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Obimba DC, Esteva C, Nzouatcham Tsicheu EN, Wong R. Effectiveness of Artificial Intelligence Technologies in Cancer Treatment for Older Adults: A Systematic Review. J Clin Med 2024; 13:4979. [PMID: 39274201 PMCID: PMC11396550 DOI: 10.3390/jcm13174979] [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: 06/20/2024] [Revised: 07/29/2024] [Accepted: 08/21/2024] [Indexed: 09/16/2024] Open
Abstract
Background: Aging is a multifaceted process that may lead to an increased risk of developing cancer. Artificial intelligence (AI) applications in clinical cancer research may optimize cancer treatments, improve patient care, and minimize risks, prompting AI to receive high levels of attention in clinical medicine. This systematic review aims to synthesize current articles about the effectiveness of artificial intelligence in cancer treatments for older adults. Methods: We conducted a systematic review by searching CINAHL, PsycINFO, and MEDLINE via EBSCO. We also conducted forward and backward hand searching for a comprehensive search. Eligible studies included a study population of older adults (60 and older) with cancer, used AI technology to treat cancer, and were published in a peer-reviewed journal in English. This study was registered on PROSPERO (CRD42024529270). Results: This systematic review identified seven articles focusing on lung, breast, and gastrointestinal cancers. They were predominantly conducted in the USA (42.9%), with others from India, China, and Germany. The measures of overall and progression-free survival, local control, and treatment plan concordance suggested that AI interventions were equally or less effective than standard care in treating older adult cancer patients. Conclusions: Despite promising initial findings, the utility of AI technologies in cancer treatment for older adults remains in its early stages, as further developments are necessary to enhance accuracy, consistency, and reliability for broader clinical use.
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Affiliation(s)
- Doris C Obimba
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Charlene Esteva
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Eurika N Nzouatcham Tsicheu
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Roger Wong
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Department of Geriatrics, SUNY Upstate Medical University, Syracuse, NY 13210, USA
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Chapla D, Chorya HP, Ishfaq L, Khan A, Vr S, Garg S. An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review. Cureus 2024; 16:e66688. [PMID: 39268329 PMCID: PMC11390952 DOI: 10.7759/cureus.66688] [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: 06/26/2024] [Accepted: 08/11/2024] [Indexed: 09/15/2024] Open
Abstract
Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.
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Affiliation(s)
- Deep Chapla
- Medicine, Jiangsu University, Zhenjiang, CHN
| | | | - Lyluma Ishfaq
- Medicine, Directorate of Health Services Kashmir, Srinagar, IND
| | - Afrasayab Khan
- Internal Medicine, Central Michigan University College of Medicine, Saginaw, USA
| | - Subrahmanyan Vr
- Internal Medicine Pediatrics, Armed Forces Medical College, Pune, IND
| | - Sheenam Garg
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
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Jacob M, Fournel P, Tissot C, Cadranel J, Bylicki O, Monnet I, Justeau G, Ricordel C, Thomas P, Falchero L, Locher C, Wislez M, Vergnenegre A, Abdiche S, Guisier F, Bizieux A, Lamy R, François G, De Chabot G, Pierret T, Sabatini M, Abeillera M, Vieillot S, Martinez S, Morel H, Doubre H, Madroszyk A, Geier M, LucLabourey J, Chouaïd C, Greillier L. A prospective analysis of the management practices for patients with Stage-III-N2Non-Small-Cell lung cancer (OBSERVE IIIA-B GFPC 04-2020Study). Lung Cancer 2024; 194:107868. [PMID: 39003937 DOI: 10.1016/j.lungcan.2024.107868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/31/2024] [Accepted: 06/29/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Management of stage-III-N2 non-small-cell lung cancer (NSCLC) based on a multimodal strategy (surgery or radiotherapycombined with systemic drugs) remains controversial. Patients are treated with a curative intent, and available data suggestprolonged survival after complete resection. However, no consensual definition of "tumor resectability" exists. This study aimed to analyze the concordanceamong French tumor board meeting (TBM)-emittedtherapeutic decisions forstage-III-N2 NSCLC. METHODS Six patients with stage-III-N2 NSCLC discussed at Saint-Etienne University Hospital'sthoracic TBMs were selected, anonymouslyreported, and submitted to the participating TBMs. The primary goal of this multicenter, prospective, observational study was to assess the consistency of TBMpanel decisions for each case. The secondary endpointwas identifying the demographic or technical factors that potentiallyaffected decision-making. RESULTS Twenty-seven TBMs from university hospitals, a cancer center, general hospitals, and a private hospitalparticipated in this study. None of their decisions for the six cases were unanimous.The decisions were homogenous for three cases (78%, 85%, and 88% TBMs opted for medical treatment, respectively),andmore ambivalent for the other three (medical versus surgical strategies were favored by 44%/56%, 46%/54%, and 58%/42% TBMs, respectively). Interestingly, decisions regarding chemoradiationand perioperative chemotherapyinthe medical and surgical strategies, respectively, were also discordant. Hospital type, specialist participation in TBMs, and activity volumes were not significantly associated with therapeutic decisions. CONCLUSION The results of this study highlight substantial disparities amongFrench TBMs regarding therapeutic management of stage-III-N2 NSCLC. The decisions were not associated with local conditions.
