS Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.
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Affiliation(s)
- Georgia Hardavella
- 4th-9th Department of Respiratory Medicine, "Sotiria" Athens' Chest Diseases Hospital, Athens, Greece
| | - Armin Frille
- Department of Respiratory Medicine, University of Leipzig, Leipzig, Germany
| | - Roberto Chalela
- Department of Respiratory Medicine: Lung Cancer and Endoscopy Unit, Hospital del Mar - Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Katherina B Sreter
- Department of Pulmonology, University Hospital Centre "Sestre Milosrdnice", Zagreb, Croatia
| | - Rene H Petersen
- Department of Cardiothoracic Surgery, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Nuria Novoa
- Department of Thoracic Surgery, University Hospital Puerta de Hierro-Majadahonda, Madrid, Spain
| | - Harry J de Koning
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
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Park JA, Pham D, Yalamanchili S, Twardus S, Suzuki K. Developing technologies and areas of interest in lung cancer screening adjuncts. J Thorac Dis 2024; 16:1552-1564. [PMID: 38505010 PMCID: PMC10944753 DOI: 10.21037/jtd-23-1326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/22/2023] [Indexed: 03/21/2024]
Abstract
Lung cancer remains the leading cause of cancer mortality. Screening guidelines have been implemented in the past decade to aid in earlier detection of at-risk groups. Nevertheless, computed tomography (CT) scans, the principal screening modality in use today, are still low yield, with 3.6% of lung cancer confirmed amongst 39.1% of lesions detected over a 3-year period. They also carry relatively high false positive rates, between 9% and 27%, which can bear unnecessary financial and emotional costs to patients. As such, research efforts have been dedicated to the development of lung cancer screening adjuncts to improve detection reliability. We herein review several emerging technologies in this specific arena and their efficacy. These include plasma markers (microDNA, DNA methylation, and tumor-associated antibodies), breath/sputum biomarkers [volatile organic compounds (VOCs) and exhaled breath condensate (EBC)], proteomics, metabolomics, and machine learning, such as radiomics technology. We find that, across the board, they offer promising results in terms of non-invasive diagnostics, genetic sequencing for higher-risk individuals, and accessibility for a diverse cohort of patients. While these screening adjuncts are unlikely to completely replace the current standard of care at the moment, continued research into these technologies is crucial to improve and personalize the identification, treatment, and outcome of lung cancer patients in the near future.
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Affiliation(s)
- Ju Ae Park
- Department of Surgery, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - Duy Pham
- University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Sriya Yalamanchili
- Department of Surgery, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - Shaina Twardus
- University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kei Suzuki
- Department of Surgery, Inova Fairfax Medical Campus, Falls Church, VA, USA
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Zhuan B, Ma HH, Zhang BC, Li P, Wang X, Yuan Q, Yang Z, Xie J. Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study. Front Oncol 2023; 13:1158948. [PMID: 37576878 PMCID: PMC10419203 DOI: 10.3389/fonc.2023.1158948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/03/2023] [Indexed: 08/15/2023] Open
Abstract
Background Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient's disease and routine clinical data. Methods Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People's Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. Results 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. Conclusion The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.
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Affiliation(s)
- Bing Zhuan
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Hong-Hong Ma
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Bo-Chao Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Ping Li
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Xi Wang
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Qun Yuan
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Zhao Yang
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jun Xie
- Department of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
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Arandia N, Garate JI, Mabe J. Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead. SENSORS (BASEL, SWITZERLAND) 2022; 22:9917. [PMID: 36560284 PMCID: PMC9781231 DOI: 10.3390/s22249917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/03/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.
