1
|
Wang YL, Gao S, Xiao Q, Li C, Grzegorzek M, Zhang YY, Li XH, Kang Y, Liu FH, Huang DH, Gong TT, Wu QJ. Role of artificial intelligence in digital pathology for gynecological cancers. Comput Struct Biotechnol J 2024; 24:205-212. [PMID: 38510535 PMCID: PMC10951449 DOI: 10.1016/j.csbj.2024.03.007] [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: 12/28/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024] Open
Abstract
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
Collapse
Affiliation(s)
- Ya-Li Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Information Center, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Ying-Ying Zhang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiao-Han Li
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dong-Hui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China
| |
Collapse
|
2
|
Chang X, Zheng Y, Xu K. Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research. Mol Biotechnol 2024; 66:1497-1519. [PMID: 37322261 PMCID: PMC11217094 DOI: 10.1007/s12033-023-00777-0] [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: 03/09/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023]
Abstract
Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.
Collapse
Affiliation(s)
- Xu Chang
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Yunxi Zheng
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Kai Xu
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
| |
Collapse
|
3
|
Li X, Cui P, Zhao X, Liu Z, Qi Y, Liu B. Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance. Int J Gen Med 2024; 17:2821-2831. [PMID: 38919704 PMCID: PMC11198022 DOI: 10.2147/ijgm.s465031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
Objective To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance. Methods Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively. Conclusion After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.
Collapse
Affiliation(s)
- Xin Li
- Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| | - Peng Cui
- Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| | - XingXing Zhao
- Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| | - Zhao Liu
- Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| | - YanXiang Qi
- Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| | - Bo Liu
- Department of Gynaecological Oncology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China
| |
Collapse
|
4
|
Xu W, Liang X, Chen L, Hong W, Hu X. Biobanks in chronic disease management: A comprehensive review of strategies, challenges, and future directions. Heliyon 2024; 10:e32063. [PMID: 38868047 PMCID: PMC11168399 DOI: 10.1016/j.heliyon.2024.e32063] [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: 12/22/2023] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024] Open
Abstract
Biobanks, through the collection and storage of patient blood, tissue, genomic, and other biological samples, provide unique and rich resources for the research and management of chronic diseases such as cardiovascular diseases, diabetes, and cancer. These samples contain valuable cellular and molecular level information that can be utilized to decipher the pathogenesis of diseases, guide the development of novel diagnostic technologies, treatment methods, and personalized medical strategies. This article first outlines the historical evolution of biobanks, their classification, and the impact of technological advancements. Subsequently, it elaborates on the significant role of biobanks in revealing molecular biomarkers of chronic diseases, promoting the translation of basic research to clinical applications, and achieving individualized treatment and management. Additionally, challenges such as standardization of sample processing, information privacy, and security are discussed. Finally, from the perspectives of policy support, regulatory improvement, and public participation, this article provides a forecast on the future development directions of biobanks and strategies to address challenges, aiming to safeguard and enhance their unique advantages in supporting chronic disease prevention and treatment.
Collapse
Affiliation(s)
- Wanna Xu
- Shenzhen Center for Chronic Disease Control, Shenzhen Institute of Dermatology, Shenzhen, 518020, China
| | - Xiongshun Liang
- Shenzhen Center for Chronic Disease Control, Shenzhen Institute of Dermatology, Shenzhen, 518020, China
| | - Lin Chen
- Shenzhen Center for Chronic Disease Control, Shenzhen Institute of Dermatology, Shenzhen, 518020, China
| | - Wenxu Hong
- Shenzhen Center for Chronic Disease Control, Shenzhen Institute of Dermatology, Shenzhen, 518020, China
| | - Xuqiao Hu
- Shenzhen Center for Chronic Disease Control, Shenzhen Institute of Dermatology, Shenzhen, 518020, China
- Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology (Shenzhen People's Hospital), Shenzhen, China
| |
Collapse
|
5
|
Liu Y, Su Z, Tavana O, Gu W. Understanding the complexity of p53 in a new era of tumor suppression. Cancer Cell 2024; 42:946-967. [PMID: 38729160 PMCID: PMC11190820 DOI: 10.1016/j.ccell.2024.04.009] [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: 02/01/2024] [Revised: 03/15/2024] [Accepted: 04/16/2024] [Indexed: 05/12/2024]
Abstract
p53 was discovered 45 years ago as an SV40 large T antigen binding protein, coded by the most frequently mutated TP53 gene in human cancers. As a transcription factor, p53 is tightly regulated by a rich network of post-translational modifications to execute its diverse functions in tumor suppression. Although early studies established p53-mediated cell-cycle arrest, apoptosis, and senescence as the classic barriers in cancer development, a growing number of new functions of p53 have been discovered and the scope of p53-mediated anti-tumor activity is largely expanded. Here, we review the complexity of different layers of p53 regulation, and the recent advance of the p53 pathway in metabolism, ferroptosis, immunity, and others that contribute to tumor suppression. We also discuss the challenge regarding how to activate p53 function specifically effective in inhibiting tumor growth without harming normal homeostasis for cancer therapy.
