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Haghshomar M, Rodrigues D, Kalyan A, Velichko Y, Borhani A. Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies. Front Oncol 2024; 14:1362737. [PMID: 38779098 PMCID: PMC11109422 DOI: 10.3389/fonc.2024.1362737] [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: 12/28/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.
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Affiliation(s)
| | | | | | | | - Amir Borhani
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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2
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Luo X, Li P, Chen H, Zhou K, Piao S, Yang L, Hu B, Geng D. Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning. Phys Med Biol 2024; 69:065008. [PMID: 38330492 DOI: 10.1088/1361-6560/ad2790] [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: 09/21/2023] [Accepted: 02/08/2024] [Indexed: 02/10/2024]
Abstract
Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.
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Affiliation(s)
- Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, People's Republic of China
| | - Peiwen Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, People's Republic of China
| | - Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, People's Republic of China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, People's Republic China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, People's Republic of China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, People's Republic of China
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, People's Republic China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, People's Republic of China
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Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P. CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 2024; 42:246-260. [PMID: 37926780 DOI: 10.1007/s11604-023-01504-0] [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: 09/13/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, 51092, Reims, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, 34000, Montpellier, France
- PINKCC Lab, IRCM, U1194, 34000, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France.
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Lee H, Chang W, Kim HY, Sung P, Cho J, Lee YJ, Kim YH. Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients. Eur Radiol 2024; 34:2036-2047. [PMID: 37656175 DOI: 10.1007/s00330-023-10135-y] [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: 11/07/2022] [Revised: 07/06/2023] [Accepted: 07/14/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms. METHODS This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE). RESULTS In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP. CONCLUSIONS CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs. CLINICAL RELEVANCE STATEMENT This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients. KEY POINTS Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.
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Affiliation(s)
- Heejin Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Pamela Sung
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Ye Y, Zhang N, Wu D, Huang B, Cai X, Ruan X, Chen L, Huang K, Li ZP, Wu PM, Jiang J, Dan G, Peng Z. Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images. BIOMED RESEARCH INTERNATIONAL 2024; 2024:9267554. [PMID: 38464681 PMCID: PMC10923620 DOI: 10.1155/2024/9267554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 12/20/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Purpose Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. Method In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. Results The mean DSC of DCNN-F, DFN-F, and DFN-R was 0.69 ± 0.20 (median, 0.72), 0.74 ± 0.21 (median, 0.77), and 0.83 ± 0.13 (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was 0.79 ± 0.11 (median, 0.83), which was lower than the performance of DFN-R. Conclusions Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.
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Affiliation(s)
- Yufeng Ye
- The First Clinical College of Jinan University, Guangzhou, China
- Department of Radiology, Panyu Central Hospital, Guangzhou, China
| | - Naiwen Zhang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Dasheng Wu
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Department of Radiology, Panyu Central Hospital, Guangzhou, China
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Shenzhen University Clinical Research Center for Neurological Diseases, Shenzhen, Guangdong, China
| | - Xun Cai
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Xiaolei Ruan
- Jiuquan Satellite Launch Center, Lanzhou, Gansu, China
| | - Liangliang Chen
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Kun Huang
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Zi-Ping Li
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Po-Man Wu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Jinzhao Jiang
- Department of Radiology, Shenzhen University General Hospital, Shenzhen, China
| | - Guo Dan
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
| | - Zhenpeng Peng
- Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [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: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Gross M, Huber S, Arora S, Ze'evi T, Haider SP, Kucukkaya AS, Iseke S, Kuhn TN, Gebauer B, Michallek F, Dewey M, Vilgrain V, Sartoris R, Ronot M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey JA. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. Eur Radiol 2024:10.1007/s00330-023-10495-5. [PMID: 38217704 DOI: 10.1007/s00330-023-10495-5] [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: 08/22/2023] [Revised: 09/20/2023] [Accepted: 10/29/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVES To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). MATERIALS AND METHODS This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). RESULTS In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. CONCLUSION Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. CLINICAL RELEVANCE STATEMENT Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. KEY POINTS • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.
