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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Song JH, Kim ER, Hong Y, Sohn I, Ahn S, Kim SH, Jang KT. Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens. Cancers (Basel) 2024; 16:1900. [PMID: 38791978 DOI: 10.3390/cancers16101900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1-25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758-0.830 in the training set and 0.781-0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.
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Affiliation(s)
- Joo Hye Song
- Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Republic of Korea
| | - Eun Ran Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Yiyu Hong
- Department of R&D Center, Arontier Co., Ltd., Seoul 06735, Republic of Korea
| | - Insuk Sohn
- Department of R&D Center, Arontier Co., Ltd., Seoul 06735, Republic of Korea
| | - Soomin Ahn
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Seok-Hyung Kim
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Kee-Taek Jang
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
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Watanabe J, Ichimasa K, Kataoka Y, Miki A, Someko H, Honda M, Tahara M, Yamashina T, Yeoh KG, Kawai S, Kotani K, Sata N. Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta-analysis. Dig Endosc 2024; 36:533-545. [PMID: 37746764 DOI: 10.1111/den.14691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/20/2023] [Indexed: 09/26/2023]
Abstract
OBJECTIVES Lymphovascular invasion (LVI) is a critical risk factor for lymph node metastasis (LNM), which requires additional surgery after endoscopic resection of T1 colorectal cancer (CRC). However, the impact of additional staining on estimating LNM is unclear. This systematic review aimed to evaluate the impact of additional staining on determining LNM in T1 CRC. METHODS We searched five electronic databases. Outcomes were diagnostic odds ratio (DOR), assessed using hierarchical summary receiver operating characteristic curves, and interobserver agreement among pathologists for positive LVI, assessed using Kappa coefficients (κ). We performed a subgroup analysis of studies that simultaneously included a multivariable analysis for other risk factors (deep submucosal invasion, poor differentiation, and tumor budding). RESULTS Among the 64 studies (18,097 patients) identified, hematoxylin-eosin (HE) and additional staining for LVI had pooled sensitivities of 0.45 (95% confidence interval [CI] 0.32-0.58) and 0.68 (95% CI 0.44-0.86), specificities of 0.88 (95% CI 0.78-0.94) and 0.76 (95% CI 0.62-0.86), and DORs of 6.26 (95% CI 3.73-10.53) and 6.47 (95% CI 3.40-12.32) for determining LNM, respectively. In multivariable analysis, the DOR of additional staining for LNM (DOR 5.95; 95% CI 2.87-12.33) was higher than that of HE staining (DOR 1.89; 95% CI 1.13-3.16) (P = 0.01). Pooled κ values were 0.37 (95% CI 0.22-0.52) and 0.62 (95% CI 0.04-0.99) for HE and additional staining for LVI, respectively. CONCLUSION Additional staining for LVI may increase the DOR for LNM and interobserver agreement for positive LVI among pathologists.
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Affiliation(s)
- Jun Watanabe
- Division of Gastroenterological, General and Transplant Surgery, Department of Surgery, Jichi Medical University, Tochigi, Japan
- Division of Community and Family Medicine, Jichi Medical University, Tochigi, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
- Department of Medicine, National University of Singapore, Singapore City, Singapore
| | - Yuki Kataoka
- Department of Internal Medicine, Kyoto Min-iren Asukai Hospital, Kyoto, Japan
- Section of Clinical Epidemiology, Department of Community Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Medicine/Public Health, Kyoto, Japan
- Scientific Research WorkS Peer Support Group, Osaka, Japan
| | - Atsushi Miki
- Division of Gastroenterological, General and Transplant Surgery, Department of Surgery, Jichi Medical University, Tochigi, Japan
| | - Hidehiro Someko
- Scientific Research WorkS Peer Support Group, Osaka, Japan
- General Internal Medicine, Asahi General Hospital, Chiba, Japan
| | - Munenori Honda
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Makiko Tahara
- Division of Gastroenterological, General and Transplant Surgery, Department of Surgery, Jichi Medical University, Tochigi, Japan
| | - Takeshi Yamashina
- Division of Gastroenterology and Hepatology, Kansai Medical University Medical Center, Osaka, Japan
| | - Khay Guan Yeoh
- Department of Medicine, National University of Singapore, Singapore City, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore City, Singapore
| | - Shigeo Kawai
- Department of Diagnostic Pathology, Tochigi Medical Center Shimotsuga, Tochigi, Japan
| | - Kazuhiko Kotani
- Division of Community and Family Medicine, Jichi Medical University, Tochigi, Japan
| | - Naohiro Sata
- Division of Gastroenterological, General and Transplant Surgery, Department of Surgery, Jichi Medical University, Tochigi, Japan
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Kumar A, Goyal A. Emerging molecules, tools, technology, and future of surgical knife in gastroenterology. World J Gastrointest Surg 2024; 16:988-998. [PMID: 38690056 PMCID: PMC11056674 DOI: 10.4240/wjgs.v16.i4.988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/18/2024] [Accepted: 04/03/2024] [Indexed: 04/22/2024] Open
Abstract
The 21st century has started with several innovations in the medical sciences, with wide applications in health care management. This development has taken in the field of medicines (newer drugs/molecules), various tools and technology which has completely changed the patient management including abdominal surgery. Surgery for abdominal diseases has moved from maximally invasive to minimally invasive (laparoscopic and robotic) surgery. Some of the newer medicines have its impact on need for surgical intervention. This article focuses on the development of these emerging molecules, tools, and technology and their impact on present surgical form and its future effects on the surgical intervention in gastroenterological diseases.
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Affiliation(s)
- Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Anirudh Goyal
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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Thompson N, Morley-Bunker A, McLauchlan J, Glyn T, Eglinton T. Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review. BJS Open 2024; 8:zrae033. [PMID: 38637299 PMCID: PMC11026097 DOI: 10.1093/bjsopen/zrae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Risk evaluation of lymph node metastasis for early-stage (T1 and T2) colorectal cancers is critical for determining therapeutic strategies. Traditional methods of lymph node metastasis prediction have limited accuracy. This systematic review aimed to review the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. METHODS A comprehensive search was performed of papers that evaluated the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. Studies were appraised using the Joanna Briggs Institute tools. The primary outcome was summarizing artificial intelligence models and their accuracy. Secondary outcomes included influential variables and strategies to address challenges. RESULTS Of 3190 screened manuscripts, 11 were included, involving 8648 patients from 1996 to 2023. Due to diverse artificial intelligence models and varied metrics, no data synthesis was performed. Models included random forest algorithms, support vector machine, deep learning, artificial neural network, convolutional neural network and least absolute shrinkage and selection operator regression. Artificial intelligence models' area under the curve values ranged from 0.74 to 0.9993 (slide level) and 0.9476 to 0.9956 (single-node level), outperforming traditional clinical guidelines. CONCLUSION Artificial intelligence models show promise in predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes. PROSPERO REGISTRATION NUMBER CRD42023409094.