Collapse
Affiliation(s)
- Yanqing Liu
- Institute for Cancer Genetics, and Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Zhenyi Su
- Institute for Cancer Genetics, and Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Omid Tavana
- Institute for Cancer Genetics, and Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Wei Gu
- Institute for Cancer Genetics, and Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
| |
Collapse
|
6
|
Crouzet A, Lopez N, Riss Yaw B, Lepelletier Y, Demange L. The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang? Molecules 2024; 29:2716. [PMID: 38930784 PMCID: PMC11206022 DOI: 10.3390/molecules29122716] [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: 03/29/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
Collapse
Affiliation(s)
- Aurore Crouzet
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
| | - Nicolas Lopez
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- ENOES, 62 Rue de Miromesnil, 75008 Paris, France
- Unité Mixte de Recherche «Institut de Physique Théorique (IPhT)» CEA-CNRS, UMR 3681, Bat 774, Route de l’Orme des Merisiers, 91191 St Aubin-Gif-sur-Yvette, France
| | - Benjamin Riss Yaw
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| | - Yves Lepelletier
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- Université Paris Cité, Imagine Institute, 24 Boulevard Montparnasse, 75015 Paris, France
- INSERM UMR 1163, Laboratory of Cellular and Molecular Basis of Normal Hematopoiesis and Hematological Disorders: Therapeutical Implications, 24 Boulevard Montparnasse, 75015 Paris, France
| | - Luc Demange
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| |
Collapse
|
7
|
Sinha S, Vegesna R, Mukherjee S, Kammula AV, Dhruba SR, Wu W, Kerr DL, Nair NU, Jones MG, Yosef N, Stroganov OV, Grishagin I, Aldape KD, Blakely CM, Jiang P, Thomas CJ, Benes CH, Bivona TG, Schäffer AA, Ruppin E. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. NATURE CANCER 2024; 5:938-952. [PMID: 38637658 DOI: 10.1038/s43018-024-00756-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
Collapse
Affiliation(s)
- Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
- NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Sumit Mukherjee
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
- University of Maryland, College Park, MD, USA
| | | | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - D Lucas Kerr
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Matthew G Jones
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | | | - Ivan Grishagin
- Rancho BioSciences, San Diego, CA, USA
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Collin M Blakely
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
| |
Collapse
|
8
|
Phanthunane C, Pongcharoen S, Pannarunothai S, Roboon J, Phanthunane P, Nontarak J. Precision medicine in Asia enhanced by next-generation sequencing: Implications for Thailand through a scoping review and interview study. Clin Transl Sci 2024; 17:e13868. [PMID: 38924657 PMCID: PMC11197108 DOI: 10.1111/cts.13868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Next-generation sequencing (NGS) significantly enhances precision medicine (PM) by offering personalized approaches to diagnosis, treatment, and prevention of unmet medical needs. Little is known about the current situation of PM in Asia. Thus, we aimed to conduct an overview of the progress and gaps in PM in Asia and enrich it with in-depth insight into the possibilities of future PM in Thailand. This scoping review focused on Asian countries starting with non-cancer studies, including rare and undiagnosed diseases (RUDs), non-communicable diseases (NCDs), infectious diseases (IDs), and pharmacogenomics, with a focus on NGS. Subsequent in-depth interviews with experts in Thailand were performed, and a thematic analysis served as the main qualitative methodology. Out of 2898 searched articles, 387 studies were included after the review. Although most of the studies focused on cancer, 89 (23.0%) studies were related to RUDs (17.1%), NCDs (2.8%), IDs (1.8%), and pharmacogenomics (1.3%). Apart from medicine and related sciences, the studies were mostly composed of PM (61.8%), followed by genetics medicine and bioinformatics. Interestingly, 28% of articles were conducted exclusively within the fields of medicine and related sciences, emphasizing interdisciplinary integration. The experts emphasized the need for sustainability-driven political will, nurturing collaboration, reinforcing computational infrastructure, and expanding the bioinformatic workforce. In Asia, developments of NGS have made remarkable progress in PM. Thailand has extended PM beyond cancer and focused on clinical implementation. We summarized the PM challenges, including equity and efficiency targeting, guided research funding, sufficient sample size, integrated collaboration, computational infrastructure, and sufficient trained human resources.
Collapse
Affiliation(s)
- Chumut Phanthunane
- Division of Medical OncologyChulabhorn Hospital, Chulabhorn Royal AcademyBangkokThailand
| | - Sutatip Pongcharoen
- Department of Medicine, Faculty of MedicineNaresuan UniversityPhitsanulokThailand
| | | | - Jureepon Roboon
- Department of Anatomy, Faculty of Medical ScienceNaresuan UniversityPhitsanulokThailand
- Centre of Excellence in Medical BiotechnologyNaresuan UniversityPhitsanulokThailand
| | - Pudtan Phanthunane
- Department of Economics, Faculty of Business, Economics and CommunicationsNaresuan UniversityPhitsanulokThailand
| | - Jiraluck Nontarak
- Department of Epidemiology, Faculty of Public HealthMahidol UniversityBangkokThailand
| |
Collapse
|
9
|
Zhou Z, Lin T, Chen S, Zhang G, Xu Y, Zou H, Zhou A, Zhang Y, Weng S, Han X, Liu Z. Omics-based molecular classifications empowering in precision oncology. Cell Oncol (Dordr) 2024; 47:759-777. [PMID: 38294647 DOI: 10.1007/s13402-023-00912-8] [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] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine. CONCLUSION This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.
Collapse
Affiliation(s)
- Zhaokai Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ting Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yudi Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Haijiao Zou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Aoyang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, 450052, China.
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, 450052, China.
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| |
Collapse
|
10
|
Wang N, Yang S, Gao Q, Jin X. Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students. Postgrad Med 2024. [PMID: 38819302 DOI: 10.1080/00325481.2024.2363171] [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: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024]
Abstract
Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.
Collapse
Affiliation(s)
- Ning Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuo Yang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qi Gao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiuming Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| |
Collapse
|
11
|
Romero-Morelos P, Herrera-López E, González-Yebra B. Development, Application and Utility of a Machine Learning Approach for Melanoma and Non-Melanoma Lesion Classification Using Counting Box Fractal Dimension. Diagnostics (Basel) 2024; 14:1132. [PMID: 38893659 PMCID: PMC11171650 DOI: 10.3390/diagnostics14111132] [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: 03/15/2024] [Revised: 04/16/2024] [Accepted: 05/09/2024] [Indexed: 06/21/2024] Open
Abstract
The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing specific parameters such as the Breslow index. However, it remains invasive and may lack effectiveness. Therefore, leveraging mathematical modeling and informatics has been a pursuit of diagnostic methods favoring early detection. Fractality, a mathematical parameter quantifying complexity and irregularity, has proven useful in melanoma diagnosis. Nonetheless, no studies have implemented this metric to feed artificial intelligence algorithms for the automatic classification of dermatological lesions, including melanoma. Hence, this study aimed to determine the combined utility of fractal dimension and unsupervised low-computational-requirements machine learning models in classifying melanoma and non-melanoma lesions. We analyzed 39,270 dermatological lesions obtained from the International Skin Imaging Collaboration. Box-counting fractal dimensions were calculated for these lesions. Fractal values were used to implement classification methods by unsupervised machine learning based on principal component analysis and iterated K-means (100 iterations). A clear separation was observed, using only fractal dimension values, between benign or malignant lesions (sensibility 72.4% and specificity 50.1%) and melanoma or non-melanoma lesions (sensibility 72.8% and specificity 50%) and subsequently, the classification quality based on the machine learning model was ≈80% for both benign and malignant or melanoma and non-melanoma lesions. However, the grouping of metastatic melanoma versus non-metastatic melanoma was less effective, probably due to the small sample size included in MM lesions. Nevertheless, we could suggest a decision algorithm based on fractal dimension for dermatological lesion discrimination. On the other hand, it was also determined that the fractal dimension is sufficient to generate unsupervised artificial intelligence models that allow for a more efficient classification of dermatological lesions.
