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Changhez J, James S, Jamala F, Khan S, Khan MZ, Gul S, Zainab I. Evaluating the Efficacy and Accuracy of AI-Assisted Diagnostic Techniques in Endometrial Carcinoma: A Systematic Review. Cureus 2024; 16:e60973. [PMID: 38910646 PMCID: PMC11193879 DOI: 10.7759/cureus.60973] [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] [Accepted: 05/23/2024] [Indexed: 06/25/2024] Open
Abstract
Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.
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Affiliation(s)
| | - Simran James
- Gynecology, Rehman Medical Institute, Peshawar, PAK
| | - Fazilat Jamala
- Obstetrics and Gynecology, Northwest General Hospital and Research Center, Peshawar, PAK
| | - Shandana Khan
- General Surgery, Medical Teaching Institution (MTI) - Hayatabad Medical Complex, Peshawar, PAK
| | | | - Sana Gul
- Gynecology, Rehman Medical Institute, Peshawar, PAK
| | - Irta Zainab
- Gynecology, Medicsi Hospital, Islamabad, PAK
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2
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Lai J, Rao B, Tian Z, Zhai QJ, Wang YL, Chen SK, Huang XT, Zhu HL, Cui H. Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model. Comput Biol Med 2024; 172:108243. [PMID: 38484694 DOI: 10.1016/j.compbiomed.2024.108243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 02/21/2024] [Accepted: 02/29/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVE This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women. METHODS Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing to identify 57 relevant characteristics from these irregular clinical data. To predict the presence of postmenopausal endometrial non-benign lesions, including atypical hyperplasia and endometrial cancer, we employed various models such as eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), as well as two ensemble models. Additionally, a test set was performed on an independent dataset consisting of 152 patients. The performance evaluation of all models was based on metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1 score. RESULTS The RF model demonstrated superior recognition capabilities for patients with non-benign lesions compared to other models. In the test set, it attained a sensitivity of 88.1% and an AUC of 0.93, surpassing all alternative models evaluated in this study. Furthermore, we have integrated this model into our hospital's Clinical Decision Support System (CDSS) and implemented it within the outpatient electronic medical record system to continuously validate and optimize its performance. CONCLUSIONS We have trained a model and deployed a system with high discriminatory power that may provide a novel approach to identify patients at higher risk of postmenopausal endometrial non-benign lesions who may benefit from more tailored screening and clinical intervention.
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Affiliation(s)
- Jin Lai
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
| | - Bo Rao
- Peking University Chongqing Research Institute of Big Data, China
| | - Zhao Tian
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
| | - Qing-Jie Zhai
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
| | - Yi-Ling Wang
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
| | - Si-Kai Chen
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
| | - Xin-Ting Huang
- Peking University Chongqing Research Institute of Big Data, China
| | - Hong-Lan Zhu
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China.
| | - Heng Cui
- Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China
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Butt SR, Soulat A, Lal PM, Fakhor H, Patel SK, Ali MB, Arwani S, Mohan A, Majumder K, Kumar V, Tejwaney U, Kumar S. Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer. Ann Med Surg (Lond) 2024; 86:1531-1539. [PMID: 38463097 PMCID: PMC10923372 DOI: 10.1097/ms9.0000000000001733] [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: 06/26/2023] [Accepted: 01/08/2024] [Indexed: 03/12/2024] Open
Abstract
Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.
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Affiliation(s)
| | | | | | | | | | | | | | - Anmol Mohan
- Karachi Medical and Dental College, Karachi, Pakistan
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4
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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5
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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Zheng T, Pan J, Du D, Liang X, Yi H, Du J, Wu S, Liu L, Shi G. Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators. Future Oncol 2023; 19:587-601. [PMID: 37097730 DOI: 10.2217/fon-2022-0631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
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Affiliation(s)
- Tao Zheng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Dan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Xin Liang
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Huiling Yi
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Juan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Shuo Wu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Lanxiang Liu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
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7
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Interpretable prognostic modeling of endometrial cancer. Sci Rep 2022; 12:21543. [PMID: 36513790 PMCID: PMC9747711 DOI: 10.1038/s41598-022-26134-w] [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: 09/07/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Endometrial carcinoma (EC) is one of the most common gynecological cancers in the world. In this work we apply Cox proportional hazards (CPH) and optimal survival tree (OST) algorithms to the retrospective prognostic modeling of disease-specific survival in 842 EC patients. We demonstrate that linear CPH models are preferred for the EC risk assessment based on clinical features alone, while interpretable, non-linear OST models are favored when patient profiles can be supplemented with additional biomarker data. We show how visually interpretable tree models can help generate and explore novel research hypotheses by studying the OST decision path structure, in which L1 cell adhesion molecule expression and estrogen receptor status are correctly indicated as important risk factors in the p53 abnormal EC subgroup. To aid further clinical adoption of advanced machine learning techniques, we stress the importance of quantifying model discrimination and calibration performance in the development of explainable clinical prediction models.
