1
|
Cegla P, Currie GM, Cholewinski W, Bryl M, Trojanowski M, Matuszewski K, Piotrowski T, Czepczyński R. [ 18F]fluorodeoxyglucose positron emission tomography/computed tomography in combination with clinical data in predicting overall survival in non-small-cell lung cancer patients: A retrospective study. Radiography (Lond) 2024; 30:971-977. [PMID: 38663216 DOI: 10.1016/j.radi.2024.04.004] [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: 10/20/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/18/2024]
Abstract
INTRODUCTION Positron emission tomography/computed tomography (PET/CT) has an established role in evaluating patients with lung cancer. The aim of this work was to assess the predictive capability of [18F]Fluorodeoxyglucose ([18F]FDG) PET/CT parameters on overall survival (OS) in lung cancer patients using an artificial neural network (ANN) in parallel with conventional statistical analysis. METHODS Retrospective analysis was performed on a group of 165 lung cancer patients (98M, 67F). PET features associated with the primary tumor: maximum and mean standardized uptake value (SUVmax, SUVmean), total lesion glycolysis (TLG) metabolic tumor volume (MTV) and area under the curve-cumulative SUV histogram (AUC-CSH) and metastatic lesions (SUVmaxtotal, SUVmeantotal, TLGtotal, and MTVtotal) were evaluated. In parallel with conventional statistical analysis (Chi-Square analysis for nominal data, Student's t test for continuous data), the data was evaluated using an ANN. There were 97 input variables in 165 patients using a binary classification of either below, or greater than/equal to median survival post primary diagnosis. Additionally, phantom study was performed to assess the most optimal contouring method. RESULTS Males had statistically higher SUVmax (mean: 10.7 vs 8.9; p = 0.020), MTV (mean: 66.5 cm3 vs. 21.5 cm3; p = 0.001), TLG (mean 404.7 vs. 115.0; p = 0.003), TLGtotal (mean: 946.7 vs. 433.3; p = 0.014) and MTVtotal (mean: 242.0 cm3 vs. 103.7 cm3; p = 0.027) than females. The ANN after training and validation was optimised with a final architecture of 4 scaling layer inputs (TLGtotal, SUVmaxtotal, SUVmeantotal and disease stage) and receiving operator characteristic (ROC) analysis demonstrated an AUC of 0.764 (sensitivity of 92.3%, specificity of 57.1%). CONCLUSION Conventional statistical analysis and the ANN provided concordant findings in relation to variables that predict decreased survival. The ANN provided a weighted algorithm of the 4 key features to predict decreased survival. IMPLICATION FOR PRACTICE Identification of parameters which can predict survival in lung cancer patients might be helpful in choosing the group of patients who require closer look during the follow-up.
Collapse
Affiliation(s)
- P Cegla
- Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznań, Poland.
| | - G M Currie
- School of Dentistry and Health Science, Charles Sturt University, Wagga Wagga, Australia
| | - W Cholewinski
- Department of Electroradiology, Poznan University of Medical Sciences, Poznań, Poland; Department of Nuclear Medicine, Greater Poland Cancer Centre, Poznań, Poland
| | - M Bryl
- Oncology Department at Regional Centre of Lung Diseases in Poznan and Department of Thoracic Surgery, Poznan University of Medical Sciences, Poznań, Poland
| | - M Trojanowski
- Greater Poland Cancer Registry, Greater Poland Cancer Centre, Poznań, Poland
| | - K Matuszewski
- Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - T Piotrowski
- Department of Electroradiology, Poznan University of Medical Sciences, Poznań, Poland; Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - R Czepczyński
- Department of Nuclear Medicine, Affidea Poznań, Poland; Department of Endocrinology, Metabolism and Internal Diseases, Poznan University of Medical Sciences, Poznań, Poland
| |
Collapse
|
2
|
Atwal K. Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence. Nutr Clin Pract 2024. [PMID: 38591653 DOI: 10.1002/ncp.11150] [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: 01/04/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.