Collapse
Affiliation(s)
- Pablo Romero-Morelos
- Department of Research, State University of the Valley of Ecatepec, Ecatepec 55210, México State, Mexico; (P.R.-M.); (E.H.-L.)
- National Laboratory of Artificial Intelligence and Data Science, CONAHCyT (LNC-IACD), Ecatepec 55210, México State, Mexico
| | - Elizabeth Herrera-López
- Department of Research, State University of the Valley of Ecatepec, Ecatepec 55210, México State, Mexico; (P.R.-M.); (E.H.-L.)
- National Laboratory of Artificial Intelligence and Data Science, CONAHCyT (LNC-IACD), Ecatepec 55210, México State, Mexico
| | - Beatriz González-Yebra
- Department of Medicine and Nutrition, Division of Health Sciences, University of Guanajuato, Campus León, León 37670, Guanajuato, Mexico
| |
Collapse
|
12
|
Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
Collapse
Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| |
Collapse
|
13
|
Masucci M, Karlsson C, Blomqvist L, Ernberg I. Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine. J Pers Med 2024; 14:561. [PMID: 38929782 PMCID: PMC11204735 DOI: 10.3390/jpm14060561] [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: 04/11/2024] [Revised: 05/05/2024] [Accepted: 05/22/2024] [Indexed: 06/28/2024] Open
Abstract
The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.
Collapse
Affiliation(s)
- Michele Masucci
- Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Tomtebodavägen 18B, 171 65 Solna, Sweden
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solnavägen 9, 171 65 Solna, Sweden
| | - Claes Karlsson
- Department of Oncology-Pathology (Onc-Pat), Karolinska Institutet, Anna Steckséns gata 30A, D2:04, 171 65 Solna, Sweden;
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surgery (MMK), Karolinska Institutet, Anna Steckséns gata 53, 171 65 Solna, Sweden;
| | - Ingemar Ernberg
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solnavägen 9, 171 65 Solna, Sweden
| |
Collapse
|
14
|
Lynch SM, Heeran AB, Burke C, Lynam-Lennon N, Eustace AJ, Dean K, Robson T, Rahman A, Marcone S. Precision Oncology, Artificial Intelligence, and Novel Therapeutic Advancements in the Diagnosis, Prevention, and Treatment of Cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference. Cancers (Basel) 2024; 16:1989. [PMID: 38893110 PMCID: PMC11171401 DOI: 10.3390/cancers16111989] [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: 04/19/2024] [Revised: 05/21/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Advancements in oncology, especially with the era of precision oncology, is resulting in a paradigm shift in cancer care. Indeed, innovative technologies, such as artificial intelligence, are paving the way towards enhanced diagnosis, prevention, and personalised treatments as well as novel drug discoveries. Despite excellent progress, the emergence of resistant cancers has curtailed both the pace and extent to which we can advance. By combining both their understanding of the fundamental biological mechanisms and technological advancements such as artificial intelligence and data science, cancer researchers are now beginning to address this. Together, this will revolutionise cancer care, by enhancing molecular interventions that may aid cancer prevention, inform clinical decision making, and accelerate the development of novel therapeutic drugs. Here, we will discuss the advances and approaches in both artificial intelligence and precision oncology, presented at the 59th Irish Association for Cancer Research annual conference.
Collapse
Affiliation(s)
- Seodhna M. Lynch
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry BT47 6SB, UK;
| | - Aisling B. Heeran
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, D04 C1P1 Dublin, Ireland;
| | - Niamh Lynam-Lennon
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
| | - Alex J. Eustace
- Life Sciences Institute, Dublin City University, D09 NR58 Dublin, Ireland;
| | - Kellie Dean
- School of Biochemistry and Cell Biology, Western Gateway Building, University College Cork, T12 XF62 Cork, Ireland;
| | - Tracy Robson
- School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, D02 YN77 Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Belfield, D04 C1P1 Dublin, Ireland;
| | - Simone Marcone
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
| |
Collapse
|
15
|
Sun G, Cai L, Yan X, Nie W, Liu X, Xu J, Zou X. A prediction model based on digital breast pathology image information. PLoS One 2024; 19:e0294923. [PMID: 38758814 PMCID: PMC11101065 DOI: 10.1371/journal.pone.0294923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/11/2023] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance. METHODS In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model. RESULTS The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis. CONCLUSION The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.
Collapse
Affiliation(s)
- Guoxin Sun
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Liying Cai
- College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan City, China
| | - Xiong Yan
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Weihong Nie
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Xin Liu
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Jing Xu
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Xiao Zou
- Department of Breast Surgery, Xiangdong Hospital Affiliated to Hunan Normal University, Hunan, China
| |
Collapse
|
16
|
Khorsandi D, Rezayat D, Sezen S, Ferrao R, Khosravi A, Zarepour A, Khorsandi M, Hashemian M, Iravani S, Zarrabi A. Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? J Mater Chem B 2024; 12:4584-4612. [PMID: 38686396 DOI: 10.1039/d4tb00310a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.
Collapse
Affiliation(s)
- Danial Khorsandi
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
| | - Dorsa Rezayat
- Center for Global Design and Manufacturing, College of Engineering and Applied Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA
| | - Serap Sezen
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956 Istanbul, Türkiye
- Nanotechnology Research and Application Center, Sabanci University, Tuzla 34956 Istanbul, Türkiye
| | - Rafaela Ferrao
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
- University of Coimbra, Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), Portugal
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Türkiye
| | - Atefeh Zarepour
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600 077, India
| | - Melika Khorsandi
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hashemian
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Siavash Iravani
- Independent Researcher, W Nazar ST, Boostan Ave, Isfahan, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye.
- Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan
| |
Collapse
|
17
|
Zhou J, Xia Y, Xun X, Yu Z. Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01132-8. [PMID: 38740661 DOI: 10.1007/s10278-024-01132-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/30/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024]
Abstract
Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10-4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions.