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Affiliation(s)
- Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Steffen Huber
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sandeep Arora
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Tal Ze'evi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Ahmet S Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Iseke
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Tom Niklas Kuhn
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Diagnostic and Interventional Radiology, University Duesseldorf, Duesseldorf, Germany
| | - Bernhard Gebauer
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Michallek
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Marc Dewey
- Charité Center for Diagnostic and Interventional Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Valérie Vilgrain
- Université Paris Cité, Île-de-France, Paris, France
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Department of Radiology, Île-de-France, Clichy, France
| | - Riccardo Sartoris
- Université Paris Cité, Île-de-France, Paris, France
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Department of Radiology, Île-de-France, Clichy, France
| | - Maxime Ronot
- Université Paris Cité, Île-de-France, Paris, France
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Department of Radiology, Île-de-France, Clichy, France
| | - Ariel Jaffe
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Mario Strazzabosco
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Urology, Yale University School of Medicine, New Haven, CT, USA.
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8
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Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang TW, Knobloch G, Koh DM, Kudo M, Lee JM, Murakami T, Pinato DJ, Ringe KI, Song B, Tabrizian P, Wang J, Yoon JH, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management. Eur Radiol 2023; 33:9152-9166. [PMID: 37500964 PMCID: PMC10730664 DOI: 10.1007/s00330-023-09928-y] [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: 01/03/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 07/29/2023]
Abstract
The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.
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Affiliation(s)
- Bachir Taouli
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-guided therapy, Medical University of Vienna, Vienna, Austria
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagpreet Chhatwal
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn Fowler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tae Wook Kang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gesine Knobloch
- Global Medical and Clinical Affairs and Digital Development, Radiology, Bayer Pharmaceuticals, Berlin, Germany
| | - Dow-Mu Koh
- Department of Diagnostic Radiology, Royal Marsden Hospital, Sutton, UK
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - David J Pinato
- Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London, UK
- Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Kristina I Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Parissa Tabrizian
- Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
- Liver Disease Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Valérie Vilgrain
- Université Paris Cité and Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP Nord, Hôpital Beaujon, Clichy, France
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Wang J, Peng Y, Jing S, Han L, Li T, Luo J. A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+. BMC Cancer 2023; 23:1060. [PMID: 37923988 PMCID: PMC10623778 DOI: 10.1186/s12885-023-11432-x] [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: 02/28/2023] [Accepted: 09/21/2023] [Indexed: 11/06/2023] Open
Abstract
OBJECTIVE Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations. METHODS 105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time. RESULTS The average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively. CONCLUSION UNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.
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Affiliation(s)
- Jing Wang
- Department of General medicine, The First Medical Center Department of Chinese PLA General Hospital, Peking, 100039, China
| | - Yanyang Peng
- Department of Radiology, First Medical Center of General Hospital of People's Liberation Army, Peking, China
| | - Shi Jing
- Department of Oncology, Huaihe Hospital, Henan University, Kaifeng, 475000, China
| | - Lujun Han
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Cancer for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510030, China.
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
| | - Tian Li
- School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China.
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
| | - Junpeng Luo
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
- Academy for Advanced Interdisciplinary Studies, Henan University, Zhengzhou, 450046, China.
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10
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Hille G, Agrawal S, Tummala P, Wybranski C, Pech M, Surov A, Saalfeld S. Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107647. [PMID: 37329803 DOI: 10.1016/j.cmpb.2023.107647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 04/21/2023] [Accepted: 06/05/2023] [Indexed: 06/19/2023]
Abstract
Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. METHODS This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was primarily applied to single-modality non-contrast-enhanced liver MRI and additionally to the publicly available computed tomography (CT) data of the liver tumor segmentation (LiTS) challenge to verify the applicability on other modalities. For a broader evaluation, multiple state-of-the-art networks were implemented and applied, ensuring direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various influencing factors on the segmentation accuracy of the presented method. RESULTS With Dice similarity scores of averaged 98±2% for liver and 81±28% lesion segmentation on the MRI dataset and 97±2% and 79±25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging. CONCLUSION The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.