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Affiliation(s)
- Nasya Thompson
- Department of Surgery, University of Otago, Christchurch, New Zealand
| | - Arthur Morley-Bunker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Jared McLauchlan
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Tamara Glyn
- Department of Surgery, University of Otago, Christchurch, New Zealand
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Tim Eglinton
- Department of Surgery, University of Otago, Christchurch, New Zealand
- Department of Surgery, Te Whatu Ora – Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
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Noori J, Yeung T, Pham T, Warrier S, Behrenbruch C, Heriot AG. Revolutionizing colorectal surgery with artificial intelligence: not just a pretty robot. ANZ J Surg 2024; 94:295-296. [PMID: 38178570 DOI: 10.1111/ans.18847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Jawed Noori
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Trevor Yeung
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Toan Pham
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Satish Warrier
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Corina Behrenbruch
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, St Vincent's Hospital, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Alexander G Heriot
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, St Vincent's Hospital, Melbourne, Victoria, Australia
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Watanabe J, Ichimasa K, Kataoka Y, Miyahara S, Miki A, Yeoh KG, Kawai S, Martínez de Juan F, Machado I, Kotani K, Sata N. Diagnostic Accuracy of Highest-Grade or Predominant Histological Differentiation of T1 Colorectal Cancer in Predicting Lymph Node Metastasis: A Systematic Review and Meta-Analysis. Clin Transl Gastroenterol 2024; 15:e00673. [PMID: 38165075 PMCID: PMC10962900 DOI: 10.14309/ctg.0000000000000673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024] Open
Abstract
INTRODUCTION Treatment guidelines for colorectal cancer (CRC) suggest 2 classifications for histological differentiation-highest grade and predominant. However, the optimal predictor of lymph node metastasis (LNM) in T1 CRC remains unknown. This systematic review aimed to evaluate the impact of the use of highest-grade or predominant differentiation on LNM determination in T1 CRC. METHODS The study protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42023416971) and was published in OSF ( https://osf.io/TMAUN/ ) on April 13, 2023. We searched 5 electronic databases for studies assessing the diagnostic accuracy of highest-grade or predominant differentiation to determine LNM in T1 CRC. The outcomes were sensitivity and specificity. We simulated 100 cases with T1 CRC, with an LNM incidence of 11.2%, to calculate the differences in false positives and negatives between the highest-grade and predominant differentiations using a bootstrap method. RESULTS In 42 studies involving 41,290 patients, the differentiation classification had a pooled sensitivity of 0.18 (95% confidence interval [CI] 0.13-0.24) and 0.06 (95% CI 0.04-0.09) ( P < 0.0001) and specificity of 0.95 (95% CI 0.93-0.96) and 0.98 (95% CI 0.97-0.99) ( P < 0.0001) for the highest-grade and predominant differentiations, respectively. In the simulation, the differences in false positives and negatives between the highest-grade and predominant differentiations were 3.0% (range 1.6-4.4) and -1.3% (range -2.0 to -0.7), respectively. DISCUSSION Highest-grade differentiation may reduce the risk of misclassifying cases with LNM as negative, whereas predominant differentiation may prevent unnecessary surgeries. Further studies should examine differentiation classification using other predictive factors.
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Affiliation(s)
- Jun Watanabe
- Department of Surgery, Division of Gastroenterological, General and Transplant Surgery, Jichi Medical University, Shimotsuke, Tochigi, Japan
- Division of Community and Family Medicine, Jichi Medical University, Shimotsuke-City, Tochigi, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University, Northern Yokohama Hospital, Tsuzuki-ku, Yokohama, Japan
- Department of Medicine, National University of Singapore, Singapore
| | - Yuki Kataoka
- Department of Internal Medicine, Kyoto Min-iren Asukai Hospital, Sakyo-ku, Kyoto, Japan
- Scientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan
- Section of Clinical Epidemiology, Department of Community Medicine, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Japan
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Medicine/Public Health, Sakyo-ku, Kyoto, Japan
| | - Shoko Miyahara
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Atsushi Miki
- Department of Surgery, Division of Gastroenterological, General and Transplant Surgery, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Khay Guan Yeoh
- Department of Medicine, National University of Singapore, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore
| | - Shigeo Kawai
- Department of Diagnostic Pathology, Tochigi Medical Center Shimotsuga, Tochigi-City, Tochigi, Japan
| | - Fernando Martínez de Juan
- Department of Gastroenterology and Endoscopy Unit, Instituto Valenciano de Oncología, Valencia, Spain
- Endoscopy Unit, Hospital Quiron Salud, Valencia, Spain
- Medicine, Universidad Cardenal Herrrera-CEU, CEU Universities, Valencia, Spain
| | - Isidro Machado
- Pathology Department, Instituto Valenciano de Oncología, Patologika Laboratory Hospital Quiron Salud and Pathology Department University of Valencia, Valencia, Spain
- CIBERONC, Madrid, Spain
| | - Kazuhiko Kotani
- Division of Community and Family Medicine, Jichi Medical University, Shimotsuke-City, Tochigi, Japan
| | - Naohiro Sata
- Department of Surgery, Division of Gastroenterological, General and Transplant Surgery, Jichi Medical University, Shimotsuke, Tochigi, Japan
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Quénéhervé L, Pioche M, Jacques J. Curative criteria for endoscopic treatment of colorectal cancer. Best Pract Res Clin Gastroenterol 2024; 68:101883. [PMID: 38522881 DOI: 10.1016/j.bpg.2024.101883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/17/2024] [Indexed: 03/26/2024]
Abstract
As endoscopic treatment enables en bloc resection of T1 colorectal cancers, the risk of recurrence, often assimilated to the risk of lymph node metastases, must be assessed in order to offer patients an additional treatment if this risk is deemed significant. The curative criteria currently used by most guidelines are depth of invasion <1 mm, well or moderately differentiated tumour, absence of lympho-vascular invasion, absence of significant budding and tumour-free resection margins. However, these factors must be assessed by qualified pathologists, as they are difficult to evaluate. Moreover, the combination of these factors leads to unnecessary surgery in over 80 % of patients whose tumours are classified as high risk. Refinement of current criteria and research into new tumour and immunological markers are needed to better predict the actual risk of our patients.
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Affiliation(s)
| | - Mathieu Pioche
- Department of Endoscopy and Hepatogastroenterology, Pavillon L, Edouard Herriot Hospital, Lyon, France.
| | - Jérémie Jacques
- Department of Endoscopy and Gastroenterology, Centre Hospitalier Universitaire Dupuytren, Limoges, France.
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Dang H, Verhoeven DA, Boonstra JJ, van Leerdam ME. Management after non-curative endoscopic resection of T1 rectal cancer. Best Pract Res Clin Gastroenterol 2024; 68:101895. [PMID: 38522888 DOI: 10.1016/j.bpg.2024.101895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 02/03/2024] [Accepted: 02/15/2024] [Indexed: 03/26/2024]
Abstract
Since the introduction of population-based screening, increasing numbers of T1 rectal cancers are detected and removed by local endoscopic resection. Patients can be cured with endoscopic resection alone, but there is a possibility of residual tumor cells remaining after the initial resection. These can be located intraluminally at the resection site or extraluminally in the form of (lymph node) metastases. To decrease the risk of residual cells progressing towards more advanced disease, additional treatment is usually needed. However, with the currently available risk stratification models, it remains challenging to determine who should and should not be further treated after non-curative endoscopic resection. In this review, the different management strategies for patients with non-curatively treated T1 rectal cancers are discussed, along with the available evidence for each strategy and relevant considerations for clinical decision making. Furthermore, we provide practical guidance on the management and surveillance following non-curative endoscopic resection of T1 rectal cancer.
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Affiliation(s)
- Hao Dang
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Daan A Verhoeven
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jurjen J Boonstra
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique E van Leerdam
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
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Elshaarawy O, Alboraie M, El-Kassas M. Artificial Intelligence in endoscopy: A future poll. Arab J Gastroenterol 2024; 25:13-17. [PMID: 38220477 DOI: 10.1016/j.ajg.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 09/18/2022] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and computer-assisted diagnosis [CADx] for optical biopsy and lesion characterization. The technology behind these systems is based on a computer algorithm that is trained for a specific function. This function could be to recognize or characterize target lesions such as colonic polyps. Moreover, AI systems can offer technical assistance to improve endoscopic performance as scope insertion guidance. Currently, we believe that such technologies still lack legal and regulatory validations as a large sector of doctors and patients have concerns. However, there is no doubt that these technologies will bring significant improvement in the endoscopic management of patients as well as save money and time.
