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Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
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Jorg T, Halfmann MC, Stoehr F, Arnhold G, Theobald A, Mildenberger P, Müller L. A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports. Insights Imaging 2024; 15:80. [PMID: 38502298 PMCID: PMC10951179 DOI: 10.1186/s13244-024-01660-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/25/2024] [Indexed: 03/21/2024] Open
Abstract
OBJECTIVES Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. METHODS Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. RESULTS Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). CONCLUSION The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. CRITICAL RELEVANCE STATEMENT With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. KEY POINTS • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.
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Affiliation(s)
- Tobias Jorg
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany.
| | - Moritz C Halfmann
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
| | - Fabian Stoehr
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
| | - Gordon Arnhold
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
| | - Annabell Theobald
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
| | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany
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Duwe G, Müller L, Ruckes C, Fischer ND, Frey LJ, Börner JH, Rölz N, Haack M, Sparwasser P, Jorg T, Neumann CCM, Tsaur I, Höfner T, Haferkamp A, Hahn F, Mager R, Brandt MP. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma-Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines 2023; 11:2482. [PMID: 37760923 PMCID: PMC10526098 DOI: 10.3390/biomedicines11092482] [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: 07/08/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND In the treatment of advanced urothelial (aUC) and renal cell carcinoma (aRCC), biomarkers such as PD-1 and PD-L1 are not robust prognostic markers for immunotherapy (IO) response. Previously, a significant association between IO and a change in splenic volume (SV) was described for several tumour entities. To the best of our knowledge, this study presents the first correlation of SV to IO in aUC and aRCC. METHODS All patients with aUC (05/2017-10/2021) and aRCC (01/2012-05/2022) treated with IO at our academic centre were included. SV was measured at baseline, 3 and 9 months after initiation of IO using an in-house developed convolutional neural network-based spleen segmentation method. Uni- and multivariate Cox regression models for overall survival (OS) and progression-free survival (PFS) were used. RESULTS In total, 35 patients with aUC and 30 patients with aRCC were included in the analysis. Lower SV at the three-month follow-up was significantly associated with improved OS in the aRCC group. CONCLUSIONS We describe a new, innovative artificial intelligence-based approach of a radiological surrogate marker for IO response in aUC and aRCC which presents a promising new predictive imaging marker. The data presented implicate improved OS with lower follow-up SV in patients with aRCC.
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Affiliation(s)
- Gregor Duwe
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Christian Ruckes
- Interdisciplinary Center for Clinical Trials Mainz, University Medical Center, Johannes Gutenberg-University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Nikita Dhruva Fischer
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Lisa Johanna Frey
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Jan Hendrik Börner
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Niklas Rölz
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Maximilian Haack
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Peter Sparwasser
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Tobias Jorg
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Christopher C. M. Neumann
- Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, 10117 Berlin, Germany
| | - Igor Tsaur
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Thomas Höfner
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
- Department of Urology, Ordensklinikum Linz Elisabethinen, Fadingerstraße 1, 4020 Linz, Austria
| | - Axel Haferkamp
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Rene Mager
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Maximilian Peter Brandt
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
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Azuri I, Wattad A, Peri-Hanania K, Kashti T, Rosen R, Caspi Y, Istaiti M, Wattad M, Applbaum Y, Zimran A, Revel-Vilk S, C. Eldar Y. A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease. J Clin Med 2023; 12:5361. [PMID: 37629403 PMCID: PMC10455264 DOI: 10.3390/jcm12165361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.
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Affiliation(s)
- Ido Azuri
- Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ameer Wattad
- Department of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Keren Peri-Hanania
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Kashti
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ronnie Rosen
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yaron Caspi
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Majdolen Istaiti
- Gaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Makram Wattad
- Department of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
| | - Yaakov Applbaum
- Department of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University, Jerusalem 9112102, Israel
| | - Ari Zimran
- Gaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University, Jerusalem 9112102, Israel
| | - Shoshana Revel-Vilk
- Gaucher Unit, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University, Jerusalem 9112102, Israel
| | - Yonina C. Eldar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
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Müller L, Gairing SJ, Foerster F, Weinmann A, Mittler J, Stoehr F, Graafen D, Düber C, Galle PR, Kloeckner R, Hahn F. Portal hypertension in patients with hepatocellular carcinoma and immunotherapy: prognostic relevance of CT-morphologic estimates. Cancer Imaging 2023; 23:40. [PMID: 37098584 PMCID: PMC10127076 DOI: 10.1186/s40644-023-00558-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/17/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Clinically significant portal hypertension (CSPH) has been identified as an important prognostic factor in patients with hepatocellular carcinoma (HCC) undergoing curative treatment. This study aimed to assess PH estimates as prognostic factors in patients with HCC treated with immunotherapy. METHODS All patients with HCC treated with an immunotherapeutic agent in first or subsequent lines at our tertiary care center between 2016 and 2021 were included (n = 50). CSPH was diagnosed using the established PH score for non-invasive PH estimation in pre-treatment CT data (cut-off ≥ 4). Influence of PH on overall survival (OS) and progression-free survival (PFS) was assessed in uni- and multivariable analyses. RESULTS Based on the PH score, 26 patients (52.0%) were considered to have CSPH. After treatment initiation, patients with CSPH had a significantly impaired median OS (4.1 vs 33.3 months, p < 0.001) and a significantly impaired median PFS (2.7 vs 5.3 months, p = 0.02). In multivariable Cox regression, CSPH remained significantly associated with survival (HR 2.9, p = 0.015) when adjusted for established risk factors. CONCLUSIONS Non-invasive assessment of CSPH using routine CT data yielded an independent prognostic factor in patients with HCC and immunotherapy. Therefore, it might function as an additional imaging biomarker to detect high-risk patients with poor survival and possibly for treatment decision making.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany
| | - Simon J Gairing
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Friedrich Foerster
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Arndt Weinmann
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jens Mittler
- Department of General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Fabian Stoehr
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany
| | - Dirk Graafen
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany
| | - Peter R Galle
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Roman Kloeckner
- Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Luebeck, Luebeck, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany.