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Affiliation(s)
- Georg Hille
- Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany.
| | - Shubham Agrawal
- Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany
| | - Pavan Tummala
- Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany
| | - Christian Wybranski
- Department of Radiology, University Hospital of Magdeburg, Magdeburg, Germany
| | - Maciej Pech
- Department of Radiology, University Hospital of Magdeburg, Magdeburg, Germany
| | - Alexey Surov
- Department of Radiology, University Hospital of Magdeburg, Magdeburg, Germany
| | - Sylvia Saalfeld
- Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany
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11
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Said D, Carbonell G, Stocker D, Hectors S, Vietti-Violi N, Bane O, Chin X, Schwartz M, Tabrizian P, Lewis S, Greenspan H, Jégou S, Schiratti JB, Jehanno P, Taouli B. Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks. Eur Radiol 2023; 33:6020-6032. [PMID: 37071167 DOI: 10.1007/s00330-023-09613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/09/2023] [Accepted: 02/26/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVE To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions.
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Affiliation(s)
- Daniela Said
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Clínica Universidad de los Andes, Santiago, Chile
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, University Hospital Virgen de La Arrixaca, Murcia, Spain
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Naik Vietti-Violi
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xing Chin
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Myron Schwartz
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Parissa Tabrizian
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Hayit Greenspan
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.
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12
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Pomohaci MD, Grasu MC, Dumitru RL, Toma M, Lupescu IG. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13091663. [PMID: 37175054 PMCID: PMC10178485 DOI: 10.3390/diagnostics13091663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. However, due to organ shortages, this treatment can only be applied to carefully selected patients according to clinical guidelines. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis. Our objectives are to review the developing AI applications that could be implemented to better stratify liver transplant candidates. The papers analysed applied AI for liver segmentation, evaluation of steatosis, sarcopenia assessment, lesion detection, segmentation, and characterization. A liver transplant is an optimal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease. Furthermore, AI could provide solutions for improving the management of liver transplant candidates to improve survival.
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Affiliation(s)
- Mihai Dan Pomohaci
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mugur Cristian Grasu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Radu Lucian Dumitru
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mihai Toma
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Ioana Gabriela Lupescu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
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13
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McMahon B, Cohen C, Brown Jr RS, El-Serag H, Ioannou GN, Lok AS, Roberts LR, Singal AG, Block T. Opportunities to address gaps in early detection and improve outcomes of liver cancer. JNCI Cancer Spectr 2023; 7:pkad034. [PMID: 37144952 PMCID: PMC10212536 DOI: 10.1093/jncics/pkad034] [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: 02/22/2023] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
Death rates from primary liver cancer (hepatocellular carcinoma [HCC]) have continued to rise in the United States over the recent decades despite the availability of an increasing range of treatment modalities, including new systemic therapies. Prognosis is strongly associated with tumor stage at diagnosis; however, most cases of HCC are diagnosed beyond an early stage. This lack of early detection has contributed to low survival rates. Professional society guidelines recommend semiannual ultrasound-based HCC screening for at-risk populations, yet HCC surveillance continues to be underused in clinical practice. On April 28, 2022, the Hepatitis B Foundation convened a workshop to discuss the most pressing challenges and barriers to early HCC detection and the need to better leverage existing and emerging tools and technologies that could improve HCC screening and early detection. In this commentary, we summarize technical, patient-level, provider-level, and system-level challenges and opportunities to improve processes and outcomes across the HCC screening continuum. We highlight promising approaches to HCC risk stratification and screening, including new biomarkers, advanced imaging incorporating artificial intelligence, and algorithms for risk stratification. Workshop participants emphasized that action to improve early detection and reduce HCC mortality is urgently needed, noting concern that many of the challenges we face today are the same or similar to those faced a decade ago and that HCC mortality rates have not meaningfully improved. Increasing the uptake of HCC screening was identified as a short-term priority while developing and validating better screening tests and risk-appropriate surveillance strategies.
