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Schmauch B, Elsoukkary SS, Moro A, Raj R, Wehrle CJ, Sasaki K, Calderaro J, Sin-Chan P, Aucejo F, Roberts DE. Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. J Pathol Inform 2024; 15:100360. [PMID: 38292073 PMCID: PMC10825615 DOI: 10.1016/j.jpi.2023.100360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.
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Affiliation(s)
| | - Sarah S. Elsoukkary
- Owkin Lab, Owkin, Inc., New York, NY, USA
- Department of Pathology, Cleveland Clinic, Cleveland, OH, USA
| | - Amika Moro
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Roma Raj
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kazunari Sasaki
- Department of Surgery, Stanford University, Palo Alto, CA, USA
| | - Julien Calderaro
- Department of Pathology, Henri Mondor University Hospital, Créteil, France
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2
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Qi L, Zhu Y, Li J, Zhou M, Liu B, Chen J, Shen J. CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma. Sci Rep 2024; 14:20027. [PMID: 39198563 PMCID: PMC11358293 DOI: 10.1038/s41598-024-70208-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 08/13/2024] [Indexed: 09/01/2024] Open
Abstract
Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.
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Affiliation(s)
- Liang Qi
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China
| | - Yahui Zhu
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China
| | - Jinxin Li
- Department of Li Ka Shing Faculty of Medicine, The University of Hong Kong, HKSAR, China
| | - Mingzhen Zhou
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Baorui Liu
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China.
| | - Jie Shen
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321 Zhongshan Road, Nanjing, 210008, China.
- The Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
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3
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Altaf A, Munir MM, Endo Y, Khan MMM, Rashid Z, Khalil M, Guglielmi A, Aldrighetti L, Bauer TW, Marques HP, Martel G, Lam V, Weiss MJ, Fields RC, Poultsides G, Maithel SK, Endo I, Pawlik TM. Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection. J Gastrointest Surg 2024:S1091-255X(24)00594-8. [PMID: 39197678 DOI: 10.1016/j.gassur.2024.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 08/12/2024] [Accepted: 08/21/2024] [Indexed: 09/01/2024]
Abstract
PURPOSE We sought to develop an artificial intelligence (AI)-based model to predict early recurrence (ER) after curative-intent resection of neuroendocrine liver metastases (NELMs). METHODS Patients with NELM who underwent resection were identified from a multi-institutional database. ER was defined as recurrence within 12 months of surgery. Different AI-based models were developed to predict ER using 10 clinicopathologic factors. RESULTS Overall, 473 patients with NELM were included. Among 284 patients with recurrence (60.0%), 118 patients (41.5%) developed an ER. An ensemble AI model demonstrated the highest area under receiver operating characteristic curves of 0.763 and 0.716 in the training and testing cohorts, respectively. Maximum diameter of the primary neuroendocrine tumor, NELM radiologic tumor burden score, and bilateral liver involvement were the factors most strongly associated with risk of NELM ER. Patients predicted to develop ER had worse 5-year recurrence-free survival and overall survival (21.4% vs 37.1% [P = .002] and 61.6% vs 90.3% [P = .03], respectively) than patients not predicted to recur. An easy-to-use tool was made available online: (https://altaf-pawlik-nelm-earlyrecurrence-calculator.streamlit.app/). CONCLUSION An AI-based model demonstrated excellent discrimination to predict ER of NELM after resection. The model may help identify patients who can benefit the most from curative-intent resection, risk stratify patients according to prognosis, as well as guide tailored surveillance and treatment decisions including consideration of nonsurgical treatment options.
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Affiliation(s)
- Abdullah Altaf
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | - Muhammad Musaab Munir
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | - Yutaka Endo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | - Muhammad Muntazir M Khan
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | - Zayed Rashid
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | - Mujtaba Khalil
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States
| | | | | | - Todd W Bauer
- Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - Guillaume Martel
- Department of Surgery, University of Ottawa, Ottawa, Ontario, Canada
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, New South Wales, Australia
| | - Mathew J Weiss
- Department of Surgery, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Ryan C Fields
- Department of Surgery, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
| | - George Poultsides
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Shishir K Maithel
- Department of Surgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Itaru Endo
- Department of Surgery, Yokohama City University School of Medicine, Yokohama, 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, United States.