Collapse
Affiliation(s)
- Kiranjit Atwal
- Department of Nutritional Sciences, King's College London, London, UK
- School of Health Professions, University of Plymouth, Plymouth, UK
| |
Collapse
|
3
|
Pawar VV, Farooqui S. Revolutionizing oral oncology: The role of artificial intelligence. Oral Oncol 2024; 150:106702. [PMID: 38271777 DOI: 10.1016/j.oraloncology.2024.106702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Vikas V Pawar
- Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning Sant-Tukaram Nagar, Pimpri, Pune 411018, MH, India.
| | - Safia Farooqui
- Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning Sant-Tukaram Nagar, Pimpri, Pune 411018, MH, India
| |
Collapse
|
4
|
Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
Collapse
Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
| |
Collapse
|
5
|
Bravo-Garzón MA, Bornstein-Quevedo L, de Camargo VP, Sanku G, Jansen AM, de Macedo MP, Rico-Restrepo M, Chacón M. BRAF-Mutated Melanoma Journey in Latin America: Expert Recommendations From Diagnosis to Treatment. Cancer Control 2024; 31:10732748241251572. [PMID: 38751033 PMCID: PMC11100406 DOI: 10.1177/10732748241251572] [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: 10/29/2023] [Revised: 03/28/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024] Open
Abstract
OBJECTIVES • Gather a panel of Latin American experts in testing and treating BRAF-melanoma. • Describe the current landscape of BRAF-mutated melanoma in Latin America. • Outline the current gaps in testing and recommend improvements for testing and treating BRAF-mutated melanoma in the region. INTRODUCTION Melanoma prevalence in Latin America is lower than in high- and middle-income countries. However, recent data indicate that the region's incidence and mortality are rising, with more stage IV patients being diagnosed. According to international clinical practice guidelines, conducting BRAF-mutation testing in patients with stage III or stage IV melanoma and high-risk resected disease is imperative. Still, BRAF-mutation testing and targeted therapies are inconsistently available in the region. METHODS Americas Health Foundation convened a meeting of Latin American experts on BRAF-mutated melanoma to develop guidelines and recommendations for diagnosis through treatment. RESULTS AND CONCLUSIONS Some recommendations for improving diagnostics through improving access and reducing the cost of BRAF-mutation testing, enhancing efficiency in pathology laboratories, and creating country-specific local guidelines. The panel also gave treatment recommendations for neo-adjuvant therapy, adjuvant therapy, and therapy for patients with metastatic disease in Latin America.
Collapse
Affiliation(s)
- María A. Bravo-Garzón
- Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center – CTIC, Bogotá D.C., Colombia
| | | | | | | | | | | | | | - Matías Chacón
- Alexander Fleming Institute, Buenos Aires, Argentina
| |
Collapse
|
6
|
Xavier D, Miyawaki I, Campello Jorge CA, Freitas Silva GB, Lloyd M, Moraes F, Patel B, Batalini F. Artificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software. J Med Screen 2023:9691413231219952. [PMID: 38115810 DOI: 10.1177/09691413231219952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
OBJECTIVE Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. METHODS PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. RESULTS Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655-0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882-0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684-0.723, I² = 97.5%, p < 0.01). CONCLUSIONS The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.
Collapse
Affiliation(s)
| | | | | | | | - Maxwell Lloyd
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Fabio Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | | | | |
Collapse
|
7
|
Weerarathna IN, Kamble AR, Luharia A. Artificial Intelligence Applications for Biomedical Cancer Research: A Review. Cureus 2023; 15:e48307. [PMID: 38058345 PMCID: PMC10697339 DOI: 10.7759/cureus.48307] [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: 10/27/2023] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care.