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Affiliation(s)
- Brian McMahon
- Liver Disease and Hepatitis Program, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | | | - Robert S Brown Jr
- Department of Medicine, Division of Gastroenterology and Hepatology, Weill Cornell Medicine, New York, NY, USA
| | - Hashem El-Serag
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - George N Ioannou
- Department of Medicine, Division of Gastroenterology, VA Puget Sound Health Care System, Seattle, WA, USA
| | - Anna S Lok
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Lewis R Roberts
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Amit G Singal
- Department of Internal Medicine, Division of Digestive and Liver Diseases, UT Southwestern, Dallas, TX, USA
| | - Timothy Block
- Baruch S. Blumberg Institute and Hepatitis B Foundation, Doylestown, PA, USA
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14
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Ringe KI, Yoon JH. Strategies and Techniques for Liver Magnetic Resonance Imaging: New and Pending Applications for Routine Clinical Practice. Korean J Radiol 2023; 24:180-189. [PMID: 36788770 PMCID: PMC9971842 DOI: 10.3348/kjr.2022.0838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 12/22/2022] [Indexed: 02/16/2023] Open
Affiliation(s)
- Kristina I. Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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15
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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [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: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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16
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Eschrich J, Kobus Z, Geisel D, Halskov S, Roßner F, Roderburg C, Mohr R, Tacke F. The Diagnostic Approach towards Combined Hepatocellular-Cholangiocarcinoma-State of the Art and Future Perspectives. Cancers (Basel) 2023; 15:cancers15010301. [PMID: 36612297 PMCID: PMC9818385 DOI: 10.3390/cancers15010301] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/17/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer which displays clinicopathologic features of both hepatocellular (HCC) and cholangiocellular carcinoma (CCA). The similarity to HCC and CCA makes the diagnostic workup particularly challenging. Alpha-fetoprotein (AFP) and carbohydrate antigen 19-9 (CA 19-9) are blood tumour markers related with HCC and CCA, respectively. They can be used as diagnostic markers in cHCC-CCA as well, albeit with low sensitivity. The imaging features of cHCC-CCA overlap with those of HCC and CCA, dependent on the predominant histopathological component. Using the Liver Imaging and Reporting Data System (LI-RADS), as many as half of cHCC-CCAs may be falsely categorised as HCC. This is especially relevant since the diagnosis of HCC may be made without histopathological confirmation in certain cases. Thus, in instances of diagnostic uncertainty (e.g., simultaneous radiological HCC and CCA features, elevation of CA 19-9 and AFP, HCC imaging features and elevated CA 19-9, and vice versa) multiple image-guided core needle biopsies should be performed and analysed by an experienced pathologist. Recent advances in the molecular characterisation of cHCC-CCA, innovative diagnostic approaches (e.g., liquid biopsies) and methods to analyse multiple data points (e.g., clinical, radiological, laboratory, molecular, histopathological features) in an all-encompassing way (e.g., by using artificial intelligence) might help to address some of the existing diagnostic challenges.
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Affiliation(s)
- Johannes Eschrich
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Zuzanna Kobus
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Dominik Geisel
- Department for Radiology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Sebastian Halskov
- Department for Radiology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Florian Roßner
- Department of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Roderburg
- Clinic for Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of Heinrich Heine University Düsseldorf, University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Raphael Mohr
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
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Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging. Diagn Interv Imaging 2023; 104:24-36. [PMID: 36272931 DOI: 10.1016/j.diii.2022.10.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 01/10/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and currently the third-leading cause of cancer-related death worldwide. Recently, artificial intelligence (AI) has emerged as an important tool to improve clinical management of HCC, including for diagnosis, prognostication and evaluation of treatment response. Different AI approaches, such as machine learning and deep learning, are both based on the concept of developing prediction algorithms from large amounts of data, or big data. The era of digital medicine has led to a rapidly expanding amount of routinely collected health data which can be leveraged for the development of AI models. Various studies have constructed AI models by using features extracted from ultrasound imaging, computed tomography imaging and magnetic resonance imaging. Most of these models have used convolutional neural networks. These tools have shown promising results for HCC detection, characterization of liver lesions and liver/tumor segmentation. Regarding treatment, studies have outlined a role for AI in evaluation of treatment response and improvement of pre-treatment planning. Several challenges remain to fully integrate AI models in clinical practice. Future research is still needed to robustly evaluate AI algorithms in prospective trials, and improve interpretability, generalizability and transparency. If such challenges can be overcome, AI has the potential to profoundly change the management of patients with HCC. The purpose of this review was to sum up current evidence on AI approaches using imaging for the clinical management of HCC.