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Altaf A, Mustafa A, Dar A, Nazer R, Riyaz S, Rana A, Bhatti ABH. Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation. Surgery 2024:S0039-6060(24)00558-0. [PMID: 39181726 DOI: 10.1016/j.surg.2024.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine the risk of recurrence after living donor liver transplantation for hepatocellular carcinoma. METHODS The study was a single-center retrospective cohort study. Patients who underwent living donor liver transplantation for hepatocellular carcinoma were divided into training and validation cohorts (n = 192). The deep learning models were used to stratify patients in the training cohort into low- and high-risk groups, and 5-year recurrence-free survival was assessed in the validation cohort. RESULTS The median follow-up period was 59.1 (33.9-72.4) months. The artificial intelligence model (pretransplant factors) had an area under the curve of 0.86 in the training cohort and 0.71 in the validation cohort. The largest tumor diameter and alpha-fetoprotein level had the greatest Shapley Additive exPlanations values for recurrence (>0.4). The 5-year recurrence-free survival rates in the low- and high-risk groups were 92.6% and 45% (P < .001). In the second artificial intelligence model (pretransplant factors + grade), the area under the curve for the validation cohort was 0.77, with 5-year recurrence-free survival rates of 96% and 30% in the low- and high-risk groups (P < .001). None of the low-risk patients outside the Milan and University of California San Francisco Criteria had recurrence during follow-up. CONCLUSIONS The artificial intelligence-based hepatocellular carcinoma transplant recurrence models might improve patient selection for liver transplantation.
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Affiliation(s)
- Abdullah Altaf
- King Edward Medical University, Lahore, Pakistan; Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan. https://twitter.com/abdullahaltaf97
| | - Ahmed Mustafa
- Department of Robotics and Artificial Intelligence, National University of Science and Technology, Islamabad, Pakistan
| | - Abdullah Dar
- Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan
| | - Rashid Nazer
- Department of Radiology, Shifa International Hospital, Islamabad, Pakistan
| | - Shahzad Riyaz
- Department of Gastroenterology and Hepatology, Shifa International Hospital, Islamabad, Pakistan. https://twitter.com/shahzadriyaz
| | - Atif Rana
- Department of Radiology, Shifa International Hospital, Islamabad, Pakistan. https://twitter.com/atifranaIR
| | - Abu Bakar Hafeez Bhatti
- Department of HPB and Liver Transplant Surgery, Shifa International Hospital, Islamabad, Pakistan; Department of Surgery, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
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5
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Altaf A, Endo Y, Munir MM, Khan MMM, Rashid Z, Khalil M, Guglielmi A, Ratti F, Marques H, Cauchy F, Lam V, Poultsides G, Kitago M, Popescu I, Martel G, Gleisner A, Hugh T, Shen F, Endo I, Pawlik TM. Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma. HPB (Oxford) 2024; 26:1040-1050. [PMID: 38796346 DOI: 10.1016/j.hpb.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVE We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). METHODS HCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. RESULTS Overall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719-0.782) and 0.717 (95% CI:0.653-0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835-0.884) and 0.764 (95% CI: 0.704-0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). CONCLUSION The AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/).
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Affiliation(s)
- Abdullah Altaf
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Yutaka Endo
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad M Munir
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad Muntazir M Khan
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Zayed Rashid
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Mujtaba Khalil
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | | | | | - Hugo Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | - George Poultsides
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | - Ana Gleisner
- Department of Surgery, University of Colorado, Aurora, CO, United States
| | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Itaru Endo
- Department of Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
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Simsek C. The evolution and revolution of artificial intelligence in hepatology: From current applications to future paradigms. HEPATOLOGY FORUM 2024; 5:97-99. [PMID: 39006141 PMCID: PMC11237248 DOI: 10.14744/hf.2024.2024.ed0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 06/30/2024] [Indexed: 07/16/2024]
Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hacettepe University School of Medicine, Ankara, Turkiye
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7