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Affiliation(s)
- Nerea Arandia
- TEKNIKER, Basque Research and Technology Alliance (BRTA), 20600 Eibar, Spain
| | - Jose Ignacio Garate
- Department of Electronics Technology, University of the Basque Country (UPV/EHU), 48080 Bilbao, Spain
| | - Jon Mabe
- TEKNIKER, Basque Research and Technology Alliance (BRTA), 20600 Eibar, Spain
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Schmid-Bindert G, Vogel-Claussen J, Gütz S, Fink J, Hoffmann H, Eichhorn ME, Herth FJ. Incidental Pulmonary Nodules - What Do We Know in 2022. Respiration 2022; 101:1024-1034. [PMID: 36228594 PMCID: PMC9945197 DOI: 10.1159/000526818] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/10/2022] [Indexed: 11/19/2022] Open
Abstract
Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, and early LC diagnosis can significantly improve outcomes and survival rates in affected patients. Implementation of LC screening programs using low-dose computed tomography CT in high-risk subjects aims to detect LC as early as possible, but so far, adoption of screening programs into routine clinical care has been very slow. In recent years, the use of CT has significantly increased the rate of incidentally detected pulmonary nodules. Although most of those incidental pulmonary nodules (IPNs) are benign, some of them represent early-stage LC. Given the large number of IPNs detected in the range of several millions each year, this represents an additional, maybe even larger, opportunity to drive stage shift in LC diagnosis, next to LC screening programs. Comprehensive evaluation and targeted work-up of IPNs are mandatory to identify the malignant nodules from the crowd, and several guidelines provide radiologists and physicians' guidance on IPN assessment and management. However, IPNs still seem to be inadequately processed due to various reasons including insufficient reporting in the radiological report, missing communication between stakeholders, absence of patient tracking systems, and uncertainty regarding responsibilities for the IPN management. In recent years, several approaches such as lung nodule programs, patient tracking software, artificial intelligence, and communication software were introduced into clinical practice to address those shortcomings. This review evaluates the current situation of IPN management and highlights recent developments in process improvement to achieve first steps toward stage shift in LC diagnosis.
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Affiliation(s)
- Gerald Schmid-Bindert
- Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AstraZeneca GmbH, Hamburg, Germany
| | - Jens Vogel-Claussen
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research, Hannover, Germany
| | - Sylvia Gütz
- Department of Pneumology, Cardiology, Endocrinology, Diabetology and General Internal Medicine, Sankt Elisabeth Hospital, Leipzig, Germany
| | | | - Hans Hoffmann
- Section for Thoracic Surgery, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Martin E. Eichhorn
- Department of Thoracic Surgery, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Felix J.F. Herth
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Pulmonology, and Critical Care Medicine, Thoraxklinik Universitätsklinikum Heidelberg, Heidelberg, Germany
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Lyu PF, Wang Y, Meng QX, Fan PM, Ma K, Xiao S, Cao XC, Lin GX, Dong SY. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Front Oncol 2022; 12:955668. [PMID: 36212413 PMCID: PMC9535738 DOI: 10.3389/fonc.2022.955668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
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Affiliation(s)
- Peng-fei Lyu
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yu Wang
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Qing-Xiang Meng
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping-ming Fan
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ke Ma
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Sha Xiao
- International School of Public Health and One Health, Heinz Mehlhorn Academician Workstation, Hainan Medical University, Haikou, China
| | - Xun-chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Guang-Xun Lin
- Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
| | - Si-yuan Dong
- Thoracic Department, The First Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
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Guo Q, Liu L, Chen Z, Fan Y, Zhou Y, Yuan Z, Zhang W. Current treatments for non-small cell lung cancer. Front Oncol 2022; 12:945102. [PMID: 36033435 PMCID: PMC9403713 DOI: 10.3389/fonc.2022.945102] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/06/2022] [Indexed: 12/12/2022] Open
Abstract
Despite improved methods of diagnosis and the development of different treatments, mortality from lung cancer remains surprisingly high. Non-small cell lung cancer (NSCLC) accounts for the large majority of lung cancer cases. Therefore, it is important to review current methods of diagnosis and treatments of NSCLC in the clinic and preclinic. In this review, we describe, as a guide for clinicians, current diagnostic methods and therapies (such as chemotherapy, chemoradiotherapy, targeted therapy, antiangiogenic therapy, immunotherapy, and combination therapy) for NSCLC.