Collapse
Affiliation(s)
- Induni N Weerarathna
- Biomedical Sciences, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aahash R Kamble
- Artificial Intelligence and Data Science, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anurag Luharia
- Radiotherapy, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| |
Collapse
|
8
|
Hesso I, Kayyali R, Dolton DR, Joo K, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Nabhani-Gebara S. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals' perspectives on cancer care and artificial intelligence. Radiat Oncol 2023; 18:167. [PMID: 37814325 PMCID: PMC10561443 DOI: 10.1186/s13014-023-02351-z] [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: 02/22/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. METHODS A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. RESULTS The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. CONCLUSION HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.
Collapse
Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Debbie-Rose Dolton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Kwanyoung Joo
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Andreas Charalambous
- Cyprus University of Technology, Limassol, Cyprus
- University of Turku, Turku, Finland
| | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, 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, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK.
| |
Collapse
|
9
|
Huang L, Ye X, Wu F, Wang X, Qiu M. Study of prevalence and risk factors of chemotherapy-induced mucositis in gastrointestinal cancer using machine learning models. Front Oncol 2023; 13:1138992. [PMID: 37841443 PMCID: PMC10569816 DOI: 10.3389/fonc.2023.1138992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 08/30/2023] [Indexed: 10/17/2023] Open
Abstract
Objective Chemotherapy-induced mucositis (CIM) significantly impacts clinical outcomes and diminishes the quality of life in patients with gastrointestinal cancer. This study aims to prospectively determine the incidence, severity, and underlying risk factors associated with CIM in this patient population. Methods To achieve this objective, we introduce a novel Machine Learning-based Toxicity Prediction Model (ML-TPM) designed to analyze the risk factors contributing to CIM development in gastrointestinal cancer patients. Within the winter season spanning from December 15th, 2018 to January 14th, 2019, we conducted in-person interviews with patients undergoing chemotherapy for gastrointestinal cancer. These interviews encompassed comprehensive questionnaires pertaining to patient demographics, CIM incidence, severity, and any supplementary prophylactic measures employed. Results The study encompassed a cohort of 447 participating patients who provided complete questionnaire responses (100%). Of these, 328 patients (73.4%) reported experiencing CIM during the course of their treatment. Notably, CIM-induced complications led to treatment discontinuation in 14 patients (3%). The most frequently encountered CIM symptoms were diarrhea (41.6%), followed by nausea (37.8%), vomiting (25.1%), abdominal pain (21%), gastritis (10.5%), and oral pain (10.3%). Supplementary prophylaxis was administered to approximately 62% of the patients. The analysis revealed significant correlations between the overall incidence of CIM and gender (p=0.015), number of chemotherapy cycles exceeding one (p=0.039), utilization of platinum-based regimens (p=0.039), and administration of irinotecan (p=0.003). Specifically, the incidence of diarrhea exhibited positive correlations with prior surgical history (p=0.037), irinotecan treatment (p=0.021), and probiotics usage (p=0.035). Conversely, diarrhea incidence demonstrated an adverse correlation with platinum-based treatment (p=0.026). Conclusion In conclusion, this study demonstrates the successful implementation of the ML-TPM model for automating toxicity prediction with accuracy comparable to conventional physical analyses. Our findings provide valuable insights into the identification of CIM risk factors among gastrointestinal cancer patients undergoing chemotherapy. Furthermore, the results underscore the potential of machine learning in enhancing our understanding of chemotherapy-induced mucositis and advancing personalized patient care strategies.
Collapse
Affiliation(s)
- Lin Huang
- Division of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xianhui Ye
- Division of Medical Oncology, Colorectal Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Fengqing Wu
- Department of Abdominal Cancer, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiuyun Wang
- Department of Abdominal Cancer, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Meng Qiu
- Division of Medical Oncology, Colorectal Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
10
|
Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2023:S0936-6555(23)00334-5. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
Collapse
Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
| |
Collapse
|
11
|
Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, von Bubnoff N, Eckardt JN, Foersch S, Loeffler CML, Middeke JM, Mueller ML, Oellerich T, Risse B, Scherag A, Schliemann C, Scholz M, Spang R, Thielscher C, Tsoukakis I, Kather JN. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023; 149:7997-8006. [PMID: 36920563 PMCID: PMC10374829 DOI: 10.1007/s00432-023-04667-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.