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Martínez Burgos M, González Grande R, López Ortega S, Santaella Leiva I, de la Cruz Lombardo J, Santoyo Santoyo J, Jiménez Pérez M. Liver Transplantation for Hepatocarcinoma: Results over Two Decades of a Transplantation Programme and Analysis of Factors Associated with Recurrence. Biomedicines 2024; 12:1302. [PMID: 38927509 PMCID: PMC11200972 DOI: 10.3390/biomedicines12061302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND In recent years, many studies have attempted to develop models to predict the recurrence of hepatocarcinoma after liver transplantation. METHOD A single-centre, retrospective cohort study analysed patients receiving transplants due to hepatocarcinoma during the 20 years of the transplant programme. We analysed patient survival, hepatocarcinoma recurrence and the influence of the different factors described in the literature as related to hepatocarcinoma recurrence. We compared the results of previous items between the first and second decades of the transplantation programme (1995-2010 and 2010-2020). RESULTS Of 265 patients, the patient survival rate was 68% at 5 years, 58% at 10 years, 45% at 15 years and 34% at 20 years. The overall recurrence rate of hepatocarcinoma was 14.5%, without differences between periods. Of these, 54% of recurrences occurred early, in the first two years after transplantation. Of the parameters analysed, an alpha-fetoprotein level of >16 ng/mL, the type of immunosuppression used and the characteristics of the pathological anatomy of the explant were significant. A trend towards statistical significance was identified for the number of nodules and the size of the largest nodule. Logistic regression analysis was used to develop a model with a sensitivity of 85.7% and a specificity of 35.7% to predict recurrences in our cohort. Regarding the comparison between periods, the survival and recurrence rates of hepatocarcinoma were similar. The impact of the factors analysed in both decades was similar. CONCLUSIONS Most recurrences occur during the first two years post-transplantation, so closer follow-ups should be performed during this period, especially in those patients where the model predicts a high risk of recurrence. The detection of patients at higher risk of recurrence allows for closer follow-up and may, in the future, make them candidates for adjuvant or neoadjuvant systemic therapies to transplantation.
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Affiliation(s)
- María Martínez Burgos
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
| | - Rocío González Grande
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
| | - Susana López Ortega
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
| | - Inmaculada Santaella Leiva
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
| | - Jesús de la Cruz Lombardo
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
| | - Julio Santoyo Santoyo
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
- Liver Transplant Unit, General Surgery and Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain
| | - Miguel Jiménez Pérez
- Liver Transplant Unit, Digestive System Department, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain; (R.G.G.); (S.L.O.); (I.S.L.); (J.d.l.C.L.); (M.J.P.)
- Instituto de Investigación Biomedica de Plataforma en Nanomedicina—IBIMA Plataforma Bionand, 29590 Malaga, Spain;
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Wang S, Shao M, Fu Y, Zhao R, Xing Y, Zhang L, Xu Y. Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis. Sci Rep 2024; 14:13232. [PMID: 38853169 PMCID: PMC11163004 DOI: 10.1038/s41598-024-63531-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.
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Affiliation(s)
- Shoucheng Wang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Mingyi Shao
- Personnel Department, The First Affiliated Hospitalof Henan University of Chinese Medicine, Zhengzhou, 450000, China.
| | - Yu Fu
- Research Department, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Ruixia Zhao
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yunfei Xing
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Liujie Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
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9
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Lai Q, Parisse S, Ginanni Corradini S, Ferri F, Kolovou K, Campagna P, Melandro F, Mennini G, Merli M, Rossi M. Evolution of transplant oncology indications: a single-institution experience over 40 years. Updates Surg 2024; 76:911-921. [PMID: 38589745 PMCID: PMC11130028 DOI: 10.1007/s13304-024-01827-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/06/2024] [Indexed: 04/10/2024]
Abstract
Liver transplantation (LT) for uncommon tumoral indications has changed across the decades, with impaired results reported in the first historical series mainly for non-tumoral-related causes. Recently, renewed interest in liver transplant oncology has been reported. The study aims to analyze a mono-center experience exploring the evolution and the impact on patient survival of LT in uncommon tumoral indications. A retrospective analysis of 851 LT performed during 1982-2023 was investigated. 33/851 (3.9%) uncommon tumoral indications were reported: hepatocellular carcinoma (HCC) on non-cirrhotic liver (n = 14), peri-hilar (phCCA) (n = 8) and intrahepatic cholangiocarcinoma (i-CCA) (n = 3), metastatic disease (n = 4), hepatic hemangioendothelioma (n = 2), and benign tumor (n = 2). Uncommon tumoral indications were mainly transplanted during the period 1982-1989, with a complete disappearance after the year 2000 and a slight rise in the last years. Poor outcomes were reported: 5-year survival rates were 28.6%, 25.0%, 0%, and 0% in the case of HCC on non-cirrhotic liver, phCCA, i-CCA, and metastases, respectively. However, the cause of patient death was often related to non-tumoral conditions. LT for uncommon oncological diseases has increased worldwide in recent decades. Historical series report poor survival outcomes despite more recent data showing promising results. Hence, the decision to transplant these patients should be under the risk and overall benefit of the patient. The results of the ongoing protocol studies are expected to confirm the validity of the unconventional tumor indications.
