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Kotoulas SC, Spyratos D, Porpodis K, Domvri K, Boutou A, Kaimakamis E, Mouratidou C, Alevroudis I, Dourliou V, Tsakiri K, Sakkou A, Marneri A, Angeloudi E, Papagiouvanni I, Michailidou A, Malandris K, Mourelatos C, Tsantos A, Pataka A. A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2025; 17:882. [PMID: 40075729 PMCID: PMC11898928 DOI: 10.3390/cancers17050882] [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: 09/15/2024] [Revised: 02/06/2025] [Accepted: 02/25/2025] [Indexed: 03/14/2025] Open
Abstract
According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5-10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans-but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis.
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Affiliation(s)
- Serafeim-Chrysovalantis Kotoulas
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Dionysios Spyratos
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Konstantinos Porpodis
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Kalliopi Domvri
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Afroditi Boutou
- Pulmonary Department General, Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (A.B.); (A.T.)
| | - Evangelos Kaimakamis
- 1st ICU, Medical Informatics Laboratory, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece;
| | - Christina Mouratidou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Ioannis Alevroudis
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Vasiliki Dourliou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Kalliopi Tsakiri
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Agni Sakkou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Alexandra Marneri
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Elena Angeloudi
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Ioanna Papagiouvanni
- 4th Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Anastasia Michailidou
- 2nd Propaedeutic Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Konstantinos Malandris
- 2nd Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Constantinos Mourelatos
- Biology and Genetics Laboratory, Aristotle’s University of Thessaloniki, 54624 Thessaloniki, Greece;
| | - Alexandros Tsantos
- Pulmonary Department General, Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (A.B.); (A.T.)
| | - Athanasia Pataka
- Respiratory Failure Clinic and Sleep Laboratory, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece;
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Wang J, Liu G, Zhou C, Cui X, Wang W, Wang J, Huang Y, Jiang J, Wang Z, Tang Z, Zhang A, Cui D. Application of artificial intelligence in cancer diagnosis and tumor nanomedicine. NANOSCALE 2024; 16:14213-14246. [PMID: 39021117 DOI: 10.1039/d4nr01832j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.
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Affiliation(s)
- Junhao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Guan Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Cheng Zhou
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Xinyuan Cui
- Imaging Department of Rui Jin Hospital, Medical School of Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jiulin Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Yixin Huang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinlei Jiang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhitao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zengyi Tang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Amin Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
| | - Daxiang Cui
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, Henan University, Henan, China
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Liu K, Lin X, Chen X, Chen B, Li S, Li K, Chen H, Li L. Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study. Transl Oncol 2024; 42:101894. [PMID: 38324961 PMCID: PMC10851213 DOI: 10.1016/j.tranon.2024.101894] [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: 09/28/2023] [Revised: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
PURPOSE The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.
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Affiliation(s)
- Kunfeng Liu
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Xiaofeng Lin
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Xiaojuan Chen
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, PR China
| | - Biyun Chen
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Sheng Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, PR China
| | - Huai Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, PR China
| | - Li Li
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China
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