Collapse
Affiliation(s)
- Jie Zhou
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yujia Xia
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolei Xun
- Global Statistics and Data Science, BeiGene, Shanghai, China
| | - Zhangsheng Yu
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| |
Collapse
|
18
|
Bektaş M, Tan C, Burchell GL, Daams F, van der Peet DL. Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108385. [PMID: 38755062 DOI: 10.1016/j.ejso.2024.108385] [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: 01/19/2024] [Revised: 02/29/2024] [Accepted: 05/01/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery. METHODS A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study. CONCLUSIONS This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.
Collapse
Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - Cevin Tan
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - George L Burchell
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Medical Library, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| |
Collapse
|
19
|
Frewing A, Gibson AB, Robertson R, Urie PM, Corte DD. Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Arch Pathol Lab Med 2024; 148:603-612. [PMID: 37594900 DOI: 10.5858/arpa.2022-0460-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2023] [Indexed: 08/20/2023]
Abstract
CONTEXT Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading. OBJECTIVE To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed. DATA SOURCES The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities. CONCLUSIONS It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.
Collapse
Affiliation(s)
- Aaryn Frewing
- From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah
| | - Alexander B Gibson
- From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah
| | - Richard Robertson
- From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah
| | - Paul M Urie
- From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah
| | - Dennis Della Corte
- From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah
| |
Collapse
|
20
|
Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
Collapse
Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
| | | | | |
Collapse
|
21
|
Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [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: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
Collapse
Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
22
|
Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
Collapse
Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
23
|
Farabi Maleki S, Yousefi M, Afshar S, Pedrammehr S, Lim CP, Jafarizadeh A, Asadi H. Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls. Semin Ophthalmol 2024; 39:271-288. [PMID: 38088176 DOI: 10.1080/08820538.2023.2293030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/23/2023] [Indexed: 03/28/2024]
Abstract
Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.
Collapse
Affiliation(s)
| | - Milad Yousefi
- Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran
| | - Sayeh Afshar
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Houshyar Asadi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
| |
Collapse
|
24
|
Shayan N, Ghiyasimoghaddam N, Mirkatuli HA, Baghbani M, Ranjbarzadhagh Z, Mohtasham N. The biomarkers for maintenance Cancer stem cell features can be applicable in precision medicine of head and neck squamous cell carcinoma. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101906. [PMID: 38688401 DOI: 10.1016/j.jormas.2024.101906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/30/2024] [Accepted: 04/27/2024] [Indexed: 05/02/2024]
Abstract
Cancer stem cells (CSCs) play a crucial role in tumor relapse, proliferation, invasion, and drug resistance in head and neck squamous cell carcinoma (HNSCC). This narrative review aims to synthesize data from articles published between 2019 and 2023 on biomarkers for detecting CSCs in HNSCC and changes in molecular pathways, genetics, epigenetics, and non-coding RNAs (ncRNAs) in CSCs relevant to precision medicine approaches in HNSCC management. The search encompassed 41 in vitro studies and 22 clinical studies. CSCs exhibit diverse molecular profiles and unique biomarker expression patterns, offering significant potential for HNSCC diagnosis, treatment, and prognosis, thereby enhancing patient survival. Their remarkable self-renewal ability and adaptability are closely linked to tumorigenicity development and maintenance. Assessing biomarkers before and after therapy can aid in identifying various cell types associated with cancer progression and relapse. Screening for CSCs, senescent tumor cells, and cells correlated with the senescence process post-treatment has proven highly beneficial. However, the clinical application of precision medicine in HNSCC management is hindered by the lack of specific and definitive CSC biomarkers. Furthermore, our limited understanding of CSC plasticity, governed by genomic, transcriptomic, and epigenomic alterations during tumorigenesis, as well as the bidirectional interaction of CSCs with the tumor microenvironment, underscores the need for further research. Well-designed studies involving large patient cohorts are, therefore, essential to establish a standardized protocol and address these unresolved queries.
Collapse
Affiliation(s)
- Navidreza Shayan
- Department of Medical Sciences, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Negin Ghiyasimoghaddam
- Department of Emergency Medicine, Bohlool Hospital, Gonabad University of Medical Sciences, Gonabad, Iran
| | | | | | - Zahra Ranjbarzadhagh
- Department of Laboratory Sciences, Faculty of Paramedicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nooshin Mohtasham
- Oral and Maxillofacial Diseases Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| |
Collapse
|
25
|
Qi JH, Huang SL, Jin SZ. Novel milestones for early esophageal carcinoma: From bench to bed. World J Gastrointest Oncol 2024; 16:1104-1118. [PMID: 38660637 PMCID: PMC11037034 DOI: 10.4251/wjgo.v16.i4.1104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/28/2024] [Accepted: 02/26/2024] [Indexed: 04/10/2024] Open
Abstract
Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.
Collapse
Affiliation(s)
- Ji-Han Qi
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Ling Huang
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Zhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| |
Collapse
|
26
|
El-Sherbini AH, Coroneos S, Zidan A, Othman M. Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review. Semin Thromb Hemost 2024. [PMID: 38604227 DOI: 10.1055/s-0044-1785482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms "machine learning," "artificial intelligence," "thrombosis," and "cancer" were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head-neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.
Collapse
Affiliation(s)
- Adham H El-Sherbini
- Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Stefania Coroneos
- Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Ali Zidan
- Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Maha Othman
- School of Baccalaureate Nursing, St Lawrence College, Kingston, Ontario, Canada
- Faculty of Medicine, Mansoura University, Mansoura, Egypt
| |
Collapse
|
27
|
Malla R, Viswanathan S, Makena S, Kapoor S, Verma D, Raju AA, Dunna M, Muniraj N. Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing. Cancers (Basel) 2024; 16:1463. [PMID: 38672545 PMCID: PMC11048531 DOI: 10.3390/cancers16081463] [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: 03/02/2024] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.