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Affiliation(s)
- Mathilde Jacob
- Department of Pneumonology and Thoracic Oncology, CHU, Saint-Etienne, France
| | - Pierre Fournel
- Department of Pneumonology and Thoracic Oncology, CHU, Saint-Etienne, France
| | - Claire Tissot
- Oncology Department, Loire Private Hospital (HPL), Saint-Etienne, France
| | | | | | | | | | | | | | - Lionel Falchero
- Pneumology Department, CH Villefranche sur Soane,Villefranche sur Soane, France
| | | | - Marie Wislez
- Pneumology Department, Hôpital Cochin, APHP, France
| | | | - Samir Abdiche
- Pneumology Department, CH Libourne, Libourne, France
| | | | - Acya Bizieux
- Pneumology Department, CH, La Roche sur Yon, France
| | - Regine Lamy
- Pneumology Department, CH Lorient, Lorient, France
| | | | | | - Thomas Pierret
- Pneumology Department, Hospices civiles de Lyon, Lyon France
| | | | | | - Sabine Vieillot
- Service d'Oncologie, Centre Catalan oncologie Perpignan, Perpignan, France
| | | | - Hugues Morel
- Pneumology Department, CH d'Orleans, Orleans, France
| | | | - Anne Madroszyk
- Service d'Oncologie, Institut paolo Calmette, Marseille, France
| | | | | | | | - Laurent Greillier
- Aix-Marseille University, APHM, INSERM, CNRS, CRCM, Hospital Nord, MultidisciplinaryOncology and Therapeutic Innovations Department, Marseille, France
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Lococo F, Ghaly G, Chiappetta M, Flamini S, Evangelista J, Bria E, Stefani A, Vita E, Martino A, Boldrini L, Sassorossi C, Campanella A, Margaritora S, Mohammed A. Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review. Cancers (Basel) 2024; 16:1832. [PMID: 38791910 PMCID: PMC11119930 DOI: 10.3390/cancers16101832] [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: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.
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Affiliation(s)
- Filippo Lococo
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Galal Ghaly
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
| | - Marco Chiappetta
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Sara Flamini
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Jessica Evangelista
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Emilio Bria
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Alessio Stefani
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Emanuele Vita
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Antonella Martino
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Luca Boldrini
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Carolina Sassorossi
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Annalisa Campanella
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Stefano Margaritora
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Abdelrahman Mohammed
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
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Choo JM, Ryu HS, Kim JS, Cheong JY, Baek SJ, Kwak JM, Kim J. Conversational artificial intelligence (chatGPT™) in the management of complex colorectal cancer patients: early experience. ANZ J Surg 2024; 94:356-361. [PMID: 37905713 DOI: 10.1111/ans.18749] [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: 03/31/2023] [Revised: 10/05/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023]
Abstract
INTRODUCTION In 2022 chatGPT™ (OpenAI, San Francisco) was introduced to the public. The complex reasoning and the natural language processing (NLP) ability of the AI platform has generated much excitement about the potential applications. This study conducted a preliminary analysis of the chatGPT™'s ability to formulate a management plan in accordance with oncological principles for patients with colorectal cancer. METHODOLOGY Colorectal cancer cases discussed in the multidisciplinary tumor (MDT) board at a single tertiary institution between September 2022 and January 2023 were prospectively collected. The treatment recommendations made by the chatGPT™ for Stage IV, recurrent, synchronous colorectal cancer were analysed for adherence to oncological principles. The recommendations by chatGPT™ were compared with the decision plans made by the MDT. RESULTS In all cases, the chatGPT™ was able to adhere to oncological principles. The recommendations in all 30 cases factored in the patient's overall health and functional status. The oncological management recommendation concordance rate between chatGPT™ and the MDT was 86.7%. CONCLUSIONS This study shows a high concordance rate of the chatGPT™'s recommendations with that given by the MDT in the management of complex colorectal patients. This will need to be verified in a larger prospective study.
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Affiliation(s)
- Jeong Min Choo
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Hyo Seon Ryu
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Ji Seon Kim
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Ju Yong Cheong
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Se-Jin Baek
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Jung Myun Kwak
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
| | - Jin Kim
- Korea University Anam Hospital, Division of Colon and Rectal Surgery, Department of Surgery, Korea University College of Medicine, Seoul, South Korea
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Hendriks MP, Jager A, Ebben KCWJ, van Til JA, Siesling S. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review. Crit Rev Oncol Hematol 2024; 195:104267. [PMID: 38311011 DOI: 10.1016/j.critrevonc.2024.104267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/18/2023] [Accepted: 01/11/2024] [Indexed: 02/06/2024] Open
Abstract
Generating guideline-based recommendations during multidisciplinary team (MDT) meetings in solid cancers is getting more complex due to increasing amount of information needed to follow the guidelines. Usage of clinical decision support systems (CDSSs) can simplify and optimize decision-making. However, CDSS implementation is lagging behind. Therefore, we aim to compose a CDSS implementation model. By performing a scoping review of the currently reported CDSSs for MDT decision-making we determined 102 barriers and 86 facilitators for CDSS implementation out of 44 papers describing 20 different CDSSs. The most frequently reported barriers and facilitators for CDSS implementation supporting MDT decision-making concerned CDSS maintenance (e.g. incorporating guideline updates), validity of recommendations and interoperability with electronic health records. Based on the identified barriers and facilitators, we composed a CDSS implementation model describing clinical utility, analytic validity and clinical validity to guide CDSS integration more successfully in the clinical workflow to support MDTs in the future.
