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Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.
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Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
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Li S, Li M, Wu J, Li Y, Han J, Song Y, Cao W, Zhou X. Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia. J Transl Med 2024; 22:405. [PMID: 38689321 PMCID: PMC11061938 DOI: 10.1186/s12967-024-05131-9] [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: 07/23/2023] [Accepted: 03/26/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. METHODS We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. RESULTS Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. CONCLUSION GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.
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Affiliation(s)
- Shengjie Li
- Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Meiyan Li
- Department of Ophthalmology and Optometry, Fudan University Eye Ear Nose and Throat Hospital, Shanghai, China
- NHC Key Laboratory of Myopia (Fudan University), Shanghai, China
- Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China
- Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China
- Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care, Shanghai, China
| | - Jianing Wu
- Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yingzhu Li
- Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jianping Han
- Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yunxiao Song
- Department of Clinical Laboratory, Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.
| | - Wenjun Cao
- Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Xingtao Zhou
- Department of Ophthalmology and Optometry, Fudan University Eye Ear Nose and Throat Hospital, Shanghai, China.
- NHC Key Laboratory of Myopia (Fudan University), Shanghai, China.
- Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China.
- Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China.
- Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care, Shanghai, China.
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Shurin MR, Wheeler SE. Clinical Significance of Uncommon, Non-Clinical, and Novel Autoantibodies. Immunotargets Ther 2024; 13:215-234. [PMID: 38686351 PMCID: PMC11057673 DOI: 10.2147/itt.s450184] [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] [Received: 11/16/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Autoantibodies are a common mark of autoimmune reaction and their identification in the patients' serum, cerebrospinal fluid, or tissues is generally believed to represent diagnostic or prognostic biomarkers of autoimmune diseases or autoinflammatory conditions. Traditionally, autoantibody testing is an important part of the clinical examination of suspected patients, and in the absence of reliable T cell tests, characterization of autoantibody responses might be suitable in finding causes of specific autoimmune responses, their strength, and sometimes commencement of autoimmune disease. Autoantibodies are also useful for prognostic stratification in clinically diverse groups of patients if checked repeatedly. Antibody discoveries are continuing, with important consequences for verifying autoimmune mechanisms, diagnostic feasibility, and clinical management. Adding newly identified autoantibody-autoantigen pairs to common clinical laboratory panels should help upgrade and harmonize the identification of systemic autoimmune rheumatic disorders and other autoimmune conditions. Herein, we aim to summarize our current knowledge of uncommon and novel autoantibodies in the context of discussing their validation, diagnostic practicability, and clinical relevance. The regular updates within the field are important and well justified.
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Affiliation(s)
- Michael R Shurin
- Division of Clinical Immunopathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sarah E Wheeler
- Division of Clinical Immunopathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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Ahmed S, Kapadia A, Ahmed Siddiqui I, Shaukat A, Khan MD, Alam Khan MQ, Iqbal S, Abbas G, Zubairi AM, Nawaz Naqvi SH, Sadiqa A, Jafri L, Siddiqui I. Artificial Intelligence - Perception of Clinical Laboratories' Technical Staff a Nationwide Multicentre Survey in Pakistan. EJIFCC 2024; 35:23-30. [PMID: 38706736 PMCID: PMC11063789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Introduction As Artificial Intelligence (AI) technology continues to assimilate into various industries, there is a huge scope in the healthcare industry specifically in clinical laboratories. The perspective of the laboratory professionals can give valuable insight on the ideal path to take for AI implementation. Methods The study utilized a cross-sectional survey design and was conducted at the section of Chemical Pathology, Department of Pathology and Laboratory Medicine, the Aga Khan University (AKU), Karachi, Pakistan in collaboration with Consultant Pathologists of 9 clinical laboratories associated with teaching hospitals across Pakistan from October-November 2023. The survey was for a duration of 2 weeks and was circulated to all working laboratory technical staff after informed consent. Results A total of 351 responses were received, of which 342 (male=146, female=196) responses were recorded after exclusion. Respondents ranged from technologists, faculty, residents, and coordinators, and were from different sections (chemical pathology, microbiology, haematology, histopathology, POCT). Out of the total 312 (91.2%) of respondents stated that they were at least somewhat familiar with AI technology. Experts in AI were only 2.0% (n=7) of all respondents, but 90% (n=6) of these were < 30 years old. 76.3% (n=261) of the respondents felt the need to implement more AI technology in the laboratories, with time saving (26.1%) and improving performances of tests (17.7%) cited to be the greatest benefits of AI. Security concerns (n=144) and a fear of decreasing personal touch (n=143) were the main concerns of the respondents while the younger employees had an increased fear of losing their jobs. 76.3% were in favour of an increase in AI usage in the laboratories. Conclusion This study highlights a favourable perspective among laboratory professionals, acknowledging the potential of AI to enhance both the efficiency and quality of laboratory practices. However, it underscores the importance of addressing their concerns in the thoughtful implementation of this emerging technology.