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Affiliation(s)
- Quirino Lai
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy.
| | - Simona Parisse
- Department of Translational and Precision Medicine, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Stefano Ginanni Corradini
- Department of Translational and Precision Medicine, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Flaminia Ferri
- Department of Translational and Precision Medicine, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Konstantina Kolovou
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Pasquale Campagna
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Fabio Melandro
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Gianluca Mennini
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Manuela Merli
- Department of Translational and Precision Medicine, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
| | - Massimo Rossi
- General Surgery and Organ Transplantation Unit, Department of General and Specialty Surgery, Sapienza University of Rome, AOU Policlinico Umberto I of Rome, Rome, Italy
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10
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Yang Y, Lin M, Zhao H, Peng Y, Huang F, Lu Z. A survey of recent methods for addressing AI fairness and bias in biomedicine. J Biomed Inform 2024; 154:104646. [PMID: 38677633 PMCID: PMC11129918 DOI: 10.1016/j.jbi.2024.104646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/17/2024] [Indexed: 04/29/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. METHODS We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. RESULTS The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.
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Affiliation(s)
- Yifan Yang
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA; Department of Computer Science, University of Maryland, College Park, USA
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, NY, USA
| | - Han Zhao
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, NY, USA
| | - Furong Huang
- Department of Computer Science, University of Maryland, College Park, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
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11
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Peng G, Cao X, Huang X, Zhou X. Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization. Eur J Radiol Open 2024; 12:100551. [PMID: 38347937 PMCID: PMC10859286 DOI: 10.1016/j.ejro.2024.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Purpose To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment. Methods A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis. Results In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk. Conclusions Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.
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Affiliation(s)
- Gang Peng
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojing Cao
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyu Huang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Zhou
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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12
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Zhou J, Cui R, Lin L. A Systematic Review of the Application of Computational Technology in Microtia. J Craniofac Surg 2024; 35:1214-1218. [PMID: 38710037 DOI: 10.1097/scs.0000000000010210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 05/08/2024] Open
Abstract
Microtia is a congenital and morphological anomaly of one or both ears, which results from a confluence of genetic and external environmental factors. Up to now, extensive research has explored the potential utilization of computational methodologies in microtia and has obtained promising results. Thus, the authors reviewed the achievements and shortcomings of the research mentioned previously, from the aspects of artificial intelligence, computer-aided design and surgery, computed tomography, medical and biological data mining, and reality-related technology, including virtual reality and augmented reality. Hoping to offer novel concepts and inspire further studies within this field.
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Affiliation(s)
- Jingyang Zhou
- Ear Reconstruction Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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13
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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14
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Mo Q, Li W, Liu L, Hao Z, Jia S, Duo Y. A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma. Clin Transl Oncol 2024; 26:375-388. [PMID: 37368201 DOI: 10.1007/s12094-023-03244-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 05/31/2023] [Indexed: 06/28/2023]
Abstract
PURPOSE Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5. METHODS To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs). RESULTS Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients). CONCLUSIONS A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.
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Affiliation(s)
- Qingguo Mo
- Department of Interventional Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Wenjing Li
- School of Pharmacy, Qiqihar Medical University, Qiqihar, China
| | - Lin Liu
- Department of Interventional Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Zhidong Hao
- Department of Interventional Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Shengjun Jia
- The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, China
| | - Yongsheng Duo
- Department of Vascular Burn Surgery, The Third Affiliated Hospital of Qiqihar Medical University, Tiefeng District, 27 Tai Shun Street, Qiqihar, 161000, Heilongjiang Province, China.
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Wei Q, Tan N, Xiong S, Luo W, Xia H, Luo B. Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:5701. [PMID: 38067404 PMCID: PMC10705136 DOI: 10.3390/cancers15235701] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/25/2023] [Accepted: 11/29/2023] [Indexed: 06/24/2024] Open
Abstract
(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.