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Affiliation(s)
- Mathijs P Hendriks
- Department of Health Technology and Services Research, Technical Medical Center, University of Twente, PO Box 217, 7500 AE Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), PO Box 19079, 3501 DB Utrecht, the Netherlands; Department of Medical Oncology, Northwest Clinics, PO Box 501, 1800 AM Alkmaar, the Netherlands.
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, PO Box 2040, 3000 CA Rotterdam, the Netherlands.
| | - Kees C W J Ebben
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), PO Box 19079, 3501 DB Utrecht, the Netherlands.
| | - Janine A van Til
- Department of Health Technology and Services Research, Technical Medical Center, University of Twente, PO Box 217, 7500 AE Enschede, the Netherlands.
| | - Sabine Siesling
- Department of Health Technology and Services Research, Technical Medical Center, University of Twente, PO Box 217, 7500 AE Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), PO Box 19079, 3501 DB Utrecht, the Netherlands.
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Alattar M, Govind A, Mainali S. Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review. Bioengineering (Basel) 2024; 11:206. [PMID: 38534480 DOI: 10.3390/bioengineering11030206] [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: 12/04/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 03/28/2024] Open
Abstract
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
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Affiliation(s)
- Maha Alattar
- Division of Adult Neurology, Sleep Medicine, Vascular Neurology, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Alok Govind
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
| | - Shraddha Mainali
- Division of Vascular Neurology and Neurocritical Care, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
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Lukac S, Dayan D, Fink V, Leinert E, Hartkopf A, Veselinovic K, Janni W, Rack B, Pfister K, Heitmeir B, Ebner F. Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases. Arch Gynecol Obstet 2023; 308:1831-1844. [PMID: 37458761 PMCID: PMC10579162 DOI: 10.1007/s00404-023-07130-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/27/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? MATERIAL AND METHODS We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. RESULTS Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy "hormonal treatment". CONCLUSIONS Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.
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Affiliation(s)
- Stefan Lukac
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany.
| | - Davut Dayan
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Visnja Fink
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Elena Leinert
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Andreas Hartkopf
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Kristina Veselinovic
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Wolfgang Janni
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Brigitte Rack
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Kerstin Pfister
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Benedikt Heitmeir
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
| | - Florian Ebner
- Department of Gynecology and Obstetrics, University Hospital Ulm, Prittwitzstr. 43, 89075, Ulm, Germany
- Gynäkologische Gemeinschaftspraxis Freising & Moosburg, Munich, Germany
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Gandhi Z, Gurram P, Amgai B, Lekkala SP, Lokhandwala A, Manne S, Mohammed A, Koshiya H, Dewaswala N, Desai R, Bhopalwala H, Ganti S, Surani S. Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Cancers (Basel) 2023; 15:5236. [PMID: 37958411 PMCID: PMC10650618 DOI: 10.3390/cancers15215236] [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: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
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Affiliation(s)
- Zainab Gandhi
- Department of Internal Medicine, Geisinger Wyoming Valley Medical Center, Wilkes Barre, PA 18711, USA
| | - Priyatham Gurram
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Birendra Amgai
- Department of Internal Medicine, Geisinger Community Medical Center, Scranton, PA 18510, USA;
| | - Sai Prasanna Lekkala
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Alifya Lokhandwala
- Department of Medicine, Jawaharlal Nehru Medical College, Wardha 442001, India;
| | - Suvidha Manne
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Adil Mohammed
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48602, USA;
| | - Hiren Koshiya
- Department of Internal Medicine, Prime West Consortium, Inglewood, CA 92395, USA;
| | - Nakeya Dewaswala
- Department of Cardiology, University of Kentucky, Lexington, KY 40536, USA;
| | - Rupak Desai
- Independent Researcher, Atlanta, GA 30079, USA;
| | - Huzaifa Bhopalwala
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Shyam Ganti
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Salim Surani
- Departmet of Pulmonary, Critical Care Medicine, Texas A&M University, College Station, TX 77845, USA;
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10
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Oehring R, Ramasetti N, Ng S, Roller R, Thomas P, Winter A, Maurer M, Moosburner S, Raschzok N, Kamali C, Pratschke J, Benzing C, Krenzien F. Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis. Front Oncol 2023; 13:1224347. [PMID: 37860189 PMCID: PMC10584147 DOI: 10.3389/fonc.2023.1224347] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
Background For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow. Methods This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM. Results A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02. Conclusion CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.