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Cho Y, Han YE, Kim MJ, Park BJ, Sim KC, Sung DJ, Han NY, Park YS. Computer-aided hepatocellular carcinoma detection on the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging using a convolutional neural network: Feasibility evaluation with multi-sequence data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107032. [PMID: 35930863 DOI: 10.1016/j.cmpb.2022.107032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 05/27/2022] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Diagnosis of hepatocellular carcinoma (HCC) on liver MRI needs analysis of multi-sequence images. However, developing computer-aided detection (CAD) for every single sequence requires considerable time and labor for image segmentation. Therefore, we developed CAD for HCC on the hepatobiliary phase (HBP) of gadoxetic acid-enhanced magnetic resonance imaging (MRI) using a convolutional neural network (CNN) and evaluated its feasibility on multi-sequence, multi-unit, and multi-center data. METHODS Patients who underwent gadoxetic acid-enhanced MRI and surgery for HCC in Korea University Anam Hospital (KUAH) and Korea University Guro Hospital (KUGH) were reviewed. Finally, 170 nodules from 155 consecutive patients from KUAH and 28 nodules from 28 patients randomly selected from KUGH were included. Regions of interests were drawn on the whole HCC volume on HBP, T1-weighted (T1WI), T2-weighted (T2WI), and portal venous phase (PVP) images. The CAD was developed from the HBP images of KUAH using customized-nnUNet and post-processed for false-positive reduction. Internal and external validation of the CAD was performed with HBP, T1WI, T2WI, and PVP of KUAH and KUGH. RESULTS The figure of merit and recall of the jackknife alternative free-response receiver operating characteristic of the CAD for HBP, T1WI, T2WI, and PVP at false-positive rate 0.5 were (0.87 and 87.0), (0.73 and 73.3), (0.13 and 13.3), and (0.67 and 66.7) in KUAH and (0.86 and 86.0), (0.61 and 53.6), (0.07 and 0.07), and (0.57 and 53.6) in KUGH, respectively. CONCLUSIONS The CAD for HCC on gadoxetic acid-enhanced MRI developed by CNN from HBP detected HCCs feasibly on HBP, T1WI, and PVP of gadoxetic acid-enhanced MRI obtained from multiple units and centers. This result imply that the CAD developed using single MRI sequence may be applied to other similar sequences and this will reduce labor and time for CAD development in multi-sequence MRI.
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Affiliation(s)
- Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea; AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Yeo Eun Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Min Ju Kim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Beom Jin Park
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ki Choon Sim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Deuk Jae Sung
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Na Yeon Han
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Yang Shin Park
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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20
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Survarachakan S, Prasad PJR, Naseem R, Pérez de Frutos J, Kumar RP, Langø T, Alaya Cheikh F, Elle OJ, Lindseth F. Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions. Artif Intell Med 2022; 130:102331. [DOI: 10.1016/j.artmed.2022.102331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022]
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21
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Hänsch A, Chlebus G, Meine H, Thielke F, Kock F, Paulus T, Abolmaali N, Schenk A. Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks. Sci Rep 2022; 12:12262. [PMID: 35851322 PMCID: PMC9293996 DOI: 10.1038/s41598-022-16388-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/08/2022] [Indexed: 02/08/2023] Open
Abstract
Automatic liver tumor segmentation can facilitate the planning of liver interventions. For diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can yield a higher sensitivity than contrast-enhanced CT. However, most studies on automatic liver lesion segmentation have focused on CT. In this study, we present a deep learning-based approach for liver tumor segmentation in the late hepatocellular phase of DCE-MRI, using an anisotropic 3D U-Net architecture and a multi-model training strategy. The 3D architecture improves the segmentation performance compared to a previous study using a 2D U-Net (mean Dice 0.70 vs. 0.65). A further significant improvement is achieved by a multi-model training approach (0.74), which is close to the inter-rater agreement (0.78). A qualitative expert rating of the automatically generated contours confirms the benefit of the multi-model training strategy, with 66 % of contours rated as good or very good, compared to only 43 % when performing a single training. The lesion detection performance with a mean F1-score of 0.59 is inferior to human raters (0.76). Overall, this study shows that correctly detected liver lesions in late-phase DCE-MRI data can be automatically segmented with high accuracy, but the detection, in particular of smaller lesions, can still be improved.