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Affiliation(s)
- Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt; Gastroenterology Department, Royal Liverpool University Hospital, NHS, UK
| | - Mohamed Alboraie
- Department of Internal Medicine, Al-Azhar University, Cairo, Egypt
| | - Mohamed El-Kassas
- Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt.
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Hu J, Sheng Y, Ma J, Tang Y, Liu D, Zhang J, Wei X, Yang Y, Liu Y, Zhang Y, Wang G. Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning. Front Oncol 2024; 13:1231508. [PMID: 38328435 PMCID: PMC10849061 DOI: 10.3389/fonc.2023.1231508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background We attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance. Methods Data from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set. Results Eventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set. Conclusions We constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.
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Affiliation(s)
- Jitao Hu
- Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yuanyuan Sheng
- School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China
| | - Jinlong Ma
- School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China
| | - Yujie Tang
- Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Dong Liu
- Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianqing Zhang
- Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xudong Wei
- Department of General Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yang Yang
- Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yongqiang Zhang
- School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China
| | - Guiying Wang
- Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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12
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Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
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Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
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13
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Piao ZH, Ge R, Lu L. An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer. Front Oncol 2023; 13:1229998. [PMID: 37941556 PMCID: PMC10628635 DOI: 10.3389/fonc.2023.1229998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023] Open
Abstract
Background According to guidelines, a lot of patients with T1 colorectal cancers (CRCs) undergo additional surgery with lymph node dissection after being treated by endoscopic resection (ER) despite the low incidence of lymph node metastasis (LNM). Aim The aim of this study was to develop an artificial intelligence (AI) model to more effectively identify T1 CRCs at risk for LNM and reduce the rate of unnecessary additional surgery. Methods We retrospectively analyzed 651 patients with T1 CRCs. The patient cohort was randomly divided into a training set (546 patients) and a test set (105 patients) (ratio 5:1), and a classification and regression tree (CART) algorithm was trained on the training set to develop a predictive AI model for LNM. The model used 12 clinicopathological factors to predict positivity or negativity for LNM. To compare the performance of the AI model with the conventional guidelines, the test set was evaluated according to the Japanese Society for Cancer of the Colon and Rectum (JSCCR) and National Comprehensive Cancer Network (NCCN) guidelines. Finally, we tested the performance of the AI model using the test set and compared it with the JSCCR and NCCN guidelines. Results The AI model had better predictive performance (AUC=0.960) than the JSCCR (AUC=0.588) and NCCN guidelines (AUC=0.850). The specificity (85.8% vs. 17.5%, p<0.001), balanced accuracy (92.9% vs. 58.7%, p=0.001), and the positive predictive value (36.3% vs. 9.0%, p=0.001) of the AI model were significantly better than those of the JSCCR guidelines and reduced the percentage of the high-risk group for LNM from 83.8% (JSCCR) to 20.9%. The specificity of the AI model was higher than that of the NCCN guidelines (85.8% vs. 82.4%, p=0.557), but there was no significant difference between the two. The sensitivity of the NCCN guidelines was lower than that of our AI model (87.5% vs. 100%, p=0.301), and according to the NCCN guidelines, 1.2% of the 105 test set patients had missed diagnoses. Conclusion The AI model has better performance than conventional guidelines for predicting LNM in T1 CRCs and therefore could significantly reduce unnecessary additional surgery.
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Kuo CY, Kuo LJ, Lin YK. Artificial intelligence based system for predicting permanent stoma after sphincter saving operations. Sci Rep 2023; 13:16039. [PMID: 37749194 PMCID: PMC10519982 DOI: 10.1038/s41598-023-43211-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023] Open
Abstract
Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.
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Affiliation(s)
- Chih-Yu Kuo
- Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan
| | - Li-Jen Kuo
- Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan
| | - Yen-Kuang Lin
- Graduate Institute of Athletics and Coaching Science, National Taiwan Sport University, Taoyuan, Taiwan.
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15
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Alaimo L, Lima HA, Moazzam Z, Endo Y, Yang J, Ruzzenente A, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Cauchy F, Koerkamp BG, Endo I, Kitago M, Pawlik TM. Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:5406-5415. [PMID: 37210452 DOI: 10.1245/s10434-023-13636-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND The high incidence of early recurrence after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a detrimental effect on overall survival (OS). Machine-learning models may improve the accuracy of outcome prediction for malignancies. METHODS Patients who underwent curative-intent hepatectomy for ICC were identified using an international database. Three machine-learning models were trained to predict early recurrence (< 12 months after hepatectomy) using 14 clinicopathologic characteristics. The area under the receiver operating curve (AUC) was used to assess their discrimination ability. RESULTS In this study, 536 patients were randomly assigned to training (n = 376, 70.1%) and testing (n = 160, 29.9%) cohorts. Overall, 270 (50.4%) patients experienced early recurrence (training: n = 150 [50.3%] vs testing: n = 81 [50.6%]), with a median tumor burden score (TBS) of 5.6 (training: 5.8 [interquartile range {IQR}, 4.1-8.1] vs testing: 5.5 [IQR, 3.7-7.9]) and metastatic/undetermined nodes (N1/NX) in the majority of the patients (training: n = 282 [75.0%] vs testing n = 118 [73.8%]). Among the three different machine-learning algorithms, random forest (RF) demonstrated the highest discrimination in the training/testing cohorts (RF [AUC, 0.904/0.779] vs support vector machine [AUC, 0.671/0.746] vs logistic regression [AUC, 0.668/0.745]). The five most influential variables in the final model were TBS, perineural invasion, microvascular invasion, CA 19-9 lower than 200 U/mL, and N1/NX disease. The RF model successfully stratified OS relative to the risk of early recurrence. CONCLUSIONS Machine-learning prediction of early recurrence after ICC resection may inform tailored counseling, treatment, and recommendations. An easy-to-use calculator based on the RF model was developed and made available online.
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Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Henrique A Lima
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Zorays Moazzam
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Yutaka Endo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Jason Yang
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | | | | | | | - Matthew Weiss
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Todd W Bauer
- Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | | | - Carlo Pulitano
- Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France
| | - Bas Groot Koerkamp
- Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
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16
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Akinrinmade AO, Adebile TM, Ezuma-Ebong C, Bolaji K, Ajufo A, Adigun AO, Mohammad M, Dike JC, Okobi OE. Artificial Intelligence in Healthcare: Perception and Reality. Cureus 2023; 15:e45594. [PMID: 37868407 PMCID: PMC10587915 DOI: 10.7759/cureus.45594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.