Collapse
Affiliation(s)
- RamaRao Malla
- Cancer Biology Laboratory, Department of Biochemistry and Bioinformatics, GITAM School of Science, GITAM (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
| | - Sathiyapriya Viswanathan
- Department of Biochemistry, ACS Medical College and Hospital, Chennai 600007, Tamil Nadu, India;
| | - Sree Makena
- Maharajah’s Institute of Medical Sciences and Hospital, Vizianagaram 535217, Andhra Pradesh, India
| | - Shruti Kapoor
- Department of Genetics, University of Alabama, Birmingham, AL 35233, USA
| | - Deepak Verma
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | | | - Manikantha Dunna
- Center for Biotechnology, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
| | - Nethaji Muniraj
- Center for Cancer and Immunology Research, Children’s National Hospital, 111, Michigan Ave NW, Washington, DC 20010, USA
| |
Collapse
|
28
|
Mhaske S, Ramalingam K, Nair P, Patel S, Menon P A, Malik N, Mhaske S. Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning. Cureus 2024; 16:e58744. [PMID: 38779230 PMCID: PMC11110917 DOI: 10.7759/cureus.58744] [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: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND As oral cancer remains a major worldwide health concern, sophisticated diagnostic tools are needed to aid in early diagnosis. Non-invasive methods like exfoliative cytology, albeit with the help of artificial intelligence (AI), have drawn additional interest. AIM The study aimed to harness the power of machine learning algorithms for the automated analysis of nuclear parameters in oral exfoliative cytology. Further, the analysis of two different AI systems, namely convoluted neural networks (CNN) and support vector machine (SVM), were compared for accuracy. METHODS A comparative diagnostic study was performed in two groups of patients (n=60). The control group without evidence of lesions (n=30) and the other group with clinically suspicious oral malignancy (n=30) were evaluated. All patients underwent cytological smears using an exfoliative cytology brush, followed by routine Hematoxylin and Eosin staining. Image preprocessing, data splitting, machine learning, model development, feature extraction, and model evaluation were done. An independent t-test was run on each nuclear characteristic, and Pearson's correlation coefficient test was performed with Statistical Package for the Social Sciences (SPSS) software (IBM SPSS Statistics for Windows, Version 28.0. IBM Corp, Armonk, NY, USA). RESULTS The study found substantial variations between the study and control groups in nuclear size (p<0.05), nuclear shape (p<0.01), and chromatin distribution (p<0.001). The Pearson correlation coefficient of SVM was 0.6472, and CNN was 0.7790, showing that SVM had more accuracy. CONCLUSION The availability of multidimensional datasets, combined with breakthroughs in high-performance computers and new deep-learning architectures, has resulted in an explosion of AI use in numerous areas of oncology research. The discerned diagnostic accuracy exhibited by the SVM and CNN models suggests prospective improvements in early detection rates, potentially improving patient outcomes and enhancing healthcare practices.
Collapse
Affiliation(s)
- Shubhangi Mhaske
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
- Oral and Maxillofacial Pathology, People's College Of Dental Science and Research Center, Bhopal, IND
| | - Karthikeyan Ramalingam
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Preeti Nair
- Oral Medicine and Radiology, People's College Of Dental Science and Research Center, Bhopal, IND
| | - Shubham Patel
- Oral and Maxillofacial Pathology, People's College Of Dental Science and Research Center, Bhopal, IND
| | - Arathi Menon P
- Dentistry, Indian Council of Medical Research, Bhopal, IND
| | - Nida Malik
- Periodontics, Kamala Nehru Hospital, Bhopal, IND
| | - Sumedh Mhaske
- Medicine, Government Medical College & Hospital, Aurangabad, IND
| |
Collapse
|
29
|
Scheper MC, van Velzen M, L U van Meeteren N. Towards responsible use of artificial intelligence in daily practice: what do physiotherapists need to know, consider and do? J Physiother 2024; 70:81-84. [PMID: 38036398 DOI: 10.1016/j.jphys.2023.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/14/2023] [Accepted: 07/09/2023] [Indexed: 12/02/2023] Open
Affiliation(s)
- Mark C Scheper
- Research Center Innovations in Care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands; Institute for Communication, Media and Information Technology, Rotterdam University of Applied Sciences Rotterdam, Rotterdam, the Netherlands; Responsible AI, Creating010, Rotterdam University of Applied Sciences Rotterdam, Rotterdam, the Netherlands; Allied Health Professions, Faculty of Medicine and Science, Macquarie University, Sydney, Australia.
| | - Mark van Velzen
- Research Center Innovations in Care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands; Erasmus University Medical Center, Department of Anesthesiology, Rotterdam, the Netherlands
| | - Nico L U van Meeteren
- Erasmus University Medical Center, Department of Anesthesiology, Rotterdam, the Netherlands; Top Sector Life Sciences & Health (Health∼Holland), The Hague, the Netherlands
| |
Collapse
|
30
|
Seth I, Lim B, Joseph K, Gracias D, Xie Y, Ross RJ, Rozen WM. Use of artificial intelligence in breast surgery: a narrative review. Gland Surg 2024; 13:395-411. [PMID: 38601286 PMCID: PMC11002485 DOI: 10.21037/gs-23-414] [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: 10/09/2023] [Accepted: 02/21/2024] [Indexed: 04/12/2024]
Abstract
Background and Objective We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
Collapse
Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Konrad Joseph
- Department of Surgery, Port Macquarie Base Hospital, New South Wales, Australia
| | - Dylan Gracias
- Department of Surgery, Townsville Hospital, Queensland, Australia
| | - Yi Xie
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| |
Collapse
|
31
|
Li Y, Gao W, Yang Z, Hu Z, Li J. Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker. Sci Rep 2024; 14:6517. [PMID: 38499612 PMCID: PMC10948783 DOI: 10.1038/s41598-024-57299-1] [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: 10/04/2023] [Accepted: 03/16/2024] [Indexed: 03/20/2024] Open
Abstract
Minichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and treatment.
Collapse
Affiliation(s)
- Yanxing Li
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Wentao Gao
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Zhen Yang
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Zhenwei Hu
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Jianjun Li
- Department of Cardiology, Jincheng People's Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi, People's Republic of China.
| |
Collapse
|
32
|
Chugh V, Basu A, Kaushik A, Manshu, Bhansali S, Basu AK. Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer. NANOSCALE 2024; 16:5458-5486. [PMID: 38391246 DOI: 10.1039/d3nr05648a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.