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Affiliation(s)
- Sibtain Ahmed
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
| | - Aqueel Kapadia
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
| | - Imran Ahmed Siddiqui
- Department of Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Asma Shaukat
- Department of Chemical Pathology, Quaid-e-Azam Medical College, Bahawalpur, Pakistan
| | | | - Muhammad Qaiser Alam Khan
- Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology, Rawalpindi, Pakistan
| | - Sahar Iqbal
- Department of Pathology, Dow International Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Ghazanfar Abbas
- Department of Chemical Pathology, Shifa International Hospital, Islamabad, Pakistan
| | - Adnan Mustafa Zubairi
- Clinical Laboratories - Outreach: Indus Hospital & Health Network, Karachi, Pakistan
| | - Syed Haider Nawaz Naqvi
- Department of Chemical Pathology, Sindh Institute of Urology and Transplantation, Karachi, Pakistan
| | - Ayesha Sadiqa
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
| | - Lena Jafri
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
| | - Imran Siddiqui
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
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Jiang Y, Dang Y, Wu Q, Yuan B, Gao L, You C. Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models. Front Neurol 2024; 15:1366307. [PMID: 38601342 PMCID: PMC11004235 DOI: 10.3389/fneur.2024.1366307] [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: 01/06/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Objective Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. Methods In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models. Results Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969). Conclusion In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.
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Affiliation(s)
- Yao Jiang
- Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Yingqiang Dang
- Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Qian Wu
- Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Boyao Yuan
- Department of Neurology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Lina Gao
- Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Chongge You
- Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China
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Jafri L, Farooqui AJ, Grant J, Omer U, Gale R, Ahmed S, Khan AH, Siddiqui I, Ghani F, Majid H. Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices. BMC MEDICAL EDUCATION 2024; 24:170. [PMID: 38389053 PMCID: PMC10882878 DOI: 10.1186/s12909-024-05078-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/21/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is gradually transforming the practises of healthcare providers. Over the last two decades, the advent of AI into numerous aspects of pathology has opened transformative possibilities in how we practise laboratory medicine. Objectives of this study were to explore how AI could impact the clinical practices of professionals working in Clinical Chemistry laboratories, while also identifying effective strategies in medical education to facilitate the required changes. METHODS From March to August 2022, an exploratory qualitative study was conducted at the Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan, in collaboration with Keele University, Newcastle, United Kingdom. Semi-structured interviews were conducted to collect information from diverse group of professionals working in Clinical Chemistry laboratories. All interviews were audio recorded and transcribed verbatim. They were asked what changes AI would involve in the laboratory, what resources would be necessary, and how medical education would assist them in adapting to the change. A content analysis was conducted, resulting in the development of codes and themes based on the analyzed data. RESULTS The interviews were analysed to identify three primary themes: perspectives and considerations for AI adoption, educational and curriculum adjustments, and implementation techniques. Although the use of diagnostic algorithms is currently limited in Pakistani Clinical Chemistry laboratories, the application of AI is expanding. All thirteen participants stated their reasons for being hesitant to use AI. Participants stressed the importance of critical aspects for effective AI deployment, the need of a collaborative integrative approach, and the need for constant horizon scanning to keep up with AI developments. CONCLUSIONS Three primary themes related to AI adoption were identified: perspectives and considerations, educational and curriculum adjustments, and implementation techniques. The study's findings give a sound foundation for making suggestions to clinical laboratories, scientific bodies, and national and international Clinical Chemistry and laboratory medicine organisations on how to manage pathologists' shifting practises because of AI.