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Affiliation(s)
- Annika Hänsch
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
| | - Grzegorz Chlebus
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Hans Meine
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,Medical Image Computing Group, University of Bremen, Bremen, Germany
| | - Felix Thielke
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Farina Kock
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Tobias Paulus
- Institut für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Katholisches Klinikum Bochum, Universitätsklinikum der Ruhr Universität Bochum, Bochum, Germany
| | - Nasreddin Abolmaali
- Institut für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Katholisches Klinikum Bochum, Universitätsklinikum der Ruhr Universität Bochum, Bochum, Germany
| | - Andrea Schenk
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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22
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A Fast Method for Whole Liver- and Colorectal Liver Metastasis Segmentations from MRI Using 3D FCNN Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The liver is the most frequent organ for metastasis from colorectal cancer, one of the most common tumor types with a poor prognosis. Despite reducing surgical planning time and providing better spatial representation, current methods of 3D modeling of patient-specific liver anatomy are extremely time-consuming. The purpose of this study was to develop a deep learning model trained on an in-house dataset of 84 MRI volumes to rapidly provide fully automated whole liver and liver lesions segmentation from volumetric MRI series. A cascade approach was utilized to address the problem of class imbalance. The trained model achieved an average Dice score for whole liver segmentation of 0.944 ± 0.009 and 0.780 ± 0.119 for liver lesion segmentation. Furthermore, applying this method to a not-annotated dataset creates a complete 3D segmentation in less than 6 s per MRI volume, with a mean segmentation Dice score of 0.994 ± 0.003 for the liver and 0.709 ± 0.171 for tumors compared to manual corrections applied after the inference was achieved. Availability and integration of our method in clinical practice may improve diagnosis and treatment planning in patients with colorectal liver metastasis and open new possibilities for research into liver tumors.
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23
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Río Bártulos C, Senk K, Schumacher M, Plath J, Kaiser N, Bade R, Woetzel J, Wiggermann P. Assessment of Liver Function With MRI: Where Do We Stand? Front Med (Lausanne) 2022; 9:839919. [PMID: 35463008 PMCID: PMC9018984 DOI: 10.3389/fmed.2022.839919] [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: 01/17/2022] [Accepted: 02/25/2022] [Indexed: 12/12/2022] Open
Abstract
Liver disease and hepatocellular carcinoma (HCC) have become a global health burden. For this reason, the determination of liver function plays a central role in the monitoring of patients with chronic liver disease or HCC. Furthermore, assessment of liver function is important, e.g., before surgery to prevent liver failure after hepatectomy or to monitor the course of treatment. Liver function and disease severity are usually assessed clinically based on clinical symptoms, biopsy, and blood parameters. These are rather static tests that reflect the current state of the liver without considering changes in liver function. With the development of liver-specific contrast agents for MRI, noninvasive dynamic determination of liver function based on signal intensity or using T1 relaxometry has become possible. The advantage of this imaging modality is that it provides additional information about the vascular structure, anatomy, and heterogeneous distribution of liver function. In this review, we summarized and discussed the results published in recent years on this technique. Indeed, recent data show that the T1 reduction rate seems to be the most appropriate value for determining liver function by MRI. Furthermore, attention has been paid to the development of automated tools for image analysis in order to uncover the steps necessary to obtain a complete process flow from image segmentation to image registration to image analysis. In conclusion, the published data show that liver function values obtained from contrast-enhanced MRI images correlate significantly with the global liver function parameters, making it possible to obtain both functional and anatomic information with a single modality.