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Affiliation(s)
- Robert Oehring
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nikitha Ramasetti
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Sharlyn Ng
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Roland Roller
- Speech and Language Technology Lab, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
| | - Philippe Thomas
- Speech and Language Technology Lab, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
| | - Axel Winter
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Max Maurer
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Simon Moosburner
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nathanael Raschzok
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Can Kamali
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Johann Pratschke
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Christian Benzing
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Felix Krenzien
- Department of Surgery, Charité – Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
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11
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Liu Y, Huo X, Li Q, Li Y, Shen G, Wang M, Ren D, Zhao F, Liu Z, Zhao J, Liu X. Watson for oncology decision system for treatment consistency study in breast cancer. Clin Exp Med 2023; 23:1649-1657. [PMID: 36138331 DOI: 10.1007/s10238-022-00896-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/12/2022] [Indexed: 11/30/2022]
Abstract
The Watson for Oncology (WFO) decision system has been rolled out in many cancers. However, the consistency of treatment for breast cancer is still unclear in relatively economically disadvantaged areas. Patients with postoperative adjuvant stage (January 2017 to December 2017) and advanced-stage breast cancer (January 2014 to December 2018) in northwest of China were included in this study. Patient information was imported to make treatment decisions using Watson version 19.20 analysis and subsequently compared with clinician decisions and analyzed for influencing factors. A total of 263 patients with postoperative adjuvant breast cancer and 200 with advanced breast cancer were included in this study. The overall treatment modality for WFO was in 80.2% and 50.5% agreement with clinicians in the adjuvant and advanced-stage population, respectively. In adjuvant treatment after breast cancer surgery, menopausal status (odds ratio (OR) = 2.89, P = 0.012, 95% CI, 1.260-6.630), histological grade (OR = 0.22, P = 0.019, 95% CI, 0.061-0.781) and tumor stage (OR = 0.22, P = 0.042, 95% CI, 0.050-0.943) were independent factors affecting the concordance between the two stages. In the first-line treatment of advanced breast cancer, hormone receptor status was a factor influencing the consistency of treatment (χ2 = 14.728, P < 0.001). There was good agreement between the WFOs and clinicians' treatment decisions in postoperative adjuvant breast cancer, but poor agreement was observed in patients with advanced breast cancer.
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Affiliation(s)
- Yaobang Liu
- Department of Surgical Oncology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, People's Republic of China
| | - Xingfa Huo
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Qi Li
- Department of Oncology, Yinchuan Hospital of Traditional Chinese Medicine, Yinchuan, 750004, People's Republic of China
| | - Yishuang Li
- Department of Clinical Nutrition, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750002, People's Republic of China
| | - Guoshuang Shen
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Miaozhou Wang
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Dengfeng Ren
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Fuxing Zhao
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Zhen Liu
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China
| | - Jiuda Zhao
- Breast Disease Diagnosis and Treatment Center of Affiliated Hospital of Qinghai University, Affiliated Cancer Hospital of Qinghai University, Xining, 810000, People's Republic of China.
| | - Xinlan Liu
- Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, 750004, People's Republic of China.
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12
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Pierre K, Gupta M, Raviprasad A, Sadat Razavi SM, Patel A, Peters K, Hochhegger B, Mancuso A, Forghani R. Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges. Expert Rev Anticancer Ther 2023; 23:1265-1279. [PMID: 38032181 DOI: 10.1080/14737140.2023.2286001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Manas Gupta
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
| | - Abheek Raviprasad
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Seyedeh Mehrsa Sadat Razavi
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Anjali Patel
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Keith Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
- Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL, USA
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13
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Ladbury C, Amini A, Govindarajan A, Mambetsariev I, Raz DJ, Massarelli E, Williams T, Rodin A, Salgia R. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med 2023; 4:100933. [PMID: 36738739 PMCID: PMC9975283 DOI: 10.1016/j.xcrm.2023.100933] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/14/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023]
Abstract
The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA.
| | - Ameish Govindarajan
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Dan J Raz
- Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA
| | - Erminia Massarelli
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Terence Williams
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Andrei Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
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14
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Walraven JEW, Verhoeven RHA, van der Meulen R, van der Hoeven JJM, Lemmens VEPP, Hesselink G, Desar IME. Facilitators and barriers to conducting an efficient, competent and high-quality oncological multidisciplinary team meeting. BMJ Open Qual 2023; 12:bmjoq-2022-002130. [PMID: 36759037 PMCID: PMC9923284 DOI: 10.1136/bmjoq-2022-002130] [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: 09/26/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Optimal oncological care nowadays requires discussing every patient in a multidisciplinary team meeting (MDTM). The number of patients to be discussed is rising rapidly due to the increasing incidence and prevalence of cancer and the emergence of new multidisciplinary treatment options. This puts MDTMs under considerable time pressure. The aim of this study is therefore to identify the facilitators and barriers with regard to performing an efficient, competent and high-quality MDTM. METHODS Semistructured interviews were conducted with Dutch medical specialists and residents participating in oncological MDTMs. Purposive sampling was used to maximise variation in participants' professional and demographic characteristics (eg, sex, medical specialist vs resident, specialty, type and location of affiliated hospital). Interview data were systematically analysed according to the principles of thematic content analysis. RESULTS Sixteen medical specialists and 19 residents were interviewed. All interviewees agreed that attending and preparing MDTMs is time-consuming and indicated the need for optimal execution in order to ensure that MDTMs remain feasible in the near future. Four themes emerged that are relevant to achieving an optimal MDTM: (1) organisational aspects; (2) participants' responsibilities and requirements; (3) competences, behaviour and team dynamics and (4) meeting content. Good organisation, a sound structure and functioning information and communication technology facilitate high-quality MDTMs. Multidisciplinary collaboration and adequate communication are essential competences for participants; a lack thereof and the existence of a hierarchy are hindering factors. CONCLUSION Conducting an efficient, competent and high-quality oncological MDTM is facilitated and hindered by many factors. Being aware of these factors provides opportunities for optimising MDTMs, which are under pressure due to the increase in the number of patients to discuss.