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Affiliation(s)
- Qiuxia Wei
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Nengren Tan
- School of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, China;
| | - Shiyu Xiong
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Wanrong Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
| | - Haiying Xia
- School of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, China;
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China; (Q.W.); (S.X.); (W.L.)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China
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16
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Li X, Yu X, Tian D, Liu Y, Li D. Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning. Med Phys 2023; 50:7049-7059. [PMID: 37722701 DOI: 10.1002/mp.16748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 08/17/2023] [Accepted: 09/08/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics. PURPOSE The purpose of this study was to explore the potential application value of pathomics signatures. METHODS Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links. RESULTS The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.
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Affiliation(s)
- Xiaoyuan Li
- Department of Traditional Chinese Medicine, The affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaoqian Yu
- Department of Dermatology, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong, China
| | - Duanliang Tian
- Department of Tuina, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong, China
| | - Yiran Liu
- Department of Traditional Chinese Medicine, Weifang Medical College, Weifang, Shandong, China
| | - Ding Li
- Department of Traditional Chinese Medicine, The affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Shams MY, El-kenawy ESM, Ibrahim A, Elshewey AM. A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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18
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Lakshmipriya B, Pottakkat B, Ramkumar G. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artif Intell Med 2023; 141:102557. [PMID: 37295904 DOI: 10.1016/j.artmed.2023.102557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
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Affiliation(s)
- B Lakshmipriya
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Biju Pottakkat
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
| | - G Ramkumar
- Department of Radio Diagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
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Schooler GR, Infante JC, Acord M, Alazraki A, Chavhan GB, Davis JC, Khanna G, Morani AC, Morin CE, Nguyen HN, Rees MA, Shaikh R, Srinivasan A, Squires JH, Tang E, Thacker PG, Towbin AJ. Imaging of pediatric liver tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e29965. [PMID: 36102690 PMCID: PMC10641897 DOI: 10.1002/pbc.29965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 08/16/2022] [Indexed: 11/11/2022]
Abstract
Primary hepatic malignancies are relatively rare in the pediatric population, accounting for approximately 1%-2% of all pediatric tumors. Hepatoblastoma and hepatocellular carcinoma are the most common primary liver malignancies in children under the age of 5 years and over the age of 10 years, respectively. This paper provides consensus-based imaging recommendations for evaluation of patients with primary hepatic malignancies at diagnosis and follow-up during and after therapy.
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Affiliation(s)
- Gary R. Schooler
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Juan C. Infante
- Department of Radiology, Nemours Children’s Health, Orlando, FL
| | - Michael Acord
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Adina Alazraki
- Department of Radiology and Imaging Sciences, Emory University, Children’s Healthcare of Atlanta, Atlanta, GA
| | - Govind B. Chavhan
- Department of Diagnostic Imaging, Hospital for Sick Children and Department of Medical Imaging, University of Toronto, ON Canada
| | | | - Geetika Khanna
- Department of Radiology and Imaging Sciences, Emory University, Children’s Healthcare of Atlanta, Atlanta, GA
| | - Ajaykumar C. Morani
- Singleton Department of Radiology, Texas Children’s Hospital and Department of Radiology, Baylor College of Medicine, Houston, TX
| | - Cara E. Morin
- Department of Radiology, Cincinnati Children’s Hospital, Cincinnati, OH
| | - HaiThuy N. Nguyen
- Singleton Department of Radiology, Texas Children’s Hospital and Department of Radiology, Baylor College of Medicine, Houston, TX
| | - Mitchell A. Rees
- Department of Radiology, Nationwide Children’s Hospital, Columbus, OH
| | - Raja Shaikh
- Department of Radiology, Boston Children’s Hospital, Boston, MA
| | - Abhay Srinivasan
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Judy H. Squires
- Department of Radiology, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA
| | - Elizabeth Tang
- Department of Radiology, Seattle Children’s Hospital, Seattle, WA
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Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:2928. [PMID: 37296890 PMCID: PMC10251861 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA 02115, USA; (A.M.); (J.C.P.)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Jonathan P. Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
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21
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Li R, Qu W, Liu Q, Tan Y, Zhang W, Hao Y, Jiang N, Mao Z, Ye J, Jiao J, Gao Q, Cui B, Dong T. Development and validation of a deep learning survival model for cervical adenocarcinoma patients. BMC Bioinformatics 2023; 24:146. [PMID: 37055729 PMCID: PMC10103498 DOI: 10.1186/s12859-023-05239-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/20/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction. METHODS A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model. RESULTS The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test set, our model achieved a 0.80 c-index and 0.13 Brier score. Thus, we developed prognosis-oriented risk grouping for patients according to risk scores computed by our DL model. Notable differences among groupings were observed. In addition, a personalized survival prediction system based on our risk-scoring grouping was developed. CONCLUSIONS We developed a deep neural network model for cervical adenocarcinoma patients. The performance of this model proved to be superior to other models. The results of external validation supported the possibility that the model can be used in clinical work. Finally, our survival grouping and personalized prediction system provided more accurate prognostic information for patients than traditional FIGO stages.