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Affiliation(s)
- Carolina Río Bártulos
- Institut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
| | - Karin Senk
- Institut für Röntgendiagnostik, Universtitätsklinikum Regensburg, Regensburg, Germany
| | | | - Jan Plath
- MeVis Medical Solutions AG, Bremen, Germany
| | | | | | | | - Philipp Wiggermann
- Institut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
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24
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [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: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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25
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Jiang L, Chen D, Cao Z, Wu F, Zhu H, Zhu F. A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health 2022; 22:106. [PMID: 35365122 PMCID: PMC8973652 DOI: 10.1186/s12903-022-02119-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/14/2022] [Indexed: 12/13/2022] Open
Abstract
Background Radiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, inconsistency, and low efficiency in imaging diagnosis. Deep learning network may be a solution to improve the accuracy and efficiency of periodontitis imaging staging diagnosis. This study aims to establish a comprehensive and accurate radiological staging model of periodontal alveolar bone loss based on panoramic images. Methods A total of 640 panoramic images were included, and 3 experienced periodontal physicians marked the key points needed to calculate the degree of periodontal alveolar bone loss and the specific location and shape of the alveolar bone loss. A two-stage deep learning architecture based on UNet and YOLO-v4 was proposed to localize the tooth and key points, so that the percentage of periodontal alveolar bone loss was accurately calculated and periodontitis was staged. The ability of the model to recognize these features was evaluated and compared with that of general dental practitioners. Results The overall classification accuracy of the model was 0.77, and the performance of the model varied for different tooth positions and categories; model classification was generally more accurate than that of general practitioners. Conclusions It is feasible to establish deep learning model for assessment and staging radiographic periodontal alveolar bone loss using two-stage architecture based on UNet and YOLO-v4.
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Affiliation(s)
- Linhong Jiang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China
| | - Daqian Chen
- School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310006, China
| | - Zheng Cao
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310006, China
| | - Fuli Wu
- School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310006, China
| | - Haihua Zhu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
| | - Fudong Zhu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
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Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning. Pediatr Res 2022; 91:1168-1175. [PMID: 34091603 DOI: 10.1038/s41390-021-01560-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Differentiating acute bilirubin encephalopathy (ABE) from non-ABE in neonates with hyperbilirubinemia (HB) from routine magnetic resonance imaging (MRI) is extremely challenging since both conditions demonstrate similar T1 hyperintensities. To this end, we investigated whether the integration of multimodal MRI from routine clinical scans with deep-learning approaches could improve diagnostic performance. METHODS A total of 75 neonates with ABE and 75 neonates with HB (non-ABE) were included in the study. Each patient had three types of multimodal images taken, i.e., a T1-weighted image (T1WI), a T2-weighted image (T2WI), and an apparent diffusion coefficient (ADC) map. The three types of MRI contrasts and their combination were fed into two deep convolutional neural networks (CNNs), i.e., ResNet18 and DenseNet201. The performance of CNNs was compared with a traditional statistical method named logistic regression. RESULTS We demonstrated that diagnostic methods with the multimodal data were better than any of the single-modal data. Both CNN models outperformed the logistic regression method. The best performance was achieved by DenseNet201 with the combination of three modalities of T1WI, T2WI, and ADC, with an accuracy of 0.929 ± 0.042 and an area under the curve (AUC) of 0.991 ± 0.007. CONCLUSIONS Our study demonstrated that CNN models with multimodal MRI significantly improve the accuracy of diagnosing ABE. IMPACT We proposed an efficient strategy of detecting ABE in neonates based on multimodal MRI with deep learning, which achieved an accuracy of 0.929 ± 0.042 and an AUC of 0.991 ± 0.007. We demonstrated the advantage of integrating multimodal MRI in detecting ABE in neonates with HB, using deep-learning models. Our strategy of diagnosing ABE using deep-learning techniques with multimodal MRI from routine clinical scans is potentially applicable to clinical practice.