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Affiliation(s)
- Abidemi O Akinrinmade
- Medicine and Surgery, Benjamin S. Carson School of Medicine, Babcock University, Ilishan-Remo, NGA
| | - Temitayo M Adebile
- Public Health, Georgia Southern University, Statesboro, USA
- Nephrology, Boston Medical Center, Malden, USA
| | | | | | | | - Aisha O Adigun
- Infectious Diseases, University of Louisville, Louisville, USA
| | - Majed Mohammad
- Geriatrics, Mount Carmel Grove City Hospital, Grove City, USA
| | - Juliet C Dike
- Internal Medicine, University of Calabar, Calabar, NGA
| | - Okelue E Okobi
- Family Medicine, Larkin Community Hospital Palm Springs Campus, Miami, USA
- Family Medicine, Medficient Health Systems, Laurel, USA
- Family Medicine, Lakeside Medical Center, Belle Glade, USA
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17
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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van Bokhorst QNE, Houwen BBSL, Hazewinkel Y, Fockens P, Dekker E. Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status. Endosc Int Open 2023; 11:E752-E767. [PMID: 37593158 PMCID: PMC10431975 DOI: 10.1055/a-2098-1999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/08/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Querijn N E van Bokhorst
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Tergooi Medical Center, Hilversum, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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Sharma A, Kumar R, Yadav G, Garg P. Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Lett 2023; 565:216238. [PMID: 37211068 DOI: 10.1016/j.canlet.2023.216238] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India; Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India.
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Santos-Antunes J, Pioche M, Ramos-Zabala F, Cecinato P, Gallego Rojo FJ, Barreiro P, Félix C, Sferrazza S, Berr F, Wagner A, Lemmers A, Figueiredo Ferreira M, Albéniz E, Uchima H, Küttner-Magalhães R, Fernandes C, Morais R, Gupta S, Martinho-Dias D, Rios E, Faria-Ramos I, Marques M, Bourke MJ, Macedo G. Risk of residual neoplasia after a noncurative colorectal endoscopic submucosal dissection for malignant lesions: a multinational study. Endoscopy 2023; 55:235-244. [PMID: 35863354 DOI: 10.1055/a-1906-8000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
BACKGROUND : Endoscopic submucosal dissection (ESD) in colorectal lesions is technically demanding and a significant rate of noncurative procedures is expected. We aimed to assess the rate of residual lesions after a noncurative ESD for colorectal cancer (CRC) and to establish predictive scores to be applied in the clinical setting. METHODS : Retrospective multicenter analysis of consecutive colorectal ESDs. Patients with noncurative ESDs performed for the treatment of CRC lesions submitted to complementary surgery or with at least one follow-up endoscopy were included. RESULTS : From 2255 colorectal ESDs, 381 (17 %) were noncurative, and 135 of these were performed in CRC lesions. A residual lesion was observed in 24 patients (18 %). Surgery was performed in 96 patients and 76 (79 %) had no residual lesion in the colorectal wall or in the lymph nodes. The residual lesion rate for sm1 cancers was 0 %, and for > sm1 cancers was also 0 % if no other risk factors were present. Independent risk factors for lymph node metastasis were poor differentiation and lymphatic permeation (NC-Lymph score). Risk factors for the presence of a residual lesion in the wall were piecemeal resection, poor differentiation, and positive/indeterminate vertical margin (NC-Wall score). CONCLUSIONS : Lymphatic permeation or poor differentiation warrant surgery owing to their high risk of lymph node metastasis, mainly in > sm1 cancers. In the remaining cases, en bloc and R0 resections resulted in a low risk of residual lesions in the wall. Our scores can be a useful tool for the management of patients who undergo noncurative colorectal ESDs.
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Affiliation(s)
- João Santos-Antunes
- Gastroenterology Department, Faculty of Medicine, Centro Hospitalar Universitário S. João, Porto, Portugal
- Ipatimup/i3S (Instituto de Investigação e Inovação em Saúde da Universidade do Porto), Porto, Portugal
| | - Mathieu Pioche
- Department of Hepatology and Gastroenterology, Edouard Herriot Hospital, Lyon, France
| | - Felipe Ramos-Zabala
- Department of Gastroenterology, Department of Clinical Medical Sciences, Hospital Universitario HM Montepríncipe, HM Hospitales, Universidad San Pablo-CEU, CEU Universities Madrid, Madrid, Spain
| | - Paolo Cecinato
- Gastroenterology and Digestive Endoscopy Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Pedro Barreiro
- Gastroenterology Department, Centro Hospitalar Lisboa Ocidental EPE, Lisbon, Portugal
- Lisbon Advanced Endoscopic Center, Hospital Lusíadas, Lisbon, Portugal
| | - Catarina Félix
- Gastroenterology Department, Centro Hospitalar Lisboa Ocidental EPE, Lisbon, Portugal
| | - Sandro Sferrazza
- Gastroenterology and Endoscopy Unit, Santa Chiara Hospital, Trento, Italy
| | - Frieder Berr
- Department of Internal Medicine I, University Clinics Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Andrej Wagner
- Department of Internal Medicine I, University Clinics Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Arnaud Lemmers
- Department of Gastroenterology, Hepatopancreatology and Digestive Oncology, CUB Erasme Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Mariana Figueiredo Ferreira
- Department of Gastroenterology, Hepatopancreatology and Digestive Oncology, CUB Erasme Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Eduardo Albéniz
- Navarrabiomed Research Institute, Complejo Hospitalario de Navarra, Public University of Navarra, IdiSNA, Pamplona, Spain
| | - Hugo Uchima
- Digestive Endoscopy Service, Centro Médico Teknon, Barcelona, Spain
- Gastroenterology Service, Hospital Universitario Germans Trias i Pujol, Barcelona, Spain
| | - Ricardo Küttner-Magalhães
- Gastroenterology Department, Hospital Santo António, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Carlos Fernandes
- Gastroenterology Department, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal
| | - Rui Morais
- Gastroenterology Department, Faculty of Medicine, Centro Hospitalar Universitário S. João, Porto, Portugal
| | - Sunil Gupta
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, Australia
| | - Daniel Martinho-Dias
- Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Elisabete Rios
- Pathology Department, Faculty of Medicine, Centro Hospitalar Universitário S. João, Porto, Portugal
| | - Isabel Faria-Ramos
- Ipatimup/i3S (Instituto de Investigação e Inovação em Saúde da Universidade do Porto), Porto, Portugal
| | - Margarida Marques
- Gastroenterology Department, Faculty of Medicine, Centro Hospitalar Universitário S. João, Porto, Portugal
| | - Michael J Bourke
- Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney, Australia
| | - Guilherme Macedo
- Gastroenterology Department, Faculty of Medicine, Centro Hospitalar Universitário S. João, Porto, Portugal
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22
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Huang P, Feng Z, Shu X, Wu A, Wang Z, Hu T, Cao Y, Tu Y, Li Z. A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022). Front Oncol 2023; 13:1077539. [PMID: 36824138 PMCID: PMC9941644 DOI: 10.3389/fonc.2023.1077539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/27/2023] [Indexed: 02/10/2023] Open
Abstract
Background Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis. Objective This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions. Method We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results. Result This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination. Conclusion AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.
