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Kucukkaya AS, Zeevi T, Chai NX, Raju R, Haider SP, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Sci Rep 2023; 13:7579. [PMID: 37165035 PMCID: PMC10172370 DOI: 10.1038/s41598-023-34439-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 04/29/2023] [Indexed: 05/12/2023] Open
Abstract
Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1-6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC-ROC). After prediction, the model's clinical relevance was evaluated using Kaplan-Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan-Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.
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Affiliation(s)
- Ahmet Said Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Nathan Xianming Chai
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Rajiv Raju
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Stefan Philipp Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Mohamed Elbanan
- Department of Diagnostic Radiology, Bridgeport Hospital, Yale New Haven Health System, 267 Grant Street, Bridgeport, CT, 06610, USA
| | - Alexandra Petukhova-Greenstein
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Visage Imaging, Inc., 12625 High Bluff Drive, Suite 205, San Diego, CA, 92130, USA
| | - John Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Michal Nowak
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Kirsten Cooper
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Elizabeth Thomas
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Jessica Santana
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Bernhard Gebauer
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - David Mulligan
- Transplantation and Immunology, Department of Surgery, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Ramesh Batra
- Transplantation and Immunology, Department of Surgery, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA.
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Iseke S, Zeevi T, Kucukkaya AS, Raju R, Gross M, Haider SP, Petukhova-Greenstein A, Kuhn TN, Lin M, Nowak M, Cooper K, Thomas E, Weber MA, Madoff DC, Staib L, Batra R, Chapiro J. Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study. AJR Am J Roentgenol 2023; 220:245-255. [PMID: 35975886 PMCID: PMC10015590 DOI: 10.2214/ajr.22.28077] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND. Posttreatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. OBJECTIVE. This proof-of-concept study evaluated the use of machine learning (ML) to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. METHODS. This retrospective study included 120 patients (88 men, 32 women; median age, 60.0 years) with early-stage HCC diagnosed who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation between June 2005 and March 2018. Patients underwent pretreatment MRI and posttreatment imaging surveillance. Imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network. Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models (clinical model, imaging model, combined model) for predicting recurrence within six time frames ranging from 1 through 6 years after treatment. Kaplan-Meier analysis with time to recurrence as the endpoint was used to assess the clinical relevance of model predictions. RESULTS. Tumor recurred in 44 of 120 (36.7%) patients during follow-up. The three models predicted recurrence with AUCs across the six time frames of 0.60-0.78 (clinical model), 0.71-0.85 (imaging model), and 0.62-0.86 (combined model). The mean AUC was higher for the imaging model than the clinical model (0.76 vs 0.68, respectively; p = .03), but the mean AUC was not significantly different between the clinical and combined models or between the imaging and combined models (p > .05). Kaplan-Meier curves were significantly different between patients predicted to be at low risk and those predicted to be at high risk by all three models for the 2-, 3-, 4-, 5-, and 6-year time frames (p < .05). CONCLUSION. The findings suggest that ML-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant. Adding MRI data as model input improved predictive performance over clinical parameters alone. The combined model did not surpass the imaging model's performance. CLINICAL IMPACT. ML-based models applied to currently underutilized imaging features may help design more reliable criteria for organ allocation and liver transplant eligibility.
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Affiliation(s)
- Simon Iseke
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Ahmet S Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Charité Center for Diagnostic and Interventional Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Rajiv Raju
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Charité Center for Diagnostic and Interventional Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Alexandra Petukhova-Greenstein
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Charité Center for Diagnostic and Interventional Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tom N Kuhn
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Department of Diagnostic and Interventional Radiology, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Clinical Research North America, Visage Imaging, Inc., San Diego, CA
| | - Michal Nowak
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Kirsten Cooper
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Elizabeth Thomas
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Marc-André Weber
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - David C Madoff
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
| | - Ramesh Batra
- Department of Surgery, Transplantation and Immunology, Yale University School of Medicine, New Haven, CT
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520
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3
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Fox AN, Liapakis A, Batra R, Bittermann T, Emamaullee J, Emre S, Genyk Y, Han H, Jackson W, Pomfret E, Raza M, Rodriguez-Davalos M, Rubman Gold S, Samstein B, Shenoy A, Taner T, Roberts JP. The use of nondirected donor organs in living donor liver transplantation: Perspectives and guidance. Hepatology 2022; 75:1579-1589. [PMID: 34859474 DOI: 10.1002/hep.32260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 12/13/2022]
Abstract
Interest in anonymous nondirected living organ donation is increasing in the United States and a small number of transplantation centers are accumulating an experience regarding nondirected donation in living donor liver transplantation. Herein, we review current transplant policy, discuss emerging data, draw parallels from nondirected kidney donation, and examine relevant considerations in nondirected living liver donation. We aim to provide a consensus guidance to ensure safe evaluation and selection of nondirected living liver donors and a schema for just allocation of nondirected grafts.
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Affiliation(s)
- Alyson N Fox
- Columbia University Irving Medical Center (CUIMC) Center for Liver Disease and Transplanation NY Presbyterian HospitalColumbia University Vagelos College of Physicians and SurgeonsNew YorkNew YorkUSA
| | - AnnMarie Liapakis
- Yale-New Haven Health Transplanation CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Ramesh Batra
- Yale-New Haven Health Transplanation CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Therese Bittermann
- Penn Transplant InstitutePenn MedicinePerelman School of Medicine Unniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Juliet Emamaullee
- University of Southern California (USC) Transplant InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Sukru Emre
- Yale-New Haven Health Transplanation CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Yuri Genyk
- University of Southern California (USC) Transplant InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Hyosun Han
- University of Southern California (USC) Transplant InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Whitney Jackson
- Colorado Center for Transplantation Care, Research and EducationUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Elizabeth Pomfret
- Colorado Center for Transplantation Care, Research and EducationUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Muhammad Raza
- Keck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Susan Rubman Gold
- Yale-New Haven Health Transplanation CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Benjamin Samstein
- Weill Cornell Medicine Center for Liver Disease and Transplantation NY Presbyterian HospitalWeill Cornell School of MedicineNew YorkNew YorkUSA
| | - Akhil Shenoy
- Columbia University Irving Medical Center (CUIMC) Center for Liver Disease and Transplanation NY Presbyterian HospitalColumbia University Vagelos College of Physicians and SurgeonsNew YorkNew YorkUSA
| | - Timucin Taner
- Mayo Clinic Transplant CenterMayo Clinic College of MedicineRochesterMinnesotaUSA
| | - John P Roberts
- Organ Transplant ProgramUniversity of California San Francisco (UCSF) HealthUCSF School of MedicineSan FranciscoCaliforniaUSA
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