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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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Kanan M, Alharbi H, Alotaibi N, Almasuood L, Aljoaid S, Alharbi T, Albraik L, Alothman W, Aljohani H, Alzahrani A, Alqahtani S, Kalantan R, Althomali R, Alameen M, Mufti A. AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:674. [PMID: 38339425 PMCID: PMC10854661 DOI: 10.3390/cancers16030674] [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: 11/19/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
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Affiliation(s)
- Mohammed Kanan
- Department of Clinical Pharmacy, King Fahad Medical City, Riyadh 12211, Saudi Arabia
| | - Hajar Alharbi
- Department of Medicine, Gdansk Medical University, 80210 Gdansk, Poland
| | - Nawaf Alotaibi
- Department of Clinical Pharmacy, Northern Border University, Rafha 73213, Saudi Arabia
| | - Lubna Almasuood
- Department of Pharmacy, Qassim University, Buraydah 52571, Saudi Arabia
| | - Shahad Aljoaid
- Department of Medicine, University of Tabuk, Tabuk 47911, Saudi Arabia
| | - Tuqa Alharbi
- Department of Medicine, Qassim University, Buraydah 52571, Saudi Arabia
| | - Leen Albraik
- Department of Medicine, Al-Faisal University, Riyadh 12385, Saudi Arabia;
| | - Wojod Alothman
- Department of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31411, Saudi Arabia
| | - Hadeel Aljohani
- Department of Medicine and Surgery, King Abdulaziz University, Jeddah 22230, Saudi Arabia; (H.A.); (R.K.)
| | - Aghnar Alzahrani
- Department of Medicine, Al-Baha University, Al Bahah 65964, Saudi Arabia
| | - Sadeem Alqahtani
- Department of Pharmacy, King Khalid University, Abha 62217, Saudi Arabia
| | - Razan Kalantan
- Department of Medicine and Surgery, King Abdulaziz University, Jeddah 22230, Saudi Arabia; (H.A.); (R.K.)
| | - Raghad Althomali
- Department of Medicine, Taif University, Taif 26311, Saudi Arabia
| | - Maram Alameen
- Department of Medicine, Taif University, Taif 26311, Saudi Arabia
| | - Ahdab Mufti
- Department of Medicine, Ibn Sina National College, Jeddah 22230, Saudi Arabia
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Huang T, Le D, Yuan L, Xu S, Peng X. Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit. PLoS One 2023; 18:e0280606. [PMID: 36701342 PMCID: PMC9879439 DOI: 10.1371/journal.pone.0280606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/04/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUNDS The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making. METHODS Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. RESULTS Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model. CONCLUSION The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.
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Affiliation(s)
- Tianzhi Huang
- Department of Rehabilitation, The Second Affiliated Hospital of Jianghan University, Wuhan, China
| | - Dejin Le
- Department of Respiratory Medicine, People’s Hospital of Daye, The Second Affiliated Hospital of Hubei Polytechnic University, Daye, Hubei, China
| | - Lili Yuan
- Department of Anesthesiology, The Second Affiliated Hospital of Jianghan University, Wuhan, China
| | - Shoujia Xu
- Department of Orthopedics, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, China
- * E-mail: (XP); (SX)
| | - Xiulan Peng
- Department of Oncology, The Second Affiliated Hospital of Jianghan University, Wuhan, China
- * E-mail: (XP); (SX)
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Liu M, Wu J, Wang N, Zhang X, Bai Y, Guo J, Zhang L, Liu S, Tao K. The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis. PLoS One 2023; 18:e0273445. [PMID: 36952523 PMCID: PMC10035910 DOI: 10.1371/journal.pone.0273445] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/03/2023] [Indexed: 03/25/2023] Open
Abstract
Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.