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Affiliation(s)
- Lena Jafri
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan.
| | - Arsala Jameel Farooqui
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Janet Grant
- Centre for Medical Education in Context [CenMEDIC], CenMEDIC, 27 Church Street, TW12 2EB, Hampton, Middlesex, UK
| | | | - Rodney Gale
- Centre for Medical Education in Context [CenMEDIC], CenMEDIC, 27 Church Street, TW12 2EB, Hampton, Middlesex, UK
| | - Sibtain Ahmed
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Aysha Habib Khan
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Imran Siddiqui
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Farooq Ghani
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Hafsa Majid
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
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Ong KTI, Kwon T, Jang H, Kim M, Lee CS, Byeon SH, Kim SS, Yeo J, Choi EY. Multitask Deep Learning for Joint Detection of Necrotizing Viral and Noninfectious Retinitis From Common Blood and Serology Test Data. Invest Ophthalmol Vis Sci 2024; 65:5. [PMID: 38306107 PMCID: PMC10851173 DOI: 10.1167/iovs.65.2.5] [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: 06/15/2023] [Accepted: 01/09/2024] [Indexed: 02/03/2024] Open
Abstract
Purpose Necrotizing viral retinitis is a serious eye infection that requires immediate treatment to prevent permanent vision loss. Uncertain clinical suspicion can result in delayed diagnosis, inappropriate administration of corticosteroids, or repeated intraocular sampling. To quickly and accurately distinguish between viral and noninfectious retinitis, we aimed to develop deep learning (DL) models solely using noninvasive blood test data. Methods This cross-sectional study trained DL models using common blood and serology test data from 3080 patients (noninfectious uveitis of the posterior segment [NIU-PS] = 2858, acute retinal necrosis [ARN] = 66, cytomegalovirus [CMV], retinitis = 156). Following the development of separate base DL models for ARN and CMV retinitis, multitask learning (MTL) was employed to enable simultaneous discrimination. Advanced MTL models incorporating adversarial training were used to enhance DL feature extraction from the small, imbalanced data. We evaluated model performance, disease-specific important features, and the causal relationship between DL features and detection results. Results The presented models all achieved excellent detection performances, with the adversarial MTL model achieving the highest receiver operating characteristic curves (0.932 for ARN and 0.982 for CMV retinitis). Significant features for ARN detection included varicella-zoster virus (VZV) immunoglobulin M (IgM), herpes simplex virus immunoglobulin G, and neutrophil count, while for CMV retinitis, they encompassed VZV IgM, CMV IgM, and lymphocyte count. The adversarial MTL model exhibited substantial changes in detection outcomes when the key features were contaminated, indicating stronger causality between DL features and detection results. Conclusions The adversarial MTL model, using blood test data, may serve as a reliable adjunct for the expedited diagnosis of ARN, CMV retinitis, and NIU-PS simultaneously in real clinical settings.
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Affiliation(s)
- Kai Tzu-iunn Ong
- Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea
| | - Taeyoon Kwon
- Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea
| | - Harok Jang
- Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea
| | - Min Kim
- Department of Ophthalmology, Institute of Vision Research, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Christopher Seungkyu Lee
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Suk Ho Byeon
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Soo Kim
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinyoung Yeo
- Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea
| | - Eun Young Choi
- Department of Ophthalmology, Institute of Vision Research, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
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Zhang X, Ren H, Tian J, Yang C, Luo H. Usefulness of baseline immature reticulocyte fraction to mature reticulocyte fraction ratio (IMR) as A prognostic predictor for patients with small cell lung cancer. Heliyon 2024; 10:e23830. [PMID: 38192754 PMCID: PMC10772623 DOI: 10.1016/j.heliyon.2023.e23830] [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: 04/29/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024] Open
Abstract
Background Small cell lung cancer (SCLC) has a strong invasive ability and a high degree of malignancy, so accurate prognosis prediction is crucial for making the most favorable treatment decision.Unfortunately, there is a scarcity of prognostic indicators specific to SCLC. Reticulocyte levels in blood parameters have been linked to the prognosis of various malignancies. Given SCLC's aggressive characteristics, identifying reliable prognostic markers, such as reticulocyte counts, becomes pivotal in enhancing prognostic accuracy and guiding effective therapeutic strategies. Objective This study aimed to evaluate the predictive power of the immature reticulocyte fraction (IRF) to mature reticulocyte fraction (MRF) ratio (IMR) for survival outcomes in patients with SCLC. Materials and methods A retrospective analysis was conducted on 192 patients with small cell lung cancer (SCLC). The median values of various prognostic indicators, such as IMR, IRF, MRF, reticulocyte count (RET), SII (systemic immune-inflammatory index), were utilized as cutoff points, categorizing patients into high and low groups. The Kaplan-Meier method, univariate, multivariate analyses Cox regression, and C-index were used to analyze the prognostic factors for overall survival (OS). Results In our cohort, 138 (71.9 %) were male, 119 (62 %) were smokers, and 82 (57.3 %) were older than 60 years old. The median survival time was 18.15 months.Higher mortality was observed in the high IMR and high IRF groups, while the high MRF group exhibited lower mortality. At the same time, mortality was lower in the high MRF group. Univariate analysis showed that smoking history (P = 0.006), tumor stage (P = 0.002), chemotherapy cycle (P = 0.014), IMR (P = 0.01), and many other factors significantly affected the prognosis of SCLC. Multivariate analysis demonstrated that elevated IMR was an independent adverse predictor of OS (P = 0.039, HR = 0.330). Spearman test confirmed that the prognostic indicators IRF, IMR, and SII were positively correlated with the overall survival rate of patients with SCLC. Kaplan-Meier analysis showed that the OS rate of patients with high IMR was significantly worse (P = 0.0096). In addition, we found that IMR was superior to IRF in distinguishing patients with different outcomes in the low and high groups (P < 0.05). Our novel integration index, combining IMR with the TNM stage system and SII index, exhibited superior prognostic value compared to the original index. Additionally, the combination of prognostic indicators IMR and SII significantly stratified stage I-II SCLC patients (P <0.05). Conclusions The prognostic index based on peripheral blood IMR stands out as an independent predictor for SCLC patients pre-treatment. Its accessibility through routine blood analysis facilitates immediate clinical application without requiring prolonged scientific research validation. The integration of IMR with the TNM score enhances survival prediction and risk stratification. Notably, when combined with the SII score, the new IMR index demonstrates significant improvements in prognostication for stage I-II small cell lung cancer.
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Affiliation(s)
- Xingmei Zhang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China
| | - Hanxiao Ren
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Jiangchuan Tian
- Key Laboratory of Clinical Laboratory Diagnostics (Chinese Ministry of Education), College of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, Chongqing 400016, China
- Department of Laboratory Medicine, Guang'an People's Hospital, Guang'an, Sichuan, 638000, China
| | - Chaoguo Yang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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10
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Zhong X, Peng J, Shu Z, Song Q, Li D. Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning. Cancer Imaging 2023; 23:88. [PMID: 37723592 PMCID: PMC10507842 DOI: 10.1186/s40644-023-00607-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning. METHODS Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves. RESULTS Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively. CONCLUSIONS The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.
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Affiliation(s)
- Xia Zhong
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiaowei Song
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dongxue Li
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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11
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Bele A, Wagh V, Munjewar PK. A Comprehensive Review on Cardiovascular Complications of COVID-19: Unraveling the Link to Bacterial Endocarditis. Cureus 2023; 15:e44019. [PMID: 37746510 PMCID: PMC10517725 DOI: 10.7759/cureus.44019] [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: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has ushered in a new era of understanding the multifaceted nature of infectious diseases. Beyond its well-documented respiratory impact, COVID-19 has unveiled intricate interactions with the cardiovascular system, with potential implications that extend to bacterial endocarditis. This review explores the complex interplay between COVID-19 and bacterial endocarditis, elucidating shared risk factors, theoretical mechanisms, and clinical implications. We examine the diverse cardiovascular manifestations of COVID-19, ranging from myocarditis and thromboembolic events to arrhythmias, and delve into the pathogenesis, clinical features, and diagnostic challenges of bacterial endocarditis. By analyzing potential connections, such as viral-induced endothelial disruption and immune modulation, we shed light on the plausible relationship between COVID-19 and bacterial endocarditis. Our synthesis highlights the significance of accurate diagnosis, optimal management, and interdisciplinary collaboration in addressing the challenges posed by these intricate interactions. In addition, we underscore the importance of future research, emphasizing prospective studies on bacterial endocarditis incidence and investigations into the long-term cardiovascular effects of COVID-19. As the boundaries of infectious diseases and cardiovascular complications converge, this review calls for continued research, vigilance, and coordinated efforts to enhance patient care and public health strategies in a rapidly evolving landscape.
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Affiliation(s)
- Anurag Bele
- Internal Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Vasant Wagh
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Pratiksha K Munjewar
- Medical Surgical Nursing, Smt. Radhikabai Meghe Memorial College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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12
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Luo L, Tan Y, Zhao S, Yang M, Che Y, Li K, Liu J, Luo H, Jiang W, Li Y, Wang W. The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer. BMC Cancer 2023; 23:496. [PMID: 37264319 DOI: 10.1186/s12885-023-10990-4] [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] [Received: 02/16/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.