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Affiliation(s)
- Qianqian Guo
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou University, Zhengzhou, China
| | - Liwei Liu
- Department of Pharmacy, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zelong Chen
- Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Artificial Intelligence and IoT Smart Medical Engineering Research Center of Henan Province, Zhengzhou, China
| | - Yannan Fan
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou University, Zhengzhou, China
| | - Yang Zhou
- Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
| | - Ziqiao Yuan
- Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, School of Pharmaceutical Sciences, Zhengzhou University, State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou University, Zhengzhou, China
- *Correspondence: Wenzhou Zhang, ; Ziqiao Yuan,
| | - Wenzhou Zhang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou University, Zhengzhou, China
- *Correspondence: Wenzhou Zhang, ; Ziqiao Yuan,
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Maller B, Tanvetyanon T. Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review. Cureus 2022; 14:e27309. [PMID: 36042989 PMCID: PMC9410538 DOI: 10.7759/cureus.27309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer screening by low-dose computed tomography (LDCT) can save lives. Nevertheless, the test suffers from low accuracy. Improving its accuracy will reduce unnecessary invasive procedures and allow lung cancer treatment to be delivered sooner. This review describes the principles, advantages, and disadvantages of selected emerging modalities potentially useful to improve the accuracy of LDCT. A literature search was conducted using PubMed and Google scholar for relevant publications. We identified four key emerging approaches: radiomics, breath analysis, urine test, and blood test. Radiomics, which uses a computer program to extract various radiological features from radiographic images, holds the potential to improve the accuracy of LDCT. However, to date, there remains no adequately validated system. Breath analysis and urine tests represent a noninvasive and convenient means of screening by detecting substances such as volatile organic compounds associated with lung cancer. However, the results can be confounded by diets, medications, and concurrent medical conditions. Finally, a blood test to screen for protein biomarkers or methylation profiles such as Galleri® has high specificity. However, its sensitivity is low, especially for detecting early-stage lung cancer. Furthermore, the cost for mass public use can be significant. Based on our review, blood tests may have potential for future clinical utility. Its high specificity may be useful to rule in a suspicious lung nodule as malignant, so that other additional tests can be omitted. Data from a well-designed clinical trial will be needed to understand the clinical utility of this strategy.
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Affiliation(s)
- Bradley Maller
- Internal Medicine, Virginia Commonwealth University, Richmond, USA
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11
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[Chinese Experts Consensus on Artificial Intelligence Assisted Management for
Pulmonary Nodule (2022 Version)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:219-225. [PMID: 35340198 PMCID: PMC9051301 DOI: 10.3779/j.issn.1009-3419.2022.102.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Low-dose computed tomography (CT) for lung cancer screening has been proven to reduce lung cancer deaths in the screening group compared with the control group. The increasing number of pulmonary nodules being detected by CT scans significantly increase the workload of the radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of pulmonary nodule discrimination and has been tested in preliminary studies for nodule management. As more and more artificial AI products are commercialized, the consensus statement has been organized in a collaborative effort by Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation to aid clinicians in the application of AI-assisted management for pulmonary nodules.
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Mercut R, Mercut I, Glodeanu A, Ionescu M, Turcu A, Stefanescu‑Dima A, Ciurea M. Eyelid carcinomas: Tumor aggressiveness tendencies for smokers compared to non‑smokers. Exp Ther Med 2022; 23:234. [PMID: 35222711 PMCID: PMC8815059 DOI: 10.3892/etm.2022.11159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 09/29/2021] [Indexed: 11/12/2022] Open
Abstract
During the past few years, several studies have demonstrated that head and neck carcinomas present more aggressive forms for smokers, relative to non-smokers. Our aim was to investigate the tumor aggressiveness for patients with eyelid carcinomas, in relation to tobacco consumption, as well as other demographic and clinical data. For 98 patients with eyelid carcinomas, we studied the relationship between the duration of their symptoms and their tumor stage at first diagnosis, trying to determine potential correlations with smoking status and several other clinical parameters. Our data revealed that, for the same duration of symptoms, tobacco consumers tended to have higher tumor stages, which did not correlate with other variables. For early diagnosed tumors, within the first year of symptoms, smokers presented 6.044 times higher odds to exhibit more advanced tumor stages, compared to non-smokers, and this value decreased to 4.501, up to 5 years of the presence of symptoms (P<0.05). We also noted that, for smokers, an increased age was associated with increased tumor stages, which was opposed to non-smokers, regardless of their symptom duration [average odds ratio (OR) 1.122, P<0.05]. Tumor aggressiveness was therefore associated with tobacco consumption, leading to an increased risk of developing more aggressive forms of eyelid carcinomas for smokers, compared to non-smokers.
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Affiliation(s)
- Razvan Mercut
- Department of Plastic Surgery and Reconstructive Microsurgery, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Irina Mercut
- Department of Ophthalmology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Adina Glodeanu
- Department of Internal Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Mihaela Ionescu
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Adina Turcu
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Alin Stefanescu‑Dima
- Department of Ophthalmology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Marius Ciurea
- Department of Plastic Surgery and Reconstructive Microsurgery, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
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