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Affiliation(s)
- Janneke E W Walraven
- Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands .,Department of Research & Development, IKNL, Utrecht, The Netherlands
| | - Rob H A Verhoeven
- Department of Research & Development, IKNL, Utrecht, The Netherlands,Department of Medical Oncology, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | | | - Gijs Hesselink
- Department of Intensive Care, Radboudumc, Nijmegen, The Netherlands,Department of IQ healthcare, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Ingrid M E Desar
- Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands
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15
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Rodríguez Ruiz N, Abd Own S, Ekström Smedby K, Eloranta S, Koch S, Wästerlid T, Krstic A, Boman M. Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach. Front Oncol 2022; 12:984021. [PMID: 36457495 PMCID: PMC9705761 DOI: 10.3389/fonc.2022.984021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/03/2022] [Indexed: 09/10/2024] Open
Abstract
Background The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process. Purpose To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders. Methods Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system. Results The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations. Impact Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients. Conclusion Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
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Affiliation(s)
- Núria Rodríguez Ruiz
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
| | - Sulaf Abd Own
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Laboratory Medicine, Division of Pathology, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Karin Ekström Smedby
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Sandra Eloranta
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Sabine Koch
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
| | - Tove Wästerlid
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Aleksandra Krstic
- Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Boman
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
- School of Electrical Engineering and Computer Science (EECS)/Software and Computer Systems, KTH Royal Institute of Technology, Stockholm, Sweden
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16
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Lyu PF, Wang Y, Meng QX, Fan PM, Ma K, Xiao S, Cao XC, Lin GX, Dong SY. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Front Oncol 2022; 12:955668. [PMID: 36212413 PMCID: PMC9535738 DOI: 10.3389/fonc.2022.955668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
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Affiliation(s)
- Peng-fei Lyu
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yu Wang
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Qing-Xiang Meng
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping-ming Fan
- Department of Breast Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ke Ma
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Sha Xiao
- International School of Public Health and One Health, Heinz Mehlhorn Academician Workstation, Hainan Medical University, Haikou, China
| | - Xun-chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Guang-Xun Lin
- Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
| | - Si-yuan Dong
- Thoracic Department, The First Hospital of China Medical University, Shenyang, China
- *Correspondence: Guang-Xun Lin, ; Si-yuan Dong,
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17
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Brown GTF, Bekker HL, Young AL. Quality and efficacy of Multidisciplinary Team (MDT) quality assessment tools and discussion checklists: a systematic review. BMC Cancer 2022; 22:286. [PMID: 35300636 PMCID: PMC8928609 DOI: 10.1186/s12885-022-09369-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background MDT discussion is the gold standard for cancer care in the UK. With the incidence of cancer on the rise, demand for MDT discussion is increasing. The need for efficiency, whilst maintaining high standards, is therefore clear. Paper-based MDT quality assessment tools and discussion checklists may represent a practical method of monitoring and improving MDT practice. This reviews aims to describe and appraise these tools, as well as consider their value to quality improvement. Methods Medline, EMBASE and PsycInfo were searched using pre-defined terms. The PRISMA model was followed throughout. Studies were included if they described the development of a relevant tool, or if an element of the methodology further informed tool quality assessment. To investigate efficacy, studies using a tool as a method of quality improvement in MDT practice were also included. Study quality was appraised using the COSMIN risk of bias checklist or the Newcastle-Ottawa scale, depending on study type. Results The search returned 7930 results. 18 studies were included. In total 7 tools were identified. Overall, methodological quality in tool development was adequate to very good for assessed aspects of validity and reliability. Clinician feedback was positive. In one study, the introduction of a discussion checklist improved MDT ability to reach a decision from 82.2 to 92.7%. Improvement was also noted in the quality of information presented and the quality of teamwork. Conclusions Several tools for assessment and guidance of MDTs are available. Although limited, current evidence indicates sufficient rigour in their development and their potential for quality improvement. Trial registration PROSPERO ID: CRD42021234326. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09369-8.
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Affiliation(s)
- George T F Brown
- Department of Pancreatic Surgery, St James's University Hospital, Leeds, UK
| | - Hilary L Bekker
- Leeds Unit of Complex Intervention Development, School of Medicine, University of Leeds, Leeds, UK.,Research Centre for Patient Involvement, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Alastair L Young
- Department of Pancreatic Surgery, St James's University Hospital, Leeds, UK.
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18
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Li Y, Chen D, Wu X, Yang W, Chen Y. A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations. J Thorac Dis 2022; 13:7006-7020. [PMID: 35070383 PMCID: PMC8743410 DOI: 10.21037/jtd-21-806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022]
Abstract
Objective To summarize the current evidence regarding the applications, workflow, and limitations of artificial intelligence (AI) in the management of patients pathologically-diagnosed with lung cancer. Background Lung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide. AI technologies have been applied to daily medical workflow and have achieved an excellent performance in predicting histopathologic subtypes, analyzing gene mutation profiles, and assisting in clinical decision-making for lung cancer treatment. More advanced deep learning for classifying pathologic images with minimal human interactions has been developed in addition to the conventional machine learning scheme. Methods Studies were identified by searching databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, up to February 2021 without language restrictions. Conclusions A number of studies have evaluated AI pipelines and confirmed that AI is robust and efficacious in lung cancer diagnosis and decision-making, demonstrating that AI models are a useful tool for assisting oncologists in health management. Although several limitations that pose an obstacle for the widespread use of AI schemes persist, the unceasing refinement of AI techniques is poised to overcome such problems. Thus, AI technology is a promising tool for use in diagnosing and managing lung cancer.