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Affiliation(s)
- Pan Huang
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xufeng Shu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ahao Wu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhonghao Wang
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tengcheng Hu
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Cao
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Yi Tu, ; Zhengrong Li,
| | - Zhengrong Li
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China,Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Yi Tu, ; Zhengrong Li,
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23
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Shao S, Zhao Y, Lu Q, Liu L, Mu L, Qin J. Artificial intelligence assists surgeons' decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection. Eur J Surg Oncol 2023; 49:433-439. [PMID: 36244844 DOI: 10.1016/j.ejso.2022.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/27/2022] [Accepted: 09/28/2022] [Indexed: 10/07/2022]
Abstract
BACKGROUND Due to the difficult evaluation of the risk of anastomotic leakage (AL) after rectal cancer resection, the decision to perform a temporary ileostomy is not easily distinguishable. The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assist surgeons in the selective implementation of a temporary ileostomy. MATERIALS AND METHODS The data from 2240 patients with rectal cancer who received anterior resection were collected, and these patients were divided into one training and two test cohorts. Five AI algorithms, such as support vector machine (SVM), logistic regression (LR), Naive Bayes (NB), stochastic gradient descent (SGD) and random forest (RF) were employed to develop predictive models using clinical variables and were assessed using the two test cohorts. RESULTS The SVM model indicated good discernment of AL, and might have increased the implementation of temporary ileostomy in patients with AL in the training cohort (p < 0.001). Following the assessment of the two test cohorts, the SVM model could identify AL in a favorable manner, which performed with positive predictive values of 0.150 (0.091-0.234) and 0.151 (0.091-0.237), and negative predictive values of 0.977 (0.958-0.988) and 0.986 (0.969-0.994), respectively. It is important to note that the implementation of temporary ileostomy in patients without AL would have been significantly reduced (p < 0.001) and which would have been significantly increased in patients with AL (p < 0.05). CONCLUSION The model (https://alrisk.21cloudbox.com/) indicated good discernment of AL, which may be used to assist the surgeon's decision-making of performing temporary ileostomy.
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Affiliation(s)
- Shengli Shao
- Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Yufeng Zhao
- Department of Vascular Surgery, First Hospital of Lanzhou University, 730030, Lanzhou, China
| | - Qiyi Lu
- Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Lu Liu
- Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Lei Mu
- Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Jichao Qin
- Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China.
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24
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Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol 2023; 13:1065402. [PMID: 36761957 PMCID: PMC9905815 DOI: 10.3389/fonc.2023.1065402] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA, United States
| | | | - Tarig Elhakim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States,*Correspondence: Dania Daye,
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25
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Hassanzadeh P, Atyabi F, Dinarvand R. Technical and engineering considerations for designing therapeutics and delivery systems. J Control Release 2023; 353:411-422. [PMID: 36470331 DOI: 10.1016/j.jconrel.2022.11.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
The newly-emerged pathological conditions and increased rates of drug resistance necessitate application of the state-of-the-art technologies for accelerated discovery of the therapeutic candidates and obtaining comprehensive knowledge about their targets, action mechanisms, and interactions within the body including those between the receptors and drugs. Using the physics- and chemistry-based modern techniques for theranostic purposes, preparing smart carriers, local delivery of genes or drugs, and enhancing pharmaceutical bioavailability could be of great value against the hard-to-treat diseases and growing drug resistance. Besides the artificial intelligence- and quantum-based techniques, crystal engineering capable of designing new molecules with appropriate characteristics, improving the stability and bioavailability of poorly soluble drugs, and efficient carrier development could play a crucial role in manufacturing efficient pharmaceuticals and reducing the adverse events. In this context, identifying the structures and behaviors of crystals and predicting their characteristics are of great value. Electron diffraction by accelerated analysis of the chemicals and sensitivity to charge alterations, electromechanical tools for controlled delivery of therapeutics, mechatronics via fabrication of multi-functional smart products including the organ-on-chip devices for healthcare applications, and optomechatronics by overcoming the limitations of conventional biomedical techniques could address the unmet biomedical requirements and facilitate development of more effective theranostics with improved outcomes.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran; Sasan Hospital, Tehran 14159-83391, Iran.
| | - Fatemeh Atyabi
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran
| | - Rassoul Dinarvand
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran
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Muacevic A, Adler JR. Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer. Cureus 2022; 14:e31008. [PMID: 36475188 PMCID: PMC9717523 DOI: 10.7759/cureus.31008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/02/2022] [Indexed: 01/25/2023] Open
Abstract
Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.
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Ortiz Zúñiga O, Fernández Esparrach MG, Daca M, Pellisé M. Artificial intelligence in gastrointestinal endoscopy - Evolution to a new era. Rev Esp Enferm Dig 2022; 114:605-615. [PMID: 35770604 DOI: 10.17235/reed.2022.8961/2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) systems based on machine learning have evolved in the last few years with an increasing applicability in gastrointestinal endoscopy. Thanks to AI, an image (input) can be transformed into a clinical decision (output). Although AI systems have been initially studied to improve detection (CADe) and characterization of colorectal lesions (CADx), other indications are being currently investigated as detection of blind spots, scope guidance, or delineation/measurement of lesions. The objective of these review is to summarize the current evidence on applicability of AI systems in gastrointestinal endoscopy, highlight strengths and limitations of the technology and review regulatory and ethical aspects for its general implementation in gastrointestinal endoscopy.
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Affiliation(s)
| | | | - María Daca
- Gastroenterología, Hospital Clínic Barcelona, España
| | - María Pellisé
- Gastroenterología, Hospital Clínic Barcelona, España
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28
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Chen L, Yang F, Qi Z, Tai J. Predicting lymph node metastasis and recurrence in patients with early stage colorectal cancer. Front Med (Lausanne) 2022; 9:991785. [PMID: 36186777 PMCID: PMC9520336 DOI: 10.3389/fmed.2022.991785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Tumor budding (TB), a powerful, independent predictor of colorectal cancer (CRC), is important for making appropriate treatment decisions. Currently, TB is assessed only using the tumor bud count (TBC). In this study, we aimed to develop a novel prediction model, which includes different TB features, for lymph node metastasis (LNM) and local recurrence in patients with pT1 CRC. Enrolled patients (n = 354) were stratified into training and validation cohorts. Independent predictors of LNM and recurrence were identified to generate predictive nomograms that were assessed using the area under the receiver operating characteristic (AUROC) and decision curve analysis (DCA). Seven LNM predictors [gross type, histological grade, lymphovascular invasion (LVI), stroma type, TBC, TB mitosis, and TB CDX2 expression] were identified in the training cohort. LNM, histology grade, LVI, TBC, stroma type, and TB mitosis were independent predictors of recurrence. We constructed an LNM predictive nomogram with a high clinical application value using the DCA. Additionally, a nomogram predicting recurrence-free survival (RFS) was constructed. It presented an AUROC value of 0.944 for the training cohort. These models may assist surgeons in making treatment decisions. In the high-risk group, radical surgery with a postoperative adjuvant chemotherapy was associated with RFS. Postoperative chemotherapy can be better for high-risk patients with pT1 CRC. We showed that TB features besides TBC play important roles in CRC pathogenesis, and our study provides prognostic information to guide the clinical management of patients with early stage CRC.
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Affiliation(s)
- Lei Chen
- Colorectal and Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Funing Yang
- Pediatric Outpatient Clinic, First Hospital of Jilin University, Changchun, Jilin, China
| | - Zhaoyan Qi
- Colorectal and Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Jiandong Tai
- Colorectal and Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
- *Correspondence: Jiandong Tai,
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29
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Song JH, Hong Y, Kim ER, Kim SH, Sohn I. Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer. J Gastroenterol 2022; 57:654-666. [PMID: 35802259 DOI: 10.1007/s00535-022-01894-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/09/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND When endoscopically resected specimens of early colorectal cancer (CRC) show high-risk features, surgery should be performed based on current guidelines because of the high-risk of lymph node metastasis (LNM). The aim of this study was to determine the utility of an artificial intelligence (AI) with deep learning (DL) of hematoxylin and eosin (H&E)-stained endoscopic resection specimens without manual-pixel-level annotation for predicting LNM in T1 CRC. In addition, we assessed AI performance for patients with only submucosal (SM) invasion depth of 1000 to 2000 μm known to be difficult to predict LNM in clinical practice. METHODS H&E-stained whole slide images (WSIs) were scanned for endoscopic resection specimens of 400 patients who underwent endoscopic treatment for newly diagnosed T1 CRC with additional surgery. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of AI for predicting LNM with a fivefold cross-validation in the training set and in a held-out test set. RESULTS We developed an AI model using a two-step attention-based DL approach without clinical features (AUC, 0.764). Incorporating clinical features into the model did not improve its prediction accuracy for LNM. Our model reduced unnecessary additional surgery by 15.1% more than using the current guidelines (67.4% vs. 82.5%). In patients with SM invasion depth of 1000 to 2000 μm, the AI avoided 16.1% of unnecessary additional surgery than using the JSCCR guidelines. CONCLUSIONS Our study is the first to show that AI trained with DL of H&E-stained WSIs has the potential to predict LNM in T1 CRC using only endoscopically resected specimens with conventional histologic risk factors.