Collapse
Affiliation(s)
- Wiebke Rösler
- Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Beissbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Gernot Beutel
- Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Robert Bock
- IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Jan-Niklas Eckardt
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Chiara M L Loeffler
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | | | - Thomas Oellerich
- Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany
| | - Benjamin Risse
- Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | | | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany
| | | | - Ioannis Tsoukakis
- Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany
| | - Jakob Nikolas Kather
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
| |
Collapse
|
12
|
Charalambous A, Dodlek N. Big Data, Machine Learning, and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges. Semin Oncol Nurs 2023; 39:151429. [PMID: 37085405 DOI: 10.1016/j.soncn.2023.151429] [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: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/23/2023]
Abstract
OBJECTIVES The rapid advances in artificial intelligence (AI), big data, and machine learning (ML) technologies hold promise for personalized, equitable cancer care and improved health outcomes within the context of cancer and beyond. Furthermore, integrating these technologies into cancer research has been effective in addressing many of the challenges for cancer control and cure. This can be achieved through the insights generated from massive amounts of data, in ways that can help inform decisions, interventions, and precision cancer care. AI, big data, and ML technologies offer, either in isolation or in combination, unconventional pathways that facilitate the better understanding and management of cancer and its impact on the person. The value of AI, big data, and ML technologies has been acknowledged and integrated within the Cancer Moonshot program in the U.S. and the EU Beating Cancer Plan in Europe. DATA SOURCES Relevant studies on the topic have formed the basis for this article. CONCLUSION In a shifting health care environment where cancer care is becoming more complex and demanding, big data and AI technologies can act as a vehicle to facilitating the care continuum. An increasing body of literature demonstrates their impactful contributions in areas such as treatment and diagnosis. These technologies, however, create additional requirements from health care professionals in terms of capacity and preparedness to integrate them effectively and efficiently in clinical practice. Therefore, there is an increasing need for investment and training in oncology to combat and overcome some of the challenges posed by cancer control. IMPLICATIONS FOR NURSING PRACTICE AI, big data, and ML are increasingly integrated in various aspects of health care. As a result, health care professionals, including nurses, will need to adjust in an ever-changing practice environment where these technologies have potential applications in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
Collapse
Affiliation(s)
- Andreas Charalambous
- Associate Professor, Oncology and Palliative Care, Cyprus University of Technology, Osijek, Croatia.
| | - Nikolina Dodlek
- Adjunct Professor, University of Turku, Turku, Finland; Teaching Assistant, Faculty of Dental Medicine and Health, Department of Nursing and Palliative Care, Osijek, Croatia; Unit Manager, Department for Oncology, Clinical Hospital Center, Osijek, Croatia
| |
Collapse
|
13
|
Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
Collapse
Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
| |
Collapse
|
14
|
Govindan B, Sabri MA, Hai A, Banat F, Haija MA. A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach. Pharmaceutics 2023; 15:pharmaceutics15030868. [PMID: 36986729 PMCID: PMC10058002 DOI: 10.3390/pharmaceutics15030868] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/10/2023] Open
Abstract
The new era of nanomedicine offers significant opportunities for cancer diagnostics and treatment. Magnetic nanoplatforms could be highly effective tools for cancer diagnosis and treatment in the future. Due to their tunable morphologies and superior properties, multifunctional magnetic nanomaterials and their hybrid nanostructures can be designed as specific carriers of drugs, imaging agents, and magnetic theranostics. Multifunctional magnetic nanostructures are promising theranostic agents due to their ability to diagnose and combine therapies. This review provides a comprehensive overview of the development of advanced multifunctional magnetic nanostructures combining magnetic and optical properties, providing photoresponsive magnetic platforms for promising medical applications. Moreover, this review discusses various innovative developments using multifunctional magnetic nanostructures, including drug delivery, cancer treatment, tumor-specific ligands that deliver chemotherapeutics or hormonal agents, magnetic resonance imaging, and tissue engineering. Additionally, artificial intelligence (AI) can be used to optimize material properties in cancer diagnosis and treatment, based on predicted interactions with drugs, cell membranes, vasculature, biological fluid, and the immune system to enhance the effectiveness of therapeutic agents. Furthermore, this review provides an overview of AI approaches used to assess the practical utility of multifunctional magnetic nanostructures for cancer diagnosis and treatment. Finally, the review presents the current knowledge and perspectives on hybrid magnetic systems as cancer treatment tools with AI models.