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Affiliation(s)
- Yongzhong Li
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Donglai Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China
| | - Xuejie Wu
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Wentao Yang
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yongbing Chen
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
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19
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Macchia G, Ferrandina G, Patarnello S, Autorino R, Masciocchi C, Pisapia V, Calvani C, Iacomini C, Cesario A, Boldrini L, Gui B, Rufini V, Gambacorta MA, Scambia G, Valentini V. Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept. Front Oncol 2022; 11:797454. [PMID: 35047408 PMCID: PMC8761664 DOI: 10.3389/fonc.2021.797454] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Aim The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. Patients and Method Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed. Results In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. Conclusion This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.
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Affiliation(s)
- Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Ferrandina
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.,Department of Woman, Child and Public Health, Catholic University of the Sacred Heart, Rome, Italy
| | - Stefano Patarnello
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Rosa Autorino
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Carlotta Masciocchi
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Pisapia
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Cristina Calvani
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Chiara Iacomini
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Boldrini
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Benedetta Gui
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vittoria Rufini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Maria Antonietta Gambacorta
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giovanni Scambia
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.,Department of Woman, Child and Public Health, Catholic University of the Sacred Heart, Rome, Italy
| | - Vincenzo Valentini
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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20
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Abstract
PURPOSE OF REVIEW In this article, we focus on the role of artificial intelligence in the management of lung cancer. We summarized commonly used algorithms, current applications and challenges of artificial intelligence in lung cancer. RECENT FINDINGS Feature engineering for tabular data and computer vision for image data are commonly used algorithms in lung cancer research. Furthermore, the use of artificial intelligence in lung cancer has extended to the entire clinical pathway including screening, diagnosis and treatment. Lung cancer screening mainly focuses on two aspects: identifying high-risk populations and the automatic detection of lung nodules. Artificial intelligence diagnosis of lung cancer covers imaging diagnosis, pathological diagnosis and genetic diagnosis. The artificial intelligence clinical decision-support system is the main application of artificial intelligence in lung cancer treatment. Currently, the challenges of artificial intelligence applications in lung cancer mainly focus on the interpretability of artificial intelligence models and limited annotated datasets; and recent advances in explainable machine learning, transfer learning and federated learning might solve these problems. SUMMARY Artificial intelligence shows great potential in many aspects of the management of lung cancer, especially in screening and diagnosis. Future studies on interpretability and privacy are needed for further application of artificial intelligence in lung cancer.
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Affiliation(s)
- Kai Zhang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
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21
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Gebbia V, Guarini A, Piazza D, Bertani A, Spada M, Verderame F, Sergi C, Potenza E, Fazio I, Blasi L, La Sala A, Mortillaro G, Roz E, Marchese R, Chiarenza M, Soto-Parra H, Valerio MR, Agneta G, Amato C, Lipari H, Baldari S, Ferraù F, Di Grazia A, Mancuso G, Rizzo S, Firenze A. Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer. Pulm Ther 2021; 7:295-308. [PMID: 34089169 PMCID: PMC8177259 DOI: 10.1007/s41030-021-00163-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/20/2021] [Indexed: 01/31/2023] Open
Abstract
To date, the virtual multidisciplinary tumor boards (vMTBs) are increasingly used to achieve high-quality treatment recommendations across health-care regions, which expands and develops the local MTB team to a regional or national expert network. This review describes the process of lung cancer-specific MTBs and the transition process from face-to-face tumor boards to virtual ones. The review also focuses on the project organization's description, advantages, and disadvantages. Semi-structured interviews identified five major themes for MTBs: current practice, attitudes, enablers, barriers, and benefits for the MTB. MTB teams exhibited positive responses to modeled data feedback. Virtualization reduces time spent for travel, allowing easier and timely patient discussions. This process requires a secure web platform to assure the respect of patients' privacy and presents the same unanswered problems. The implementation of vMTB also permits the implementation of networks especially in areas with geographical barriers facilitating interaction between large referral cancer centers and tertiary or community hospitals as well as easier access to clinical trial opportunities. Studies aimed to improve preparations, structure, and conduct of MTBs, research methods to monitor their performance, teamwork, and outcomes are also outlined in this article. Analysis of literature shows that MTB participants discuss 5-8 cases per meeting and that the use of a vMTB for lung cancer and in particular stage III NSCLC and complex stage IV cases is widely accepted by most health professionals. Despite still-existing gaps, overall vMTB represents a unique opportunity to optimize patient management in a patient-centered approach.
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Affiliation(s)
- Vittorio Gebbia
- Medical Oncology Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, La Maddalena Clinic for Cancer, University of Palermo, Via San Lorenzo Colli n. 312D, 90100, Palermo, Italy.