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Affiliation(s)
- Joo Hye Song
- Department of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yiyu Hong
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
| | - Eun Ran Kim
- Department of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Seok-Hyung Kim
- Department of Pathology, Samsung Medical Center, Seoul, Republic of Korea
| | - Insuk Sohn
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
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Quero G, Mascagni P, Kolbinger FR, Fiorillo C, De Sio D, Longo F, Schena CA, Laterza V, Rosa F, Menghi R, Papa V, Tondolo V, Cina C, Distler M, Weitz J, Speidel S, Padoy N, Alfieri S. Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives. Cancers (Basel) 2022; 14:cancers14153803. [PMID: 35954466 PMCID: PMC9367568 DOI: 10.3390/cancers14153803] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.
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Affiliation(s)
- Giuseppe Quero
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Pietro Mascagni
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
- Institute of Image-Guided Surgery, IHU-Strasbourg, 67000 Strasbourg, France
| | - Fiona R. Kolbinger
- Department for Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Claudio Fiorillo
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Correspondence: ; Tel.: +39-333-8747996
| | - Davide De Sio
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Fabio Longo
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Carlo Alberto Schena
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Vito Laterza
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Fausto Rosa
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Roberta Menghi
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Valerio Papa
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Vincenzo Tondolo
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Caterina Cina
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Marius Distler
- Department for Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Juergen Weitz
- Department for Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Stefanie Speidel
- National Center for Tumor Diseases (NCT), Partner Site Dresden, 01307 Dresden, Germany
| | - Nicolas Padoy
- Institute of Image-Guided Surgery, IHU-Strasbourg, 67000 Strasbourg, France
- ICube, Centre National de la Recherche Scientifique (CNRS), University of Strasbourg, 67000 Strasbourg, France
| | - Sergio Alfieri
- Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
- Faculty of Medicine, Università Cattolica del Sacro Cuore di Roma, Largo Francesco Vito 1, 00168 Rome, Italy
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Koyama Y, Yamada M, Makiguchi ME, Sekiguchi M, Takamaru H, Sakamoto T, Kono S, Fukuzawa M, Sylvia Wu SY, Sugumaran A, Kawai T, Matsuda T, Itoi T, Saito Y. New scoring system to distinguish deep invasive submucosal and muscularis propria colorectal cancer during colonoscopy: a development and global multicenter external validation study (e-T2 Score). Gastrointest Endosc 2022; 96:321-329.e2. [PMID: 35271864 DOI: 10.1016/j.gie.2022.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/01/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Diagnostics to differentiate deep submucosal invasive (invasion depth ≥1000 μm [T1b]) colorectal cancer (CRC) from muscularis propria invasive (T2) CRC are limited. We aimed to establish and validate a scoring system that differentiates T1b from T2. METHODS A multicenter retrospective cross-validation study was performed. Four hundred sixty-one consecutive pathologically confirmed T1b or T2 CRCs were divided into the development (T1b, 222; T2, 189) and internal validation (T1b, 31; T2, 19) cohorts. Eight potential endoscopic findings were evaluated using the development cohort: loss of lobulation, deep depression, demarcated depressed area, protuberance within the depression, expanding appearance, fold convergency, erosion or white plaque, and Borrmann type 2 or 3 tumor. A scoring system that differentiates T1b from T2 was developed, and diagnostic performance was tested using the internal validation cohort by 8 endoscopists. External validation was conducted using 50 CRC images by 4 endoscopists from other institutions, including outside of Japan. RESULTS Multivariate analysis identified the following 5 independent predictive endoscopic findings of T2 CRC: deep depression (odds ratio [OR], 2.08; 95% confidence interval [CI], 1.07-4.04), demarcated depressed area (OR, 4.40; 95% CI, 1.39-13.9), 4-fold convergency or more (OR, 3.41; 95% CI, 1.90-6.11), erosion or white plaque (OR, 8.28; 95% CI, 2.77-24.7), and Borrmann type 2 or 3 tumor (OR, 8.76; 95% CI, 3.58-21.5). The area under the receiver-operating characteristic curve (AUROC) was .90 (95% CI, .87-.93) in the development cohort, .80 (95% CI, .76-.85) in the internal validation, and .76 (95% CI, .69-.83) in the external validation. CONCLUSIONS We established and validated a new scoring system to differentiate T1b from T2 CRC using 5 simple endoscopic findings.
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Affiliation(s)
- Yohei Koyama
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan; Department of Gastroenterology and Hepatology, Tokyo Medical University Hospital, Tokyo, Japan
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan; Department of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan
| | | | - Masau Sekiguchi
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Taku Sakamoto
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Shin Kono
- Department of Gastroenterology and Hepatology, Tokyo Medical University Hospital, Tokyo, Japan
| | - Masakatsu Fukuzawa
- Department of Gastroenterology and Hepatology, Tokyo Medical University Hospital, Tokyo, Japan
| | | | | | - Takashi Kawai
- Department of Endoscopy, Tokyo Medical University Hospital, Tokyo, Japan
| | - Takahisa Matsuda
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Takao Itoi
- Department of Gastroenterology and Hepatology, Tokyo Medical University Hospital, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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Hong SW, Byeon JS. Endoscopic diagnosis and treatment of early colorectal cancer. Intest Res 2022; 20:281-290. [PMID: 35916019 PMCID: PMC9344247 DOI: 10.5217/ir.2021.00169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/23/2022] [Indexed: 11/15/2022] Open
Abstract
Early colorectal cancer refers to cancer in the colorectum that is confined to the mucosa or submucosa and does not invade the muscularis propria, irrespective of lymph node or distant metastasis. As the number of persons undergoing screening colonoscopy increases, the proportion of patients diagnosed with precancerous colorectal lesions and early colorectal cancer also increases. In the last decade, innovative optical technologies for endoscopic diagnosis have been introduced and endoscopic treatment techniques such as endoscopic submucosal dissection have provided major breakthroughs in the management of early colorectal cancer. With these remarkable developments, endoscopic treatment has established itself as an alternative to surgical resection in the treatment of early colorectal cancer. This review will discuss the endoscopic diagnosis and treatment of early colorectal cancer. Furthermore, the unmet needs in this field and the latest research addressing those issues will be summarized.