Collapse
Affiliation(s)
- Bharath Govindan
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Department of Chemistry, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence: (B.G.); (M.A.H.); Tel.: +971-2-4150 (B.G.)
| | - Muhammad Ashraf Sabri
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Abdul Hai
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Fawzi Banat
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Mohammad Abu Haija
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Advanced Materials Chemistry Center (AMCC), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence: (B.G.); (M.A.H.); Tel.: +971-2-4150 (B.G.)
| |
Collapse
|
15
|
Guerrisi A, Falcone I, Valenti F, Rao M, Gallo E, Ungania S, Maccallini MT, Fanciulli M, Frascione P, Morrone A, Caterino M. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications. Cells 2022; 11:cells11243965. [PMID: 36552729 PMCID: PMC9777238 DOI: 10.3390/cells11243965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.
Collapse
Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
- Correspondence:
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Marco Rao
- Enea-FSN-TECFIS-APAM, C.R. Frascati, via Enrico Fermi, 45, 00146 Rome, Italy
| | - Enzo Gallo
- Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy
| | - Maria Teresa Maccallini
- Departement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy
| | - Maurizio Fanciulli
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Pasquale Frascione
- Oncologic and Preventative Dermatology, IFO-San Gallicano Dermatological Institute-IRCCS, 00144 Rome, Italy
| | - Aldo Morrone
- Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| |
Collapse
|
16
|
Adeoye J, Akinshipo A, Koohi-Moghadam M, Thomson P, Su YX. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022; 12:976168. [DOI: 10.3389/fonc.2022.976168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
Collapse
|
17
|
Sussman L, Garcia-Robledo JE, Ordóñez-Reyes C, Forero Y, Mosquera AF, Ruíz-Patiño A, Chamorro DF, Cardona AF. Integration of artificial intelligence and precision oncology in Latin America. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:1007822. [PMID: 36311461 PMCID: PMC9608820 DOI: 10.3389/fmedt.2022.1007822] [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: 07/31/2022] [Accepted: 09/21/2022] [Indexed: 11/07/2022] Open
Abstract
Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.
Collapse
Affiliation(s)
- Liliana Sussman
- Department of Neurology, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia,Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia
| | - Juan Esteban Garcia-Robledo
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ, United States
| | - Camila Ordóñez-Reyes
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Yency Forero
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Mosquera
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Alejandro Ruíz-Patiño
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Diego F. Chamorro
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Cardona
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia,Direction of Research, Science and Education, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (CTIC), Bogotá, Colombia,Correspondence: Andrés F. Cardona
| |
Collapse
|
18
|
Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, Liu SL, Yan QY. Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review. Asia Pac J Oncol Nurs 2022; 9:100127. [PMID: 36176267 PMCID: PMC9513729 DOI: 10.1016/j.apjon.2022.100127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/29/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.
Collapse
Affiliation(s)
- Lu-Chen Pan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xiao-Ru Wu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ying Lu
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Han-Qing Zhang
- Health Science Center, Yangtze University, Jinzhou 434023, China
| | - Yao-Ling Zhou
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xue Liu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sheng-Lin Liu
- Department of Medical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
| | - Qiao-Yuan Yan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
| |
Collapse
|
19
|
Artificial intelligence in interdisciplinary life science and drug discovery research. Future Sci OA 2022; 8:FSO792. [PMID: 35369273 PMCID: PMC8965817 DOI: 10.2144/fsoa-2022-0010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 02/23/2022] [Indexed: 11/23/2022] Open
|