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Grants
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- No.2020SDUCRCA007 Clinical Research Center of Shandong University
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- QDKY2020BS04 Scientific Research Foundation of Qilu Hospital of Shandong University(Qingdao)
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
- ZR2021QH107 Natural Science Foundation of Shandong Province, China
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Affiliation(s)
- Ruowen Li
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Wenjie Qu
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Qingqing Liu
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Yilin Tan
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Wenjing Zhang
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, No. 107, Wenhua West Road, Jinan, 250012, Shandong Province, China
| | - Yiping Hao
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Nan Jiang
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Zhonghao Mao
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Jinwen Ye
- Cheeloo College of Medicine, Shandong University, No. 44 Wenhua West Road, Lixia District, Jinan, 250012, Shandong Province, China
| | - Jun Jiao
- Department of Obstetrics and Gynaecology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Qun Gao
- Department of Obstetrics and Gynecology, Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266555, Shandong Province, China
| | - Baoxia Cui
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, No. 107, Wenhua West Road, Jinan, 250012, Shandong Province, China.
| | - Taotao Dong
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, No. 107, Wenhua West Road, Jinan, 250012, Shandong Province, China.
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22
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Jiang Z, Yan L, Deng S, Gu J, Qin L, Mao F, Xue Y, Cai W, Nie X, Liu H, Shang F, Tao K, Wang J, Wu K, Cao Y, Cai K. Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer. DISEASE MARKERS 2023; 2023:5178750. [PMID: 36860582 PMCID: PMC9969972 DOI: 10.1155/2023/5178750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/05/2023] [Accepted: 01/28/2023] [Indexed: 02/20/2023]
Abstract
Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.
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Affiliation(s)
- Zhenxing Jiang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shenghe Deng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Junnan Gu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Le Qin
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
- Department of General Surgery, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, Xinjiang 832008, China
| | - Fuwei Mao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yifan Xue
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Wentai Cai
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Hongli Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Fumei Shang
- Department of Medical Oncology, Nanyang Central Hospital, Nanyang, Henan, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Jiliang Wang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Ke Wu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yinghao Cao
- Department of Digestive Surgical Oncology, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Kailin Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
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23
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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24
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Liu X, Elbanan MG, Luna A, Haider MA, Smith AD, Sabottke CF, Spieler BM, Turkbey B, Fuentes D, Moawad A, Kamel S, Horvat N, Elsayes KM. Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status. AJR Am J Roentgenol 2022; 219:985-995. [PMID: 35766531 PMCID: PMC10616929 DOI: 10.2214/ajr.22.27695] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.