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Affiliation(s)
- Seung Wook Hong
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Ianole V, Danciu M, Volovat C, Stefanescu C, Herghelegiu PC, Leon F, Iftene A, Cusmuliuc CG, Toma B, Drug V, Ciobanu Apostol DG. Is High Expression of Claudin-7 in Advanced Colorectal Carcinoma Associated with a Poor Survival Rate? A Comparative Statistical and Artificial Intelligence Study. Cancers (Basel) 2022; 14:2915. [PMID: 35740581 DOI: 10.3390/cancers14122915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/02/2022] [Accepted: 06/10/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs by drawing a parallel between statistical and AI algorithms. Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs and correlated with clinicopathological parameters and survival using statistical and AI algorithms. The Kaplan–Meier univariate survival analysis showed that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs. On the contrary, AI models could not predict the same outcome, probably because of the small number of patients included in our cohort. Abstract Aim: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs, by drawing a parallel between statistical and AI algorithms. Methods: Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs from 84 patients and correlated with clinicopathological parameters and survival. The results were compared with those obtained by using various AI algorithms. Results: the Kaplan–Meier univariate survival analysis showed a significant correlation between survival and Claudin-7 intensity in the invasive front (p = 0.00), a higher expression being associated with a worse prognosis, while Claudin-7 intensity in the tumor core had no impact on survival. In contrast, AI models could not predict the same outcome on survival. Conclusion: The study showed through statistical means that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs, contrary to AI models which could not predict the same outcome, probably because of the small number of patients included in our cohort.
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Taha-Mehlitz S, Däster S, Bach L, Ochs V, von Flüe M, Steinemann D, Taha A. Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review. J Clin Med 2022; 11:2431. [PMID: 35566555 DOI: 10.3390/jcm11092431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/12/2022] [Accepted: 03/29/2022] [Indexed: 12/09/2022] Open
Abstract
Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on “ML in colorectal surgery”, with its viable applications. Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched. Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients’ perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients’ end. Confidence can only be developed within society if more research with precise results is carried out.
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Johnson H, El-Schich Z, Ali A, Zhang X, Simoulis A, Wingren AG, Persson JL. Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients. Cancers (Basel) 2022; 14:cancers14082045. [PMID: 35454952 PMCID: PMC9030299 DOI: 10.3390/cancers14082045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.
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Affiliation(s)
| | - Zahra El-Schich
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Amjad Ali
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
| | - Xuhui Zhang
- Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing 100005, China;
| | - Athanasios Simoulis
- Department of Clinical Pathology and Cytology, Skåne University Hospital, SE-205 02 Malmö, Sweden;
| | - Anette Gjörloff Wingren
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Jenny L. Persson
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
- Correspondence: ; Tel.: +46-0706391199
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Liu Z, Huang C, Tian H, Liu Y, Huang Y, Zhu Z. Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer. Front Surg 2022; 9:845666. [PMID: 35388361 PMCID: PMC8977409 DOI: 10.3389/fsurg.2022.845666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/16/2022] [Indexed: 12/03/2022] Open
Abstract
Background Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer. Methods The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model. Results The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard. Conclusion This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.
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Abstract
STUDY DESIGN This was a systematic review of existing literature. OBJECTIVE The objective of this study was to evaluate the current state-of-the-art trends and utilization of machine learning in the field of spine surgery. SUMMARY OF BACKGROUND DATA The past decade has seen a rise in the clinical use of machine learning in many fields including diagnostic radiology and oncology. While studies have been performed that specifically pertain to spinal surgery, there have been relatively few aggregate reviews of the existing scientific literature as applied to clinical spine surgery. METHODS This study utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2009 to 2019 with syntax specific for machine learning and spine surgery applications. Specific data was extracted from the available literature including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. RESULTS A total of 44 studies met inclusion criteria, of which the majority were level III evidence. Studies were grouped into 4 general types: diagnostic tools, clinical outcome prediction, surgical assessment tools, and decision support tools. Across studies, a wide swath of algorithms were used, which were trained across multiple disparate databases. There were no studies identified that assessed the ethical implementation or patient perceptions of machine learning in clinical care. CONCLUSIONS The results reveal the broad range of clinical applications and methods used to create machine learning algorithms for use in the field of spine surgery. Notable disparities exist in algorithm choice, database characteristics, and training methods. Ongoing research is needed to make machine learning operational on a large scale.
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Affiliation(s)
- Edward M DelSole
- Department of Orthopaedic Surgery, Division of Spine Surgery, Geisinger Musculoskeletal Institute
| | - Wyatt L Keck
- Geisinger Commonwealth School of Medicine, Scranton
| | - Aalpen A Patel
- Department of Radiology (Geisinger), Steele Institute for Health Innovation and Geisinger, Danville, PA
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Ichimasa K, Kudo SE, Kouyama Y, Mochizuki K, Takashina Y, Misawa M, Mori Y, Hayashi T, Wakamura K, Miyachi H. Tumor Location as a Prognostic Factor in T1 Colorectal Cancer. J Anus Rectum Colon 2022; 6:9-15. [PMID: 35128132 PMCID: PMC8801246 DOI: 10.23922/jarc.2021-029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/23/2021] [Indexed: 11/30/2022] Open
Abstract
The incidence of T1 colorectal cancer is expected to increase because of the prevalence of colorectal cancer screening and the progress of endoscopic treatment such as endoscopic submucosal dissection or endoscopic full-thickness resection. Currently, the requirement for additional surgery after endoscopic resection of T1 colorectal cancer is determined according to several treatment guidelines (in USA, Europe, and Japan) referring to the following pathological findings: lymphovascular invasion, tumor differentiation, depth of invasion, and tumor budding, all of which are reported to be risk factors for lymph node metastasis. In addition to these factors, in this review, we investigate whether tumor location, which is an objective factor, has an impact on the presence of lymph node metastasis and recurrence. From recent studies, left-sided location, especially the sigmoid colon in addition to rectum, could be a risk factor for lymph node metastasis and cancer recurrence. The treatment of T1 colorectal cancer should be managed considering these findings.
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Affiliation(s)
- Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuta Kouyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuki Takashina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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Solitano V, Zilli A, Franchellucci G, Allocca M, Fiorino G, Furfaro F, D’amico F, Danese S, Al Awadhi S. Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future. J Clin Med 2022; 11:569. [PMID: 35160021 PMCID: PMC8836846 DOI: 10.3390/jcm11030569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.
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Han T, Zhu J, Chen X, Chen R, Jiang Y, Wang S, Xu D, Shen G, Zheng J, Xu C. Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer. Cancer Cell Int 2022; 22:28. [PMID: 35033083 PMCID: PMC8761313 DOI: 10.1186/s12935-021-02424-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 12/23/2021] [Indexed: 12/11/2022] Open
Abstract
Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02424-7.
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Affiliation(s)
- Tenghui Han
- Xijing Hospital, Airforce Medical University, Xi'an, China
| | - Jun Zhu
- State Key Laboratory of Cancer Biology, Institute of Digestive Diseases, Xijing Hospital, Airforce Medical University, Xi'an, China.,Department of General Surgery, The Southern Theater Air Force Hospital, Guangzhou, China
| | - Xiaoping Chen
- Department of General Surgery, The Southern Theater Air Force Hospital, Guangzhou, China
| | - Rujie Chen
- State Key Laboratory of Cancer Biology, Institute of Digestive Diseases, Xijing Hospital, Airforce Medical University, Xi'an, China
| | - Yu Jiang
- State Key Laboratory of Cancer Biology, Institute of Digestive Diseases, Xijing Hospital, Airforce Medical University, Xi'an, China
| | - Shuai Wang
- Ming Gang Station Hospital, Xi'an Institute of Flight of the Air Force, Minggang, China
| | - Dong Xu
- School of Clinical Medicine, Xi'an Medical University, Xi'an, China
| | - Gang Shen
- Ming Gang Station Hospital, Xi'an Institute of Flight of the Air Force, Minggang, China
| | - Jianyong Zheng
- Division of Digestive Surgery, Xijing Hospital of Digestive Diseases, Airforce Medical University, Xi'an, China.
| | - Chunsheng Xu
- Division of Digestive Surgery, Xijing Hospital of Digestive Diseases, Airforce Medical University, Xi'an, China.