Collapse
Affiliation(s)
- Vibhas Chugh
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
| | - Adreeja Basu
- Biological Science, St. John's University, New York, NY 10301, United States
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, Florida 33805, USA
| | - Manshu
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
| | - Shekhar Bhansali
- Electrical and Computer Engineering, Florida International University, Miami, FL 33199, USA
| | - Aviru Kumar Basu
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
| |
Collapse
|
33
|
Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
Collapse
Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| |
Collapse
|
34
|
Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endosc Ultrasound 2024; 13:65-75. [PMID: 38947752 PMCID: PMC11213611 DOI: 10.1097/eus.0000000000000053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
Collapse
Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| |
Collapse
|
35
|
Angeloni M, van Doeveren T, Lindner S, Volland P, Schmelmer J, Foersch S, Matek C, Stoehr R, Geppert CI, Heers H, Wach S, Taubert H, Sikic D, Wullich B, van Leenders GJLH, Zaburdaev V, Eckstein M, Hartmann A, Boormans JL, Ferrazzi F, Bahlinger V. A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. J Pathol Clin Res 2024; 10:e12369. [PMID: 38504364 PMCID: PMC10951050 DOI: 10.1002/2056-4538.12369] [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: 11/24/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.
Collapse
Affiliation(s)
- Miriam Angeloni
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Thomas van Doeveren
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Sebastian Lindner
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Patrick Volland
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Jorina Schmelmer
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | | | - Christian Matek
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Robert Stoehr
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Hendrik Heers
- Department of UrologyPhilipps‐Universität MarburgMarburgGermany
| | - Sven Wach
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Helge Taubert
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Danijel Sikic
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Bernd Wullich
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Geert JLH van Leenders
- Department of PathologyErasmus MC Cancer Institute, University Medical CentreRotterdamthe Netherlands
| | - Vasily Zaburdaev
- Department of BiologyFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Max‐Planck‐Zentrum für Physik und MedizinErlangenGermany
| | - Markus Eckstein
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Joost L Boormans
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Fulvia Ferrazzi
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of NephropathologyInstitute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Veronika Bahlinger
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Pathology and NeuropathologyUniversity Hospital and Comprehensive Cancer Center TübingenTübingenGermany
| |
Collapse
|
36
|
Hesso I, Kayyali R, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Gebara SN. Cancer care pathways across seven countries in Europe: What are the current obstacles? And how can artificial intelligence help? J Cancer Policy 2024; 39:100457. [PMID: 38008356 DOI: 10.1016/j.jcpo.2023.100457] [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: 08/07/2023] [Revised: 10/25/2023] [Accepted: 11/18/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND Cancer poses significant challenges for healthcare professionals across the disease pathway including cancer imaging. This study constitutes part of the user requirement definition of INCISIVE EU project. The project has been designed to explore the full potential of artificial intelligence (AI)-based technologies in cancer imaging to streamline diagnosis and management. The study aimed to map cancer care pathways (breast, prostate, colorectal and lung cancers) across INCISIVE partner countries, and identify bottle necks within these pathways. METHODS Email interviews were conducted with ten oncology specialised healthcare professionals representing INCISIVE partner countries: Greece, Cyprus, Spain, Italy, Finland, the United Kingdom (UK) and Serbia. A purposive sampling strategy was employed for recruitment and data was collected between December 2020 and April 2021. Data was entered into Microsoft Excel spreadsheet to allow content examination and comparative analysis. RESULTS The analysed pathways all shared a common characteristic: inequalities in relation to delays in cancer diagnosis and treatment. All the studied countries, except the UK, lacked official national data about diagnostic and therapeutic delays. Furthermore, a considerable variation was noted regarding the availability of imaging and diagnostic services across the seven countries. Several concerns were also noted for inefficiencies/inequalities with regards to national screening for the four investigated cancer types. CONCLUSIONS Delays in cancer diagnosis and treatment are an ongoing challenge and a source for inequalities. It is important to have systematic reporting of diagnostic and therapeutic delays in all countries to allow the proper estimation of its magnitude and support needed to address it. Our findings also support the orientation of the current policies towards early detection and wide scale adoption and implementation of cancer screening, through research, innovation, and technology. Technologies involving AI can have a great potential to revolutionise cancer care delivery. POLICY SUMMARY This study highlights the widespread delay in cancer diagnosis across Europe and supports the need for, systematic reporting of delays, improved availability of imaging services, and optimised national screening programs. The goal is to enhance cancer care delivery, encourage early detection, and implement research, innovation, and AI-based technologies for improved cancer imaging.
Collapse
Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston upon Thames, United Kingdom
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston upon Thames, United Kingdom
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston upon Thames, United Kingdom
| | | | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece; Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, Spain
| | - Wanda Acampa
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Jasmina Boban
- Department of Radiology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; Diagnostic Imaging Center, Oncology Institute of Vojvodine, Put dr Goldmana 4, 21204 Sremska Kamenica, Serbia
| | - Shereen Nabhani Gebara
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston upon Thames, United Kingdom.
| |
Collapse
|
37
|
Pesapane F, Rotili A, Raimondi S, Aurilio G, Lazzeroni M, Nicosia L, Latronico A, Pizzamiglio M, Cassano E, Gandini S. Evolving paradigms in breast cancer screening: Balancing efficacy, personalization, and equity. Eur J Radiol 2024; 172:111321. [PMID: 38244317 DOI: 10.1016/j.ejrad.2024.111321] [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: 12/30/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024]
Abstract
Breast cancer remains a significant global health challenge, with projections indicating a troubling increase in incidence. Breast cancer screening programs have long been hailed as life-saving initiatives, yet their true impact on mortality rates is a subject of ongoing debate. Screening poses the risk of false positives and the detection of indolent tumors, potentially leading to overtreatment. Bias factors, including lead time, length time, and selection biases, further complicate the assessment of screening efficacy. Recent studies suggest that AI-driven image analysis may revolutionize breast cancer screening, maintaining diagnostic accuracy while reducing radiologists' workload. However, the generalizability of these findings to diverse populations is a critical consideration. Personalized screening approaches and equitable access to advanced technologies are essential to mitigate disparities. In conclusion, the breast cancer screening landscape is evolving, emphasizing the need for risk stratification, appropriate imaging modalities, and a personalized approach to reduce overdiagnosis and focus on cancers with the potential to impact lives while prioritizing patient-centered care.