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Affiliation(s)
- Xiaoyang Liu
- Joint Department of Medical Imaging, Division of Abdominal Imaging, University Health Network, University of Toronto, ON, Canada
| | - Mohamed G Elbanan
- Department of Radiology, Yale New Haven Health, Bridgeport Hospital, Bridgeport, CT
| | | | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ
| | - Bradley M Spieler
- Department of Radiology, University Medical Center, Louisiana State University Health Sciences Center, New Orleans, LA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD
| | - David Fuentes
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ahmed Moawad
- Department of Diagnostic and Interventional Radiology, Mercy Catholic Medical Center, Darby, PA
| | - Serageldin Kamel
- Department of Lymphoma, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Khaled M Elsayes
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030
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25
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Predicting mortality in the very old: a machine learning analysis on claims data. Sci Rep 2022; 12:17464. [PMID: 36261581 PMCID: PMC9581892 DOI: 10.1038/s41598-022-21373-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 09/27/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) may be used to predict mortality. We used claims data from one large German insurer to develop and test differently complex ML prediction models, comparing them for their (balanced) accuracy, but also the importance of different predictors, the relevance of the follow-up period before death (i.e. the amount of accumulated data) and the time distance of the data used for prediction and death. A sample of 373,077 insured very old, aged 75 years or above, living in the Northeast of Germany in 2012 was drawn and followed over 6 years. Our outcome was whether an individual died in one of the years of interest (2013-2017) or not; the primary metric was (balanced) accuracy in a hold-out test dataset. From the 86,326 potential variables, we used the 30 most important ones for modeling. We trained a total of 45 model combinations: (1) Three different ML models were used; logistic regression (LR), random forest (RF), extreme gradient boosting (XGB); (2) Different periods of follow-up were employed for training; 1-5 years; (3) Different time distances between data used for prediction and the time of the event (death/survival) were set; 0-4 years. The mortality rate was 9.15% in mean per year. The models showed (balanced) accuracy between 65 and 93%. A longer follow-up period showed limited to no advantage, but models with short time distance from the event were more accurate than models trained on more distant data. RF and XGB were more accurate than LR. For RF and XGB sensitivity and specificity were similar, while for LR sensitivity was significantly lower than specificity. For all three models, the positive-predictive-value was below 62% (and even dropped to below 20% for longer time distances from death), while the negative-predictive-value significantly exceeded 90% for all analyses. The utilization of and costs for emergency transport as well as emergency and any hospital visits as well as the utilization of conventional outpatient care and laboratory services were consistently found most relevant for predicting mortality. All models showed useful accuracies, and more complex models showed advantages. The variables employed for prediction were consistent across models and with medical reasoning. Identifying individuals at risk could assist tailored decision-making and interventions.
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26
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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Qin Y, Zhu LH, Zhao W, Wang JJ, Wang H. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer. Front Oncol 2022; 12:913683. [PMID: 36016617 PMCID: PMC9395725 DOI: 10.3389/fonc.2022.913683] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
By breaking the traditional medical image analysis framework, precision medicine-radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
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Affiliation(s)
- Yun Qin
- School of Physics, Beihang University, Beijing, China
| | - Li-Hua Zhu
- School of Physics, Beihang University, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Jun-Jie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
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28
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The impact of biological features for a better prediction of posttransplant hepatocellular cancer recurrence. Curr Opin Organ Transplant 2022; 27:305-311. [PMID: 36354256 DOI: 10.1097/mot.0000000000000955] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW Morphological criteria (i.e., Milan Criteria) have been considered for a long time to be the best tool for selecting patients with hepatocellular cancer (HCC) waiting for liver transplantation (LT). In the last ten years, a refinement of the selection criteria has been observed, with the introduction of biological tumor characteristics enabling to enlarge the number of potential transplant candidates and to select LT candidates with a lower risk of posttransplant recurrence. RECENT FINDINGS Several biological tumor aspects have been explored and validated in international cohorts to expand the ability to predict patients at high risk for recurrence. Alpha-fetoprotein, radiological response to locoregional treatments, and other more recently proposed markers have been principally explored. Moreover, more complex statistical approaches (i.e., deep learning) have been advocated to explore the nonlinear intercorrelations between the investigated features. SUMMARY The addition of biological aspects to morphology has improved the ability to discriminate among high- and low-risk patients for recurrence. New prognostic algorithms based on the more sophisticated artificial intelligence approach are further improving the capability to select LT candidates with HCC.
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29
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Lerut J. Modern technology, liver surgery and transplantation. Hepatobiliary Pancreat Dis Int 2022; 21:307-309. [PMID: 35750600 DOI: 10.1016/j.hbpd.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/16/2022] [Indexed: 02/05/2023]
Affiliation(s)
- Jan Lerut
- Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 55 1200, Brussels, Belgium.
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30
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Zhang J, Huang S, Xu Y, Wu J. Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:763842. [PMID: 35280776 PMCID: PMC8907853 DOI: 10.3389/fonc.2022.763842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Background The presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging. Aim To assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data. Methods Original studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity. Results Data from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75–0.90], 0.84 [0.77–0.89], 5.14 [3.53–7.48], 0.2 [0.12–0.31], and 0.90 [0.87–0.93]; and for non-deep learning models, they were 0.77 [0.71–0.82], 0.77 [0.73–0.80], 3.30 [2.83–3.84], 0.30 [0.24–0.38], and 0.82 [0.79–0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC. Conclusion This meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.