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Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
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Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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Abstract
Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People’ Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 People’s Republic of China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030 People’s Republic of China
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Jin J, Zhou H, Sun S, Tian Z, Ren H, Feng J. Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis. Cancer Manag Res 2021; 13:8967-8977. [PMID: 34880677 PMCID: PMC8645952 DOI: 10.2147/cmar.s337516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment. Patients and Methods We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models. Results Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability. Conclusion Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.
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Affiliation(s)
- Jinlian Jin
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Haiyan Zhou
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Shulin Sun
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Zhe Tian
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Haibing Ren
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Jinwu Feng
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
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44
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Jang SI, Kim YJ, Kim EJ, Kang H, Shon SJ, Seol YJ, Lee DK, Kim KG, Cho JH. Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions. J Gastroenterol Hepatol 2021; 36:3548-3555. [PMID: 34431545 DOI: 10.1111/jgh.15673] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/14/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIM Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under development. We evaluated the diagnostic performance of AI in differentiating polypoid lesions using EUS images. METHODS The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing using an AI development cohort of 1039 EUS images (836 GB polyps and 203 gallstones). The diagnostic performance was verified using an external validation cohort of 83 patients and compared with the performance of EUS endoscopists. RESULTS In the AI development cohort, we developed an EUS-AI algorithm and evaluated the diagnostic performance of the EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 57.9%, 96.5%, 77.8%, 91.6%, and 89.8%, respectively. In the external validation cohort, we compared diagnostic performances between EUS-AI and endoscopists. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, the sensitivity and specificity were 33.3% and 96.1% for EUS-AI; they were 74.2% and 44.9%, respectively, for the endoscopists. Besides, the accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). CONCLUSIONS This newly developed EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.
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Affiliation(s)
- Sung Ill Jang
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gachon University College of Health Science, Incheon, South Korea
| | - Eui Joo Kim
- Department of Internal Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Huapyong Kang
- Department of Internal Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Seung Jin Shon
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yu Jin Seol
- Department of Biomedical Engineering, Gachon University College of Health Science, Incheon, South Korea
| | - Dong Ki Lee
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gachon University College of Health Science, Incheon, South Korea
| | - Jae Hee Cho
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
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45
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Chiu Y, Chiu H, Chang L. Endoscopic features of
high‐risk T1
colorectal cancer: A case report with literature review. Adv in Digestive Med 2021. [DOI: 10.1002/aid2.13298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Yu‐Tse Chiu
- Division of Gastroenterology and Hepatology, Department of Internal Medicine Fu Jen Catholic University Hospital New Taipei Taiwan
| | - Han‐Mo Chiu
- Division of Gastroenterology and Hepatology, Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan
- Health Management Center National Taiwan University Hospital Taipei Taiwan
| | - Li‐Chun Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan
- Health Management Center National Taiwan University Hospital Taipei Taiwan
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Cai YW, Dong FF, Shi YH, Lu LY, Chen C, Lin P, Xue YS, Chen JH, Chen SY, Luo XB. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World J Clin Cases 2021; 9:9376-9385. [PMID: 34877273 PMCID: PMC8610875 DOI: 10.12998/wjcc.v9.i31.9376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/26/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.
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Affiliation(s)
- Yu-Wen Cai
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Fang-Fen Dong
- Department of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Yu-Heng Shi
- Computer Science and Engineering College, University of Alberta, Edmonton T6G 2R3, Canada
| | - Li-Yuan Lu
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Chen Chen
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Ping Lin
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Yu-Shan Xue
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Jian-Hua Chen
- Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
| | - Su-Yu Chen
- Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
| | - Xiong-Biao Luo
- Department of Computer Science, Xiamen University, Xiamen 361005, Fujian, China
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Henn J, Buness A, Schmid M, Kalff JC, Matthaei H. Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review. Langenbecks Arch Surg 2021. [PMID: 34716472 DOI: 10.1007/s00423-021-02348-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 10/03/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery. METHODS Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed. RESULTS Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM. CONCLUSIONS A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.
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Islam MM, Poly TN, Walther BA, Lin MC, Li YC(J. Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network. Cancers (Basel) 2021; 13:cancers13215253. [PMID: 34771416 PMCID: PMC8582393 DOI: 10.3390/cancers13215253] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 02/08/2023] Open
Abstract
Simple Summary Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Previous studies reported that the detection rate of gastric cancer (EGC) at an earlier stage is low, and the overall false-negative rate with esophagogastroduodenoscopy (EGD) is up to 25.8%, which often leads to inappropriate treatment. Accurate diagnosis of EGC can reduce unnecessary interventions and benefits treatment planning. Convolutional neural network (CNN) models have recently shown promising performance in analyzing medical images, including endoscopy. This study shows that an automated tool based on the CNN model could improve EGC diagnosis and treatment decision. Abstract Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current literature to evaluate the performance of the CNN model in detecting EGC. We conducted a systematic search in the online databases (e.g., PubMed, Embase, and Web of Science) for all relevant original studies on the subject of CNN in EGC published between 1 January 2010, and 26 March 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Moreover, a summary receiver operating characteristic curve (SROC) was plotted. Of the 171 studies retrieved, 15 studies met inclusion criteria. The application of the CNN model in the diagnosis of EGC achieved a SROC of 0.95, with corresponding sensitivity of 0.89 (0.88–0.89), and specificity of 0.89 (0.89–0.90). Pooled sensitivity and specificity for experts endoscopists were 0.77 (0.76–0.78), and 0.92 (0.91–0.93), respectively. However, the overall SROC for the CNN model and expert endoscopists was 0.95 and 0.90. The findings of this comprehensive study show that CNN model exhibited comparable performance to endoscopists in the diagnosis of EGC using digital endoscopy images. Given its scalability, the CNN model could enhance the performance of endoscopists to correctly stratify EGC patients and reduce work load.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Bruno Andreas Walther
- Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany;
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Correspondence: ; Tel.: +886-2-27361661 (ext. 7600); Fax: +886-2-6638-75371
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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50
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Kumar A, Gadag S, Nayak UY. The Beginning of a New Era: Artificial Intelligence in Healthcare. Adv Pharm Bull 2021; 11:414-425. [PMID: 34513616 PMCID: PMC8421632 DOI: 10.34172/apb.2021.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/26/2020] [Accepted: 07/15/2020] [Indexed: 11/13/2022] Open
Abstract
The healthcare sector is considered to be one of the largest and fast-growing industries in the world. Innovations and novel approaches have always remained the prime aims in order to bring massive development. Before the emergence of technology, the healthcare sector was dependent on manpower, which was time-consuming and less accurate with lack of efficiency. With the recent advancements in machine learning, the condition has been steadily revolutionizing. Artificial intelligence (AI) lies in the computer science department, which stresses on the intelligent machines’ creation, that work and react just like human beings. Currently, the applications of AI have been expanding into those fields, which was once thought to be the only domain of human expertise such as healthcare sector. In this review, we have shed light on the present usage of AI in the healthcare sector, such as its working, and the way this system is being implemented in different domains, such as drug discovery, diagnosis of diseases, clinical trials, remote patient monitoring, and nanotechnology. We have also briefly touched upon its applications in other sectors as well. The public opinions have also been analyzed and discussed along with the future prospects. We have discussed the merits, and the other side of AI, i.e. the disadvantages in the last part of the manuscript.
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Affiliation(s)
- Akshara Kumar
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576 104, India
| | - Shivaprasad Gadag
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576 104, India
| | - Usha Yogendra Nayak
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576 104, India
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