Collapse
Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Gaetano Aurilio
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Matteo Lazzeroni
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Sara Gandini
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
| |
Collapse
|
38
|
Cao S, Wei Y, Yue Y, Chen Y, Liao S, Li A, Liu P, Xiong A, Zeng H. Targeting ferroptosis unveils a new era for traditional Chinese medicine: a scientific metrology study. Front Pharmacol 2024; 15:1366852. [PMID: 38464725 PMCID: PMC10921231 DOI: 10.3389/fphar.2024.1366852] [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: 01/07/2024] [Accepted: 02/02/2024] [Indexed: 03/12/2024] Open
Abstract
In the past 11 years, there has been a surge in studies exploring the regulatory effect of Traditional Chinese Medicine (TCM) on ferroptosis. However, a significant gap persists in comprehensive scientometric analysis and scientific mapping research, especially in tracking the evolution, primary contributors, and emerging research focal points. This study aims to comprehensively update the advancements in targeting ferroptosis with various TCMs during the previous 11 years. The data, covering the period from 1 January 2012, to 30 November 2023, were retrieved from the Web of Science database. For in-depth scientometric and visualized analyses, a series of advanced analytical instruments were employed. The findings highlight China's predominant role, accounting for 71.99% of total publications and significantly shaping research in this domain. Noteworthy productivity was observed at various institutions, including Guangzhou University of Chinese Medicine, Chengdu University of Traditional Chinese Medicine, and Zhejiang University. Thomas Efferth emerged as the foremost author within this field, while Frontiers in Pharmacology boasted the highest publication count. This study pinpointed hepatocellular carcinoma, chemical and drug-induced liver injury, mitochondrial diseases, acute kidney injury, and liver failure as the most critical disorders addressed in this research realm. The research offers a comprehensive bibliometric evaluation, enhancing our understanding of the present status of TCM therapy in managing ferroptosis-related diseases. Consequently, it aids both seasoned researchers and newcomers by accelerating access to vital information and fostering innovative concept extraction within this specialized field.
Collapse
Affiliation(s)
- Siyang Cao
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yihao Wei
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yaohang Yue
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yingqi Chen
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Shuai Liao
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Aikang Li
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Peng Liu
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Ao Xiong
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Hui Zeng
- National and Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| |
Collapse
|
39
|
Zong H, Li J, Wu E, Wu R, Lu J, Shen B. Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses. BMC MEDICAL EDUCATION 2024; 24:143. [PMID: 38355517 PMCID: PMC10868058 DOI: 10.1186/s12909-024-05125-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). METHODS We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. RESULTS ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. CONCLUSIONS These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.
Collapse
Affiliation(s)
- Hui Zong
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China
| | - Erman Wu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China
| | - Rongrong Wu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China
| | - Junyu Lu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.
| |
Collapse
|
40
|
Echecopar C, Abad I, Galán-Gómez V, Mozo Del Castillo Y, Sisinni L, Bueno D, Ruz B, Pérez-Martínez A. An artificial intelligence-driven predictive model for pediatric allogeneic hematopoietic stem cell transplantation using clinical variables. Eur J Haematol 2024. [PMID: 38333914 DOI: 10.1111/ejh.14184] [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: 10/31/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision-making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. METHODS We studied allogenic HSCTs performed on children with malignant disorders at a third-level hospital between 1991 and 2021. Survival was analyzed using the Kaplan-Meier method, log-rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1-year survival after HSCT. RESULTS We analyzed 229 HSCTs in 201 patients with a median follow-up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12-1.76, p = .003), oldest period of HSCT (HR 0.46, 95% CI 0.29-0.73, p < .001), and mismatched donor (HR 2.65, 95% CI 1.51-4.65, p = .001). Our prognostic index was associated with 3-year overall survival (OS; p < .001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1-year OS. CONCLUSIONS Our index and random forest was effective in predicting 1-year survival. However, further validation in diverse populations is necessary to establish their generalizability.
Collapse
Affiliation(s)
- Carlos Echecopar
- Pediatric Hemato-Oncology, La Paz University Hospital, Madrid, Spain
| | - Inés Abad
- Pediatric Department, Autonomous University of Madrid, Madrid, Spain
| | - Víctor Galán-Gómez
- Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain
| | | | - Luisa Sisinni
- Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain
| | - David Bueno
- Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain
| | - Beatriz Ruz
- Institute of Medical and Molecular Genetics (INGEMM) idiPAZ Research Institute, La Paz University Hospital, Madrid, Spain
| | - Antonio Pérez-Martínez
- Pediatric Hemato-Oncology, La Paz University Hospital, Madrid, Spain
- Pediatric Department, Autonomous University of Madrid, Madrid, Spain
- Pediatric Hemato-Oncology, La Paz University Hospital, idiPAZ Research Institute, Madrid, Spain
- Institute of Medical and Molecular Genetics (INGEMM) idiPAZ Research Institute, La Paz University Hospital, Madrid, Spain
| |
Collapse
|
41
|
Wang LJ, Ning M, Nayak T, Kasper MJ, Monga SP, Huang Y, Chen Y, Chiu YC. shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning. PATTERNS (NEW YORK, N.Y.) 2024; 5:100894. [PMID: 38370127 PMCID: PMC10873157 DOI: 10.1016/j.patter.2023.100894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 02/20/2024]
Abstract
Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.
Collapse
Affiliation(s)
- Li-Ju Wang
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Michael Ning
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Tapsya Nayak
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Michael J. Kasper
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Satdarshan P. Monga
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Yufei Huang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Cancer Virology Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yu-Chiao Chiu
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| |
Collapse
|
42
|
Feng X, Shu W, Li M, Li J, Xu J, He M. Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview. J Transl Med 2024; 22:131. [PMID: 38310237 PMCID: PMC10837897 DOI: 10.1186/s12967-024-04915-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/20/2024] [Indexed: 02/05/2024] Open
Abstract
The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.
Collapse
Affiliation(s)
- Xiaobing Feng
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wen Shu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Mingya Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyu Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyao Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Min He
- College of Electrical and Information Engineering, Hunan University, Changsha, China.
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
| |
Collapse
|
43
|
Palomino-Fernández D, Seiffert AP, Gómez-Grande A, Jiménez López-Guarch C, Moreno G, Bueno H, Gómez EJ, Sánchez-González P. Robustness of [ 18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107981. [PMID: 38154326 DOI: 10.1016/j.cmpb.2023.107981] [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: 07/27/2023] [Revised: 11/01/2023] [Accepted: 12/12/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity. METHODS Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated. RESULTS The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified. CONCLUSIONS Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.