- GSTU Foundation, Palermo, Italy.
| | - Aurelia Guarini
- Medical Oncology Unit, Fondazione Ospedale Giglio, Cefalù, Palermo, Italy
| | | | - Alessandro Bertani
- Division of Thoracic Surgery and Lung Transplantation, Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS Ismett, UPMC, Palermo, Italy
| | - Massimiliano Spada
- Medical Oncology Unit, Fondazione Ospedale Giglio, Cefalù, Palermo, Italy
| | | | - Concetta Sergi
- Thoracic Surgery Unit, ARNAS, Ospedale Garibaldi, Nesima, Catania, Italy
| | - Enrico Potenza
- Thoracic Surgery Unit, ARNAS, Ospedale Garibaldi, Nesima, Catania, Italy
| | - Ivan Fazio
- Radiation Therapy Unit, Clinica Macchiarella, Palermo, Italy
| | - Livio Blasi
- Medical Oncology Unit, Arnas Civico, Palermo, Italy
| | - Alba La Sala
- Bronchial Endoscopy Unit, Arnas Civico, Palermo, Italy
| | | | - Elena Roz
- Pathology Unit, La Maddalena Clinic for Cancer, Palermo, Italy
| | - Roberto Marchese
- Thoracic Surgery Unit, La Maddalena Clinic for Cancer, Palermo, Italy
| | | | | | | | - Giuseppe Agneta
- Thoracic Surgery Unit, Ospedale Cervello Villa Sofia, Palermo, Italy
| | - Carmela Amato
- Patients Advocacy "Serena a Palermo", Palermo, Italy
| | - Helga Lipari
- Medical Oncology Unit, Ospedale Cannizzaro, Catania, Italy
| | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University Hospital G. Martino, Messina, Italy
| | - Francesco Ferraù
- Medical Oncology Unit, Ospedale San Vincenzo, Taormina, Messina, Italy
| | - Alfio Di Grazia
- Radiation Oncology Unit, Istituto Clinico Humanitas, Catania, Italy
| | - Gianfranco Mancuso
- Medical Oncology Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, La Maddalena Clinic for Cancer, University of Palermo, Via San Lorenzo Colli n. 312D, 90100, Palermo, Italy
| | - Sergio Rizzo
- Medical Oncology Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, La Maddalena Clinic for Cancer, University of Palermo, Via San Lorenzo Colli n. 312D, 90100, Palermo, Italy
| | - Alberto Firenze
- Risk Management Unit, Policlinico, University of Palermo, Palermo, Italy
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22
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Guirado M, Sanchez-Hernandez A, Pijuan L, Teixido C, Gómez-Caamaño A, Cilleruelo-Ramos Á. Quality indicators and excellence requirements for a multidisciplinary lung cancer tumor board by the Spanish Lung Cancer Group. Clin Transl Oncol 2021; 24:446-459. [PMID: 34665437 PMCID: PMC8525055 DOI: 10.1007/s12094-021-02712-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/18/2021] [Indexed: 12/24/2022]
Abstract
Multidisciplinary care is needed to decide the best therapeutic approach and to provide optimal care to patients with lung cancer (LC). Multidisciplinary teams (MDTs) are optimal strategies for the management of patients with LC and have been associated with better outcomes, such as an increase in quality of life and survival. The Spanish Lung Cancer Group has promoted this review about the current situation of the existing national LC-MDTs, which also offers a set of excellence requirements and quality indicators to achieve the best care in any patient with LC. Time and sufficient resources; leadership; administrative and institutional support; and recording of activity are key factors for the success of LC-MDTs. A set of excellence requirements in terms of staff, resources and organization of the LC-MDT have been proposed. At last, a list of quality indicators has been agreed to achieve and measure the performance of current LC-MDTs.
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Affiliation(s)
- M Guirado
- Medical Oncology Department, Hospital General Universitario de Elche, 03203, Elche, Spain
| | - A Sanchez-Hernandez
- Medical Oncology Department, Consorcio Hospitalario Provincial de Castellón, 12002, Castellón de la Plana, Spain
| | - L Pijuan
- Pathology Department, Bellvitge University Hospital, 08907, L'Hospitalet de Llobregat, Spain
| | - C Teixido
- Thoracic Oncology Unit, Department of Pathology, IDIBAPS, Hospital Clinic of Barcelona, C. de Villarroel, 170, 08036, Barcelona, Spain.
| | - A Gómez-Caamaño
- Department of Radiation Oncology, Hospital Clínico Universitario Santiago de Compostela, 15706, Santiago de Compostela, Spain
| | - Á Cilleruelo-Ramos
- Thoracic Surgery Department, Hospital Clínico Universitario Valladolid, 47005, Valladolid, Spain
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23
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Byun H, Yu S, Oh J, Bae J, Yoon MS, Lee SH, Chung JH, Kim TH. An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases. J Clin Med 2021; 10:jcm10153198. [PMID: 34361982 PMCID: PMC8347824 DOI: 10.3390/jcm10153198] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/16/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.
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Affiliation(s)
- Hayoung Byun
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
| | - Sangjoon Yu
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Jaehoon Oh
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Junwon Bae
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Myeong Seong Yoon
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Seung Hwan Lee
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
| | - Jae Ho Chung
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of HY-KIST Bio-Convergence, College of Medicine, Hanyang University, Seoul 04763, Korea
- Correspondence: (J.H.C.); (T.H.K.)
| | - Tae Hyun Kim
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Correspondence: (J.H.C.); (T.H.K.)