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27
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Elena Laino M, Viganò L, Ammirabile A, Lofino L, Generali E, Francone M, Lleo A, Saba L, Savevski V. The added value of Artificial Intelligence to LI-RADS categorization: a systematic review. Eur J Radiol 2022; 150:110251. [PMID: 35303556 DOI: 10.1016/j.ejrad.2022.110251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/05/2022] [Accepted: 03/07/2022] [Indexed: 02/07/2023]
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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29
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Gross M, Spektor M, Jaffe A, Kucukkaya AS, Iseke S, Haider SP, Strazzabosco M, Chapiro J, Onofrey JA. Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 2021; 16:e0260630. [PMID: 34852007 PMCID: PMC8635384 DOI: 10.1371/journal.pone.0260630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 11/13/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages. METHODS This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages ("All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages ("Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons. RESULTS 219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107). CONCLUSION To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.
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Affiliation(s)
- Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Charité Center for Diagnostic and Interventional Radiology, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Spektor
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Ariel Jaffe
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Ahmet S. Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Charité Center for Diagnostic and Interventional Radiology, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Iseke
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Stefan P. Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Mario Strazzabosco
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - John A. Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Urology, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America
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30
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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31
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Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27:6825-6843. [PMID: 34790009 PMCID: PMC8567471 DOI: 10.3748/wjg.v27.i40.6825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
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Affiliation(s)
- Charles E Hill
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Luca Biasiolli
- Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | | | - Vicente Grau
- Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
- Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
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32
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Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021; 11:698373. [PMID: 34616673 PMCID: PMC8488263 DOI: 10.3389/fonc.2021.698373] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively. Objective This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC. Methods A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis. Results Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients. Conclusion Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yao-Kun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xi Yu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ran Wang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Hua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing-Dong Li
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Gang Yang
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Qin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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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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Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27:5341-5350. [PMID: 34539136 PMCID: PMC8409162 DOI: 10.3748/wjg.v27.i32.5341] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/15/2021] [Accepted: 07/27/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.
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Affiliation(s)
- Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiao-Hong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wei Cai
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xia-Bi Liu
- Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Xin-Ming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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35
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Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 2021; 11:14440. [PMID: 34262088 PMCID: PMC8280152 DOI: 10.1038/s41598-021-93792-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/29/2021] [Indexed: 12/29/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57–0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.
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36
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Takenaga T, Hanaoka S, Nomura Y, Nakao T, Shibata H, Miki S, Yoshikawa T, Hayashi N, Abe O. Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI. Int J Comput Assist Radiol Surg 2021; 16:1527-1536. [PMID: 34075548 DOI: 10.1007/s11548-021-02416-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/20/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI. METHODS We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix. RESULTS Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790. CONCLUSION We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists.
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Affiliation(s)
- Tomomi Takenaga
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Hisaichi Shibata
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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Cardobi N, Dal Palù A, Pedrini F, Beleù A, Nocini R, De Robertis R, Ruzzenente A, Salvia R, Montemezzi S, D’Onofrio M. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers (Basel) 2021; 13:2162. [PMID: 33946223 PMCID: PMC8124771 DOI: 10.3390/cancers13092162] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.
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Affiliation(s)
- Nicolò Cardobi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Alessandro Dal Palù
- Department of Mathematical, Physical and Computer Sciences, University of Parma, 43121 Parma, Italy;
| | - Federica Pedrini
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Alessandro Beleù
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Riccardo Nocini
- Otolaryngology-Head and Neck Surgery Department, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy;
| | - Riccardo De Robertis
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Andrea Ruzzenente
- Department of Surgery, General and Hepatobiliary Surgery, University Hospital G.B. Rossi, University and Hospital Trust of Verona, 37126 Verona, Italy;
| | - Roberto Salvia
- Unit of General and Pancreatic Surgery, Department of Surgery and Oncology, University of Verona Hospital Trust, 37126 Verona, Italy;
| | - Stefania Montemezzi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Mirko D’Onofrio
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
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Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021; 11:diagnostics11020292. [PMID: 33673229 PMCID: PMC7918339 DOI: 10.3390/diagnostics11020292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/03/2021] [Accepted: 02/10/2021] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.
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Affiliation(s)
- Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
- Correspondence: ; Tel.: +81-3-38133111; Fax: +81-3-56845960
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoicho, Inage, Chiba 263-8522, Japan;
| | - Hiroaki Nagamatsu
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
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