Collapse
Affiliation(s)
- David Palomino-Fernández
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain.
| | - Alexander P Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain
| | - Adolfo Gómez-Grande
- Department of Nuclear Medicine, Hospital Universitario 12 de Octubre, Spain; Facultad de Medicina, Universidad Complutense de Madrid, Spain
| | - Carmen Jiménez López-Guarch
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain
| | - Guillermo Moreno
- Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Facultad de Enfermería, Fisioterapia y Podología, Universidad Complutense de Madrid, Spain
| | - Héctor Bueno
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Spain
| | - Enrique J Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain.
| |
Collapse
|
44
|
Hofman P, Berezowska S, Kazdal D, Mograbi B, Ilié M, Stenzinger A, Hofman V. Current challenges and practical aspects of molecular pathology for non-small cell lung cancers. Virchows Arch 2024; 484:233-246. [PMID: 37801103 PMCID: PMC10948551 DOI: 10.1007/s00428-023-03651-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023]
Abstract
The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.
Collapse
Affiliation(s)
- Paul Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France.
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France.
| | - Sabina Berezowska
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Daniel Kazdal
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Baharia Mograbi
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Marius Ilié
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Albrecht Stenzinger
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Véronique Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| |
Collapse
|
45
|
Truhn D, Tayebi Arasteh S, Saldanha OL, Müller-Franzes G, Khader F, Quirke P, West NP, Gray R, Hutchins GGA, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Brobeil A, Yuan T, Chang-Claude J, Hoffmeister M, Foersch S, Han T, Keil S, Schulze-Hagen M, Isfort P, Bruners P, Kaissis G, Kuhl C, Nebelung S, Kather JN. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Med Image Anal 2024; 92:103059. [PMID: 38104402 PMCID: PMC10804934 DOI: 10.1016/j.media.2023.103059] [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: 08/25/2022] [Revised: 04/28/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.
Collapse
Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Oliver Lester Saldanha
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Nicholas P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Richard Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Jenny Chang-Claude
- Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Tianyu Han
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Sebastian Keil
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Maximilian Schulze-Hagen
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Peter Isfort
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philipp Bruners
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Georgios Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany; Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| |
Collapse
|
46
|
Aamir A, Iqbal A, Jawed F, Ashfaque F, Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, Khan M, Mansoor T. Exploring the current and prospective role of artificial intelligence in disease diagnosis. Ann Med Surg (Lond) 2024; 86:943-949. [PMID: 38333305 PMCID: PMC10849462 DOI: 10.1097/ms9.0000000000001700] [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: 10/31/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024] Open
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.
Collapse
Affiliation(s)
- Ali Aamir
- Department of Medicine, Dow University of Health Sciences
| | - Arham Iqbal
- Department of Medicine, Dow International Medical College, Karachi, Pakistan
| | - Fareeha Jawed
- Department of Medicine, Dow University of Health Sciences
| | - Faiza Ashfaque
- Department of Medicine, Dow University of Health Sciences
| | - Hafiza Hafsa
- Department of Medicine, Dow University of Health Sciences
| | - Zahra Anas
- Department of Medicine, Dow University of Health Sciences
| | - Malik Olatunde Oduoye
- Department of Research, Medical Research Circle, Bukavu, Democratic Republic of Congo
| | - Abdul Basit
- Department of Medicine, Dow University of Health Sciences
| | - Shaheer Ahmed
- Department of Medicine, Dow University of Health Sciences
| | | | - Mushkbar Khan
- Liaquat National Hospital and Medical College, Pakistan
| | | |
Collapse
|
47
|
Nguyen HS, Ho DKN, Nguyen NN, Tran HM, Tam KW, Le NQK. Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis. Acad Radiol 2024; 31:660-683. [PMID: 37120403 DOI: 10.1016/j.acra.2023.03.040] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 05/01/2023]
Abstract
RATIONALE AND OBJECTIVES Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC. MATERIALS AND METHODS We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738. RESULTS Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value < 0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance. CONCLUSION DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.
Collapse
Affiliation(s)
- Hung Song Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (H.S.N., N.N.N.); Department of Pediatrics, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam (H.S.N.); Intensive Care Unit Department, Children's Hospital 1, Ho Chi Minh City, Viet Nam (H.S.N.)
| | - Dang Khanh Ngan Ho
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan (D.K.N.H.)
| | - Nam Nhat Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (H.S.N., N.N.N.)
| | - Huy Minh Tran
- Department of Neurosurgery, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam (H.M.T.)
| | - Ka-Wai Tam
- Center for Evidence-based Health Care, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan (K.-W.T.); Cochrane Taiwan, Taipei Medical University, Taipei City, Taiwan (K.-W.T.); Division of General Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan (K.-W.T.); Division of General Surgery, Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (K.-W.T.)
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan (N.Q.K.L.).
| |
Collapse
|
48
|
Bailleux C, Gal J, Chamorey E, Mograbi B, Milano G. Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head. Pharmaceutics 2024; 16:211. [PMID: 38399265 PMCID: PMC10893490 DOI: 10.3390/pharmaceutics16020211] [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: 12/25/2023] [Revised: 01/22/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.
Collapse
Affiliation(s)
- Caroline Bailleux
- Centre Antoine Lacassagne, Oncology Departement Unit, University Côte d’Azur, 06000 Nice, France;
| | - Jocelyn Gal
- Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, University Côte d’Azur, 06000 Nice, France; (J.G.); (E.C.)
| | - Emmanuel Chamorey
- Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, University Côte d’Azur, 06000 Nice, France; (J.G.); (E.C.)
| | | | - Gérard Milano
- Centre Antoine Lacassagne, University Côte d’Azur, 33 Avenue de Valombrose, 06189 Nice, France
| |
Collapse
|
49
|
Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [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: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
Collapse
Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| |
Collapse
|
50
|
Lu T, Lu M, Wu D, Ding YY, Liu HN, Li TT, Song DQ. Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study. World J Gastrointest Surg 2024; 16:85-94. [PMID: 38328326 PMCID: PMC10845275 DOI: 10.4240/wjgs.v16.i1.85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted. AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice. METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model. RESULTS Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive. CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.
Collapse
Affiliation(s)
- Tong Lu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Miao Lu
- Wuxi Mental Health Center, Wuxi 214000, Jiangsu Province, China
| | - Dong Wu
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Yuan-Yuan Ding
- Department of Gastroenterology, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Hao-Nan Liu
- Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, Jiangsu Province, China
| | - Tao-Tao Li
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| | - Da-Qing Song
- Department of Emergency Medicine, Jining No. 1 People’s Hospital, Jining 272000, Shandong Province, China
| |
Collapse
|