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Affiliation(s)
- Liping Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yubo Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shixuan Zhao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Man Yang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yurou Che
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kezhen Li
- School of Medicine, Southwest Medical University, Luzhou, China
| | - Jieke Liu
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Wenjun Jiang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yongjie Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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13
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Zhang X, Yu S, Li X, Wen X, Liu S, Zu R, Ren H, Li T, Yang C, Luo H. Research progress on the interaction between oxidative stress and platelets: Another avenue for cancer? Pharmacol Res 2023; 191:106777. [PMID: 37080257 DOI: 10.1016/j.phrs.2023.106777] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/06/2023] [Accepted: 04/18/2023] [Indexed: 04/22/2023]
Abstract
Oxidative stress (OS) is a chemical imbalance between an oxidant and an antioxidant, causing damage to redox signaling and control or causing molecular damage. Unbalanced oxidative metabolism can produce excessive reactive oxygen species (ROS). These excess ROS can cause drastic changes in platelet metabolism and further affect platelet function. It will also lead to an increase in platelet procoagulant phenotype and cell apoptosis, which will increase the risk of thrombosis. The creation of ROS and subsequent platelet activation, adhesion, and recruitment are then further encouraged in an auto-amplifying loop by ROS produced from platelets. Meanwhile, cancer cells produce a higher concentration of ROS due to their fast metabolism and high proliferation rate. However, excessive ROS can result in damage to and modification of cellular macromolecules. The formation of cancer and its progression is strongly associated with oxidative stress and the resulting oxidative damage. In addition, platelets are an important part of the tumor microenvironment, and there is a significant cross-communication between platelets and cancer cells. Cancer cells alter the activation status of platelets, their RNA spectrum, proteome, and other properties. The "cloaking" of cancer cells by platelets providing physical protection,avoiding destruction from shear stress and the attack of immune cells, promoting tumor cell invasion.We explored the vicious circle interaction between ROS, platelets, and cancer in this review, and we believe that ROS can play a stimulative role in tumor growth and metastasis through platelets.
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Affiliation(s)
- Xingmei Zhang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China; College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Sisi Yu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China
| | - Xiaobo Li
- Molecular Diagnostic Laboratory of Department of Microbiology and Immunology, 3201 Hospital Affiliated to Medical College of Xi'an Jiaotong University, Hanzhong 723099, China
| | - Xiaoxia Wen
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Shan Liu
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Ruiling Zu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China
| | - Hanxiao Ren
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China
| | - Tian Li
- School of Basic Medicine, Fourth Military Medical University, Xi'an 710032, China.
| | - Chaoguo Yang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610042, China.
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041 China.
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14
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Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
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15
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Zhang T, Pang A, Lyu J, Ren H, Song J, Zhu F, Liu J, Cui Y, Ling C, Tian Y. Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer. J Clin Med 2023; 12:jcm12030844. [PMID: 36769493 PMCID: PMC9917843 DOI: 10.3390/jcm12030844] [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: 11/20/2022] [Revised: 01/11/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher's linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis.
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Affiliation(s)
- Tongshuo Zhang
- Department of Laboratory Medicine and Pathology, Jiangsu Provincial Corps Hospital of Chinese People’s Armed Police Force (PAP), Yangzhou 225003, China
| | - Aibo Pang
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
| | - Jungang Lyu
- Third Department of Internal Medicine, Beijing Corps Hospital of PAP, Beijing 100027, China
| | - Hefei Ren
- Department of Laboratory Medicine, The Second Affiliated Hospital, Naval Medical University, Shanghai 200003, China
| | - Jiangnan Song
- Department of Obstetrics and Gynecology, The First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Feng Zhu
- Department of Laboratory Medicine and Pathology, Jiangsu Provincial Corps Hospital of Chinese People’s Armed Police Force (PAP), Yangzhou 225003, China
| | - Jinlong Liu
- Department of Obstetrics and Gynecology, The 79th Group Army Hospital of PLA, Liaoyang 111000, China
| | - Yuntao Cui
- Department of Laboratory Medicine, Characteristic Medical Center of PAP, Tianjin 300162, China
| | - Cunbao Ling
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
| | - Yaping Tian
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
- Correspondence:
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16
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Zaydman MA, Jackups R. By Pathologists for Pathologists: Solving Common Informatics Problems in Laboratory Medicine with Open-Source Software Solutions. J Appl Lab Med 2023; 8:11-13. [PMID: 36610430 DOI: 10.1093/jalm/jfac120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/27/2022] [Indexed: 01/09/2023]
Affiliation(s)
- Mark A Zaydman
- Washington University in St. Louis School of Medicine, Department of Pathology and Immunology, St. Louis, MO, United States
| | - Ronald Jackups
- Washington University in St. Louis School of Medicine, Department of Pathology and Immunology, St. Louis, MO, United States
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