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24
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Winters DA, Soukup T, Sevdalis N, Green JSA, Lamb BW. The cancer multidisciplinary team meeting: in need of change? History, challenges and future perspectives. BJU Int 2021; 128:271-279. [PMID: 34028162 DOI: 10.1111/bju.15495] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Two decades since their inception, multidisciplinary teams (MDTs) are widely regarded as the 'gold standard' of cancer care delivery. Benefits of MDT working include improved patient outcomes, adherence to guidelines, and even economic benefits. Benefits to MDT members have also been demonstrated. An increasing body of evidence supports the use of MDTs and provides guidance on best practise. The system of MDTs in cancer care has come under increasing pressure of late, due to the increasing incidence of cancer, the popularity of MDT working, and financial pressures. This pressure has resulted in recommendations by national bodies to implement streamlining to reduce workload and improve efficiency. In the present review we examine the historical evidence for MDT working, and the scientific developments that dictate best practise. We also explore how streamlining can be safely and effectively undertaken. Finally, we discuss the future of MDT working including the integration of artificial intelligence and decision support systems and propose a new model for improving patient centredness.
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Affiliation(s)
- David A Winters
- Department of Urology, Barts Health NHS Trust, Whipps Cross University Hospital, London, UK
| | - Tayana Soukup
- Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - Nick Sevdalis
- Department of Urology, Barts Health NHS Trust, Whipps Cross University Hospital, London, UK.,Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - James S A Green
- Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - Benjamin W Lamb
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,Faculty of Health, Education, Medicine and Social Care, School of Allied Health, Anglia Ruskin University, Cambridge, UK
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25
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Guo H, Diao L, Zhou X, Chen JN, Zhou Y, Fang Q, He Y, Dziadziuszko R, Zhou C, Hirsch FR. Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse. Transl Lung Cancer Res 2021; 10:2452-2474. [PMID: 34295654 PMCID: PMC8264317 DOI: 10.21037/tlcr-21-96] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/16/2021] [Indexed: 12/11/2022]
Abstract
Background Conventional analysis of single-plex chromogenic immunohistochemistry (IHC) focused on quantitative but spatial analysis. How immune checkpoints localization related to non-small cell lung cancer (NSCLC) prognosis remained unclear. Methods Here, we analyzed ten immune checkpoints on 1,859 tumor microarrays (TMAs) from 121 NSCLC patients and recruited an external cohort of 30 NSCLC patients with 214 whole-slide IHC. EfficientUnet was applied to segment tumor cells (TCs) and tumor-infiltrating lymphocytes (TILs), while ResNet was performed to extract prognostic features from IHC images. Results The features of galectin-9, OX40, OX40L, KIR2D, and KIR3D played an un-negatable contribution to overall survival (OS) and relapse-free survival (RFS) in the internal cohort, validated in public databases (GEPIA, HPA, and STRING). The IC-Score and Res-Score were two predictive models established by EfficientUnet and ResNet. Based on the IC-Score, Res-Score, and clinical features, the integrated score presented the highest AUC for OS and RFS, which could achieve 0.9 and 0.85 in the internal testing cohort. The robustness of Res-Score was validated in the external cohort (AUC: 0.80–0.87 for OS, and 0.83–0.94 for RFS). Additionally, the neutrophil-to-lymphocyte ratio (NLR) combined with the PD-1/PD-L1 signature established by EfficientUnet can be a predictor for RFS in the external cohort. Conclusions Overall, we established a reliable model to risk-stratify relapse and death in NSCLC with a generalization ability, which provided a convenient approach to spatial analysis of single-plex chromogenic IHC.
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Affiliation(s)
- Haoyue Guo
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China.,School of Medicine, Tongji University, Shanghai, China
| | - Li Diao
- Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaofeng Zhou
- School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, China
| | - Jie-Neng Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yue Zhou
- Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiyu Fang
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China
| | - Rafal Dziadziuszko
- Department of Oncology and Radiotherapy, Medical University of Gdansk, ul. M. Sklodowskiej-Curie 3A, Gdańsk 80-210, Województwo pomorskie, Poland
| | - Caicun Zhou
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China
| | - Fred R Hirsch
- Center for Thoracic Oncology, Mount Sinai Cancer, New York, NY, USA
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26
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Lin B, Wu S. Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 26:77-81. [PMID: 33887155 DOI: 10.1089/omi.2021.0037] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Digital transformation is impacting every facet of science and society, not least because there is a growing need for digital services and products with the COVID-19 pandemic. But the need for digital transformation in diagnostics and personalized medicine field cuts deeper. In the past, personalized/precision medicine initiatives have been unable to capture the patients' experiences and clinical outcomes in real-time and in real-world settings. The availability of wearable smart sensors, wireless connectivity, artificial intelligence, and the Internet of Medical Things is changing the personalized/precision medicine research and implementation landscape. Digital transformation in poised to accelerate personalized/precision medicine and systems science in multiple fronts such as deep real-time phenotyping with patient-reported outcomes, high-throughput association studies between omics and highly granular phenotypic variation, digital clinical trials, among others. The present expert review offers an analysis of these systems science frontiers with a view to future applications at the intersection of digital health and personalized medicine, or put in other words, signaling the rise of "digital personalized medicine."
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Affiliation(s)
- Biaoyang Lin
- Zhejiang-California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, Zhejiang, China.,Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Medicine, Zhejiang University, The First Affiliated Hospital, Hangzhou, China.,Department of Urology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Shengjun Wu
- Department of Clinical Laboratories, School of Medicine, Zhejiang University, Sir Run Run Shaw Hospital, Hangzhou, China
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