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Lam ST, Lam JW, Reddy AJ, Lee L, Yu Z, Falkenstein BE, Fu VW, Cheng E, Patel R. Advancing Breast Cancer Research Through Collaborative Computing: Harnessing Google Colab for Innovation. Cureus 2024; 16:e57280. [PMID: 38690491 PMCID: PMC11058570 DOI: 10.7759/cureus.57280] [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: 03/30/2024] [Indexed: 05/02/2024] Open
Abstract
This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.
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Affiliation(s)
- Sydney T Lam
- Medicine, California University of Science and Medicine, Colton, USA
| | - Jonathan W Lam
- Medicine, California University of Science and Medicine, Colton, USA
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | - Longines Lee
- Medicine, California University of Science and Medicine, Colton, USA
| | - Zeyu Yu
- Medicine, California Health Sciences University, Clovis, USA
| | | | - Victor W Fu
- Medicine, California Health Sciences University, Clovis, USA
| | - Evan Cheng
- Medicine, California Health Sciences University, Clovis, USA
| | - Rakesh Patel
- Internal Medicine, Quillen College of Medicine, East Tennessee State University, Johnson City, USA
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Jacquemyn X, Kutty S, Manlhiot C. The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:440-452. [PMID: 38161675 PMCID: PMC10755786 DOI: 10.1016/j.cjcpc.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/24/2023] [Indexed: 01/03/2024]
Abstract
Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.
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Affiliation(s)
- Xander Jacquemyn
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Bahado‐Singh RO, Turkoglu O, Aydas B, Vishweswaraiah S. Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study. Cancer Med 2023; 12:19644-19655. [PMID: 37787018 PMCID: PMC10587955 DOI: 10.1002/cam4.6604] [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: 05/23/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. METHODS The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. RESULTS In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed. CONCLUSION Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.
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Affiliation(s)
- Ray O. Bahado‐Singh
- Department of Obstetrics and GynecologyCorewell Health – William Beaumont University HospitalRoyal OakMichiganUSA
| | - Onur Turkoglu
- Department of Obstetrics and GynecologyCorewell Health – William Beaumont University HospitalRoyal OakMichiganUSA
| | - Buket Aydas
- Department of Care Management AnalyticsBlue Cross Blue Shield of MichiganDetroitMichiganUSA
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Sopic M, Robinson EL, Emanueli C, Srivastava P, Angione C, Gaetano C, Condorelli G, Martelli F, Pedrazzini T, Devaux Y. Integration of epigenetic regulatory mechanisms in heart failure. Basic Res Cardiol 2023; 118:16. [PMID: 37140699 PMCID: PMC10158703 DOI: 10.1007/s00395-023-00986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/05/2023]
Abstract
The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.
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Affiliation(s)
- Miron Sopic
- Department of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Emma L Robinson
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Costanza Emanueli
- National Heart & Lung Institute, Imperial College London, London, UK
| | | | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, Middlesbrough, TS1 3BA, UK
- Centre for Digital Innovation, Teesside University, Campus Heart, Tees Valley, Middlesbrough, TS1 3BX, UK
- National Horizons Centre, Darlington, DL1 1HG, UK
| | - Carlo Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 10, 27100, Pavia, Italy
| | - Gianluigi Condorelli
- IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Arnold-Heller-Str.3, 24105, Milan, Italy
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS-Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Milan, Italy
| | - Thierry Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, 1011, Lausanne, Switzerland
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg.
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Liu YH, Lin XM, Li DZ. Precision medicine based on circulating cell-free DNA in maternal blood: there is still a long way to go. Am J Obstet Gynecol 2023; 228:247-248. [PMID: 36183776 DOI: 10.1016/j.ajog.2022.09.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/21/2022] [Indexed: 01/28/2023]
Affiliation(s)
- Yan-Hui Liu
- Prenatal Diagnostic Center, Dongguan Maternal and Children Health Hospital, Dongguan, Guangdong, China
| | - Xiao-Mei Lin
- Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China
| | - Dong-Zhi Li
- Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China.
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