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Affiliation(s)
- Jian Zhang
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Shenglan Huang
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Yongkang Xu
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Jianbing Wu
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
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31
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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32
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Sarode SC, Sharma NK, Sarode G. A critical appraisal on cancer prognosis and artificial intelligence. Future Oncol 2022; 18:1531-1534. [PMID: 35137629 DOI: 10.2217/fon-2021-1528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Sachin C Sarode
- Department of Oral Pathology & Microbiology, Dr. D. Y. Patil Dental College & Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, 411018, Maharashtra, India
| | - Nilesh Kumar Sharma
- Cancer & Translational Research Laboratory, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pune, 411033, Maharashtra, India
| | - Gargi Sarode
- Department of Oral Pathology & Microbiology, Dr. D. Y. Patil Dental College & Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, 411018, Maharashtra, India
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33
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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] [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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34
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Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2:127-135. [DOI: 10.37126/aige.v2.i4.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/05/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
Abstract
Each year, hepatocellular carcinoma is diagnosed in more than half a million people worldwide. It is the fifth most common cancer in men and the seventh most common cancer in women. Its diagnosis is currently made using imaging techniques, such as computed tomography and magnetic resonance imaging. For most cirrhotic patients, these methods are enough for diagnosis, foregoing the necessity of a liver biopsy. In order to improve outcomes and bypass obstacles, many companies and clinical centers have been trying to develop deep learning systems that could be able to diagnose and classify liver nodules in the cirrhotic liver, in which the neural networks are one of the most efficient approaches to accurately diagnose liver nodules. Despite the advances in deep learning systems for the diagnosis of imaging techniques, there are many issues that need better development in order to make such technologies more useful in daily practice.
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Affiliation(s)
| | | | - John Soldera
- Computer Science, Federal Institute of Education, Science and Technology Farroupilha, Santo Ângelo 98806-700, RS, Brazil
| | - Jonathan Soldera
- Clinical Gastroenterology, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
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35
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Blidisel A, Marcovici I, Coricovac D, Hut F, Dehelean CA, Cretu OM. Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective. Cancers (Basel) 2021; 13:3651. [PMID: 34359553 PMCID: PMC8344976 DOI: 10.3390/cancers13153651] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/16/2021] [Accepted: 07/17/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC), the most frequent form of primary liver carcinoma, is a heterogenous and complex tumor type with increased incidence, poor prognosis, and high mortality. The actual therapeutic arsenal is narrow and poorly effective, rendering this disease a global health concern. Although considerable progress has been made in terms of understanding the pathogenesis, molecular mechanisms, genetics, and therapeutical approaches, several facets of human HCC remain undiscovered. A valuable and prompt approach to acquire further knowledge about the unrevealed aspects of HCC and novel therapeutic candidates is represented by the application of experimental models. Experimental models (in vivo and in vitro 2D and 3D models) are considered reliable tools to gather data for clinical usability. This review offers an overview of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model. Furthermore, we also focus on the role played by computational studies (in silico models and artificial intelligence-based prediction models) as promising novel tools in liver cancer research.
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Affiliation(s)
- Alexandru Blidisel
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Iasmina Marcovici
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Dorina Coricovac
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Florin Hut
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Cristina Adriana Dehelean
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Octavian Marius Cretu
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
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36
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Granata V, Grassi R, Fusco R, Belli A, Cutolo C, Pradella S, Grazzini G, La Porta M, Brunese MC, De Muzio F, Ottaiano A, Avallone A, Izzo F, Petrillo A. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect Agent Cancer 2021; 16:53. [PMID: 34281580 PMCID: PMC8287696 DOI: 10.1186/s13027-021-00393-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023] Open
Abstract
This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy
- Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Milan, Italy
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Silvia Pradella
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giulia Grazzini
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Alessandro Ottaiano
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Lovejoy CA, Alqahtani SA. AI in colonoscopy and beyond: On the cusp of clinical implementation? United European Gastroenterol J 2021; 9:525-526. [PMID: 33960666 PMCID: PMC8259269 DOI: 10.1002/ueg2.12076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/05/2021] [Indexed: 11/06/2022] Open
Affiliation(s)
| | - Saleh A. Alqahtani
- Liver Transplantation UnitKing Faisal Specialist Hospital & Research CenterRiyadhSaudi Arabia
- Division of Gastroenterology and HepatologyJohns Hopkins UniversityBaltimoreMarylandUSA
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