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Jorg T, Kämpgen B, Feiler D, Müller L, Düber C, Mildenberger P, Jungmann F. Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing. Insights Imaging 2023; 14:47. [PMID: 36929101 PMCID: PMC10019433 DOI: 10.1186/s13244-023-01392-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/15/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Structured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages. Artificial intelligence and related fields, like natural language processing (NLP), offer enormous possibilities to facilitate the imaging workflow. Here, we aimed to use the potential of NLP to combine the advantages of SR and speech recognition. RESULTS We developed a reporting tool that uses NLP to automatically convert dictated free text into a structured report. The tool comprises a task-oriented dialogue system, which assists the radiologist by sending visual feedback if relevant findings are missed. The system was developed on top of several NLP components and speech recognition. It extracts structured content from dictated free text and uses it to complete an SR template in RadLex terms, which is displayed in its user interface. The tool was evaluated for reporting of urolithiasis CTs, as a use case. It was tested using fictitious text samples about urolithiasis, and 50 original reports of CTs from patients with urolithiasis. The NLP recognition worked well for both, with an F1 score of 0.98 (precision: 0.99; recall: 0.96) for the test with fictitious samples and an F1 score of 0.90 (precision: 0.96; recall: 0.83) for the test with original reports. CONCLUSION Due to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting.
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Affiliation(s)
- Tobias Jorg
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany.
| | | | | | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Florian Jungmann
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
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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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Xiong M, Xu Y, Zhao Y, He S, Zhu Q, Wu Y, Hu X, Liu L. Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis. Front Oncol 2023; 13:990306. [PMID: 36874099 PMCID: PMC9978515 DOI: 10.3389/fonc.2023.990306] [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: 07/09/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023] Open
Abstract
Objective To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study. Methods In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles. Results 1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer. Conclusion AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.
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Affiliation(s)
- Ming Xiong
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yaona Xu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yang Zhao
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Si He
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Qihan Zhu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yi Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaofei Hu
- Department of Nuclear Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Li Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China.,Department of Ultrasound, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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Müller L, Gairing SJ, Kloeckner R, Foerster F, Weinmann A, Mittler J, Stoehr F, Emrich T, Düber C, Galle PR, Hahn F. Baseline Splenic Volume Outweighs Immuno-Modulated Size Changes with Regard to Survival Outcome in Patients with Hepatocellular Carcinoma under Immunotherapy. Cancers (Basel) 2022; 14:cancers14153574. [PMID: 35892833 PMCID: PMC9332404 DOI: 10.3390/cancers14153574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Splenic volume (SV) has been identified as a highly predictive parameter for prognosis in patients with hepatocellular carcinoma (HCC). Moreover, an association between immunotherapy and an increase in SV has been described for various types of cancer. In our cohort of patients with HCC under immunotherapy, SV was a highly predictive factor for overall survival at baseline and initial follow-up. Although a large proportion of patients (76%) showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in our patient cohort. Abstract Background: An association between immunotherapy and an increase in splenic volume (SV) has been described for various types of cancer. SV is also highly predictive of overall survival (OS) in patients with hepatocellular carcinoma (HCC). We evaluated SV and its changes with regard to their prognostic influence in patients with HCC undergoing immunotherapy. Methods: All patients with HCC who received immunotherapy in first or subsequent lines at our tertiary care center between 2016 and 2021 were screened for eligibility. SV was assessed at baseline and follow-up using an AI-based tool for spleen segmentation. Patients were dichotomized into high and low SV based on the median value. Results: Fifty patients were included in the analysis. The median SV prior to treatment was 532 mL. The median OS of patients with high and low SV was 5.1 months and 18.1 months, respectively (p = 0.01). An increase in SV between treatment initiation and the first follow-up was observed in 28/37 (75.7%) patients with follow-up imaging available. This increase in itself was not prognostic for median OS (7.0 vs. 8.5 months, p = 0.73). However, patients with high absolute SV at the first follow-up continued to have impaired survival (4.0 months vs. 30.7 months, p = 0.004). Conclusion: High SV prior to and during treatment was a significant prognostic factor for impaired outcome. Although a large proportion of patients showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in patients with HCC.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (L.M.); (F.S.); (T.E.); (C.D.)
| | - Simon Johannes Gairing
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (S.J.G.); (F.F.); (A.W.); (P.R.G.)
| | - Roman Kloeckner
- Department of Interventional Radiology, University Hospital Schleswig-Holstein–Campus Luebeck, 23562 Luebeck, Germany;
| | - Friedrich Foerster
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (S.J.G.); (F.F.); (A.W.); (P.R.G.)
| | - Arndt Weinmann
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (S.J.G.); (F.F.); (A.W.); (P.R.G.)
| | - Jens Mittler
- Department of General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany;
| | - Fabian Stoehr
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (L.M.); (F.S.); (T.E.); (C.D.)
| | - Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (L.M.); (F.S.); (T.E.); (C.D.)
- German Center for Cardiovascular Research (DZHK), Partner-Site Rhine-Main, 55131 Mainz, Germany
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (L.M.); (F.S.); (T.E.); (C.D.)
| | - Peter Robert Galle
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (S.J.G.); (F.F.); (A.W.); (P.R.G.)
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (L.M.); (F.S.); (T.E.); (C.D.)
- Correspondence:
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