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Affiliation(s)
- Mingsi Liu
- Department of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, China
| | - Jinghui Wu
- College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Nian Wang
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
| | - Xianqin Zhang
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
| | - Yujiao Bai
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
- Non-Coding RNA and Drug Discovery Key Laboratory of Sichuan Province, Chengdu Medical College, Chengdu, Sichuan, China
| | - Jinlin Guo
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lin Zhang
- Department of Pharmacy, Shaoxing people's Hospital, Shaoxing, Zhejiang, China
| | - Shulin Liu
- Department of the First Affiliated Hospital of Chengdu Medical College, Sichuan, China
| | - Ke Tao
- College of Life Science, Sichuan University, Chengdu, Sichuan, China
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Zhu YQ, Liu C, Mo Y, Dong H, Huang C, Duan YN, Tang LL, Chu YY, Qin J. Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography. Br J Radiol 2022; 95:20210768. [PMID: 35262392 PMCID: PMC10996418 DOI: 10.1259/bjr.20210768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency. RESULTS 129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSION The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
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Affiliation(s)
- Yan-qiu Zhu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Chaohui Liu
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Yan Mo
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Ya-ni Duan
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Lei-lei Tang
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Yuan-yuan Chu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Jie Qin
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
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Yang H, Chen H, Zhang G, Li H, Ni R, Yu Y, Zhang Y, Wu Y, Liu H. Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules. BMC Cancer 2022; 22:382. [PMID: 35397524 PMCID: PMC8994303 DOI: 10.1186/s12885-022-09472-w] [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: 07/15/2021] [Accepted: 03/30/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening.
Methods
From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve.
Results
A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS.
Conclusions
CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.
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Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost. Int J Colorectal Dis 2022; 37:1621-1634. [PMID: 35704090 PMCID: PMC9262764 DOI: 10.1007/s00384-022-04157-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/17/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Watch and wait strategy is a safe and effective alternative to surgery in patients with locally advanced rectal cancer (LARC) who have achieved pathological complete response (pCR) after neoadjuvant therapy (NAT); present restaging methods do not meet clinical needs. This study aimed to construct a machine learning (ML) model to predict pCR preoperatively. METHODS LARC patients who received NAT were included to generate an extreme gradient boosting-based ML model to predict pCR. The group was divided into a training set and a tuning set at a 7:3 ratio. The SHapley Additive exPlanations value was used to quantify feature importance. The ML model was compared with a nomogram model developed using independent risk factors identified by conventional multivariate logistic regression analysis. RESULTS Compared with the nomogram model, our ML model improved the area under the receiver operating characteristics from 0.72 to 0.95, sensitivity from 43 to 82.2%, and specificity from 87.1 to 91.6% in the training set, the same trend applied to the tuning set. Neoadjuvant radiotherapy, preoperative carbohydrate antigen 125 (CA125), CA199, carcinoembryonic antigen level, and depth of tumor invasion were significant in predicting pCR in both models. CONCLUSION Our ML model is a potential alternative to the existing assessment tools to conduct triage treatment for patients and provides reference for clinicians in tailoring individual treatment: the watch and wait strategy is used to avoid surgical trauma in pCR patients, and non-pCR patients receive surgical treatment to avoid missing the optimal operation time window.
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Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
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9
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Liu SQ, Ma XB, Song WM, Li YF, Li N, Wang LN, Liu JY, Tao NN, Li SJ, Xu TT, Zhang QY, An QQ, Liang B, Li HC. Using a risk model for probability of cancer in pulmonary nodules. Thorac Cancer 2021; 12:1881-1889. [PMID: 33973725 PMCID: PMC8201526 DOI: 10.1111/1759-7714.13991] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
Background Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. Methods A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. Results The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. Conclusions Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.
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Affiliation(s)
- Si-Qi Liu
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiao-Bin Ma
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wan-Mei Song
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi-Fan Li
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ning Li
- Shandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Li-Na Wang
- Department of Medical Imaging, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jin-Yue Liu
- Department of Intensive Care Unit, Shandong Provincial Third Hospital, Jinan, China
| | - Ning-Ning Tao
- Department of Respiratory and Critical Care Medicine, Beijing Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi-Jin Li
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ting-Ting Xu
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qian-Yun Zhang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qi-Qi An
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bin Liang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huai-Chen Li
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
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