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8
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On the use of spectroscopy, prediction machines and cybernetics for an affordable and proactive care approach for endometrial cancer. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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9
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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Bhardwaj V, Sharma A, Parambath SV, Gul I, Zhang X, Lobie PE, Qin P, Pandey V. Machine Learning for Endometrial Cancer Prediction and Prognostication. Front Oncol 2022; 12:852746. [PMID: 35965548 PMCID: PMC9365068 DOI: 10.3389/fonc.2022.852746] [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: 01/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
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Affiliation(s)
- Vipul Bhardwaj
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Arundhiti Sharma
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | | | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Xi Zhang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peter E. Lobie
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peiwu Qin
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Vijay Pandey
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Vijay Pandey,
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Research on the Guiding Effect of CTCs on Postoperative Adjuvant Therapy for Patients with Early Stage Endometrial Cancer. JOURNAL OF ONCOLOGY 2022; 2022:4327977. [PMID: 35685426 PMCID: PMC9174000 DOI: 10.1155/2022/4327977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/17/2022] [Indexed: 11/17/2022]
Abstract
Endometrial tumor has increased in occurrence and fatality in China during the last 11 years, owing to inconsistent hormone use and modifications in people living surrounding and lifestyles. One of the three main gynaecological tumors is endometrial carcinoma (EC). Longer waiting duration of operation was linked to a lower chance of sustainability in endometrial tumor patients. Despite the great sustainability rate of endometrial tumor, only around 46 percent of patients undergo adjuvant treatment. Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and circulating free DNA (cfDNA) are the most investigated tumor noninvasive indicators. These circulating biomarkers are important in the knowledge of metastasis and tumorigenesis, and they could help researchers comprehend how cancer dynamics evolve throughout the therapy and illness development. In patients with solid tumor, the existence of circulating tumor cells (CTCs) in the peripheral blood is linked to a weak prognosis. However, there is a scarcity of information on how to detect CTCs in endometrial cancer (EC). Hence, in this paper, we analyze the guiding effect of CTCs on postoperative adjuvant treatment for sufferers with initial phase endometrial tumor using multi-cox regression method. The dataset is selected and the blood samples are collected using plasma separation method. The CTC is detected using differential diagnosis. The morphology and biological features, Immunocytochemistry, Genomic analysis, Transcriptomic analysis, Proteomic analysis, and molecular analysis are performed and the outcomes are evaluated.
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12
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Wang W, Xu Y, Yuan S, Li Z, Zhu X, Zhou Q, Shen W, Wang S. Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method. Front Med (Lausanne) 2022; 9:851890. [PMID: 35308550 PMCID: PMC8931475 DOI: 10.3389/fmed.2022.851890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 01/28/2022] [Indexed: 11/24/2022] Open
Abstract
Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.
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Affiliation(s)
- Wenwen Wang
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Suzhen Yuan
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Zhiying Li
- Department of Obstetrics and Gynecology, Affiliated Renhe Hospital of China Three Gorges University, Yichang, China
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Qin Zhou
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Wenfeng Shen
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- Wenfeng Shen
| | - Shixuan Wang
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Shixuan Wang
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Hart GR, Yan V, Nartowt BJ, Roffman DA, Stark G, Muhammad W, Deng J. Statistical biopsy: An emerging screening approach for early detection of cancers. Front Artif Intell 2022; 5:1059093. [PMID: 36744110 PMCID: PMC9895959 DOI: 10.3389/frai.2022.1059093] [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: 09/30/2022] [Accepted: 12/14/2022] [Indexed: 01/22/2023] Open
Abstract
Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.
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Affiliation(s)
- Gregory R. Hart
- Institute for Disease Modeling, Global Health Division, Bill and Melinda Gates Foundation, Seattle, WA, United States
| | - Vanessa Yan
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States
| | | | - David A. Roffman
- Research Partners, Sun Nuclear Corporation (Mirion Technologies Inc.), Melbourne, FL, United States
| | - Gigi Stark
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States
| | - Wazir Muhammad
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States
- *Correspondence: Jun Deng ✉
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Kim BW, Choi MC, Kim MK, Lee JW, Kim MT, Noh JJ, Park H, Jung SG, Joo WD, Song SH, Lee C. Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers. Cancers (Basel) 2021; 13:cancers13225670. [PMID: 34830824 PMCID: PMC8616351 DOI: 10.3390/cancers13225670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers. Abstract To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers.
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Affiliation(s)
- Byung Wook Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Min Chul Choi
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Min Kyu Kim
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Korea
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Jeong-Won Lee
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Min Tae Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Joseph J. Noh
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Hyun Park
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Sang Geun Jung
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Won Duk Joo
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Seung Hun Song
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Chan Lee
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
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15
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Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med 2021; 120:102164. [PMID: 34629152 DOI: 10.1016/j.artmed.2021.102164] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/28/2021] [Accepted: 08/31/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers. METHODS A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy. RESULTS Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review. CONCLUSIONS In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.
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Affiliation(s)
- Munetoshi Akazawa
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
| | - Kazunori Hashimoto
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
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