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Huang Y, Yuan F, Yang L, Guo H, Jiang Y, Cun H, Mou Z, Chen J, Li C, Zhang Z, He B. Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer. Quant Imaging Med Surg 2024; 14:5737-5747. [PMID: 39144051 PMCID: PMC11320558 DOI: 10.21037/qims-24-120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/07/2024] [Indexed: 08/16/2024]
Abstract
Background Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC). Methods In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups. Results We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI. Conclusions The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.
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Affiliation(s)
- Yilong Huang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Feng Yuan
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lei Yang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Honglei Guo
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuanming Jiang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Hanxue Cun
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhanglin Mou
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiaxin Chen
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunli Li
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhenguang Zhang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bo He
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
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Müller L, Mähringer-Kunz A, Auer TA, Fehrenbach U, Gebauer B, Haubold J, Schaarschmidt BM, Kim MS, Hosch R, Nensa F, Kleesiek J, Diallo TD, Eisenblätter M, Kuzior H, Roehlen N, Bettinger D, Steinle V, Mayer P, Zopfs D, Pinto Dos Santos D, Kloeckner R. AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study. JHEP Rep 2024; 6:101125. [PMID: 39139458 PMCID: PMC11321290 DOI: 10.1016/j.jhepr.2024.101125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 08/15/2024] Open
Abstract
Background & Aims Body composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Herein, we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE). Methods This retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010-2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival and performed multivariate analysis including established estimates of survival. Results Univariate survival analysis revealed that impaired median overall survival was predicted by low SM (p <0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. This predictive role of SM was confirmed in a subgroup analysis of patients with BCLC stage B. Conclusions SM is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine. Impact and implications Body composition assessment parameters, especially skeletal muscle volume, have been identified as relevant prognostic factors for many diseases and treatments. In this study, skeletal muscle volume has been identified as an independent prognostic factor for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Therefore, skeletal muscle volume as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with artificial intelligence is essential for automated, quantitative body composition assessment, enabling broad availability in multidisciplinary case discussions.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - University Medicine Berlin, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Bernhard Gebauer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | | | - Moon-Sung Kim
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Thierno D. Diallo
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Michel Eisenblätter
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany
| | - Hanna Kuzior
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Natascha Roehlen
- Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
| | - Dominik Bettinger
- Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
| | - Verena Steinle
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - Philipp Mayer
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - David Zopfs
- Department of Radiology, University Hospital Cologne, Cologne, Germany
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - Roman Kloeckner
- Institute of Interventional Radiology, University Hospital of Schleswig-Holstein – Campus Lübeck, Lübeck, Germany
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Huang W, Feng Z, Ma M, Song F, Zeng S, Shao F, Yu X, Rong P, Chen J. Different impacts of adipose tissue dynamics on prognosis in patients with resectable locally advanced rectal cancer treated with and without neoadjuvant treatment. Front Oncol 2024; 14:1421651. [PMID: 39148902 PMCID: PMC11324464 DOI: 10.3389/fonc.2024.1421651] [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: 05/02/2024] [Accepted: 07/09/2024] [Indexed: 08/17/2024] Open
Abstract
Background Body composition is recognized to be associated with clinical outcomes in patients with locally advanced rectal cancer (LARC). This study aimed to determine the prognostic role of regional adipose tissue distribution in patients with resectable LARC treated with or without neoadjuvant chemoradiotherapy (nCRT). Methods This retrospective study included 281 consecutive patients who underwent radical surgery for LARC with or without preoperative nCRT between 2013 and 2019. Patients underwent contrast-enhanced CT scans before nCRT and before surgery. Visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT), and gluteal subcutaneous adipose tissue (gSAT) were quantified on the CT images. The association of adipose tissue distribution with progression-free survival (PFS) was analyzed using Cox proportional hazards analysis. Results A total of 102 nCRT-treated and 179 primarily resected patients were included. During a median follow-up period of 24 months, 74 (26.3%) patients experienced local recurrence or metastasis. Multivariable analysis showed that VAT was associated with PFS in all patients (hazard ratio [HR] 1.28, 95% confidence interval [CI] 1.04-1.57; P = 0.021). This association was only maintained in primarily resected patients (HR 1.31, 95% CI 1.02-1.69; P = 0.037). For patients receiving preoperative nCRT, VAT was not significantly associated with PFS, while the dynamic change in gSAT (ΔgSAT) between nCRT and surgery was associated with PFS (HR 0.43, 95%CI 0.27-0.69, P = 0.001). Conclusion Visceral obesity is an adverse prognostic factor in patients with resectable LARC treated by primary resection, while increased gluteal subcutaneous adiposity during preoperative nCRT may indicate favorable clinical outcomes.
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Affiliation(s)
- Weiyan Huang
- Department of Radiology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zhichao Feng
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Mengtian Ma
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Fulong Song
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Shumin Zeng
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Fang Shao
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jianqiang Chen
- Department of Radiology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
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Wu J, Lu Y, Dong S, Wu L, Shen X. Predicting COPD exacerbations based on quantitative CT analysis: an external validation study. Front Med (Lausanne) 2024; 11:1370917. [PMID: 38933101 PMCID: PMC11199769 DOI: 10.3389/fmed.2024.1370917] [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: 01/16/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Purpose Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Methods Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. Results A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. Conclusion The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.
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Affiliation(s)
- Ji Wu
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Yao Lu
- Department of Anesthesia, Fifth People's Hospital of Wujiang District, Suzhou, China
| | - Sunbin Dong
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Luyang Wu
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Xiping Shen
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
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Cao K, Yeung J, Arafat Y, Qiao J, Gartrell R, Master M, Yeung JMC, Baird PN. Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients. J Med Radiat Sci 2024. [PMID: 38777346 DOI: 10.1002/jmrs.798] [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: 10/31/2023] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients. METHODS A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods. RESULTS The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001). CONCLUSIONS Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.
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Affiliation(s)
- Ke Cao
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Josephine Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Yasser Arafat
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
| | - Jing Qiao
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Richard Gartrell
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Mobin Master
- Department of Radiology, Western Health, Melbourne, Victoria, Australia
| | - Justin M C Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
| | - Paul N Baird
- Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
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Koitka S, Baldini G, Kroll L, van Landeghem N, Pollok OB, Haubold J, Pelka O, Kim M, Kleesiek J, Nensa F, Hosch R. SAROS: A dataset for whole-body region and organ segmentation in CT imaging. Sci Data 2024; 11:483. [PMID: 38729970 PMCID: PMC11087485 DOI: 10.1038/s41597-024-03337-6] [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: 10/26/2023] [Accepted: 05/01/2024] [Indexed: 05/12/2024] Open
Abstract
The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.
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Affiliation(s)
- Sven Koitka
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Giulia Baldini
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Lennard Kroll
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Natalie van Landeghem
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Olivia B Pollok
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Obioma Pelka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany
| | - Moon Kim
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
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Wang F, Chen L, Bin C, Cao Y, Wang J, Zhou G, Zheng C. Drug-eluting beads transcatheter arterial chemoembolization combined with systemic therapy versus systemic therapy alone as first-line treatment for unresectable colorectal liver metastases. Front Oncol 2024; 14:1338293. [PMID: 38720801 PMCID: PMC11076665 DOI: 10.3389/fonc.2024.1338293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Purpose The purpose of this retrospective study was to compare the therapeutic efficacy and safety of drug-eluting bead transarterial chemoembolization (DEB-TACE) combined with systemic therapy to systemic therapy alone as first-line treatment for unresectable patients with colorectal liver metastases (CRLM). Methods From December 2017 to December 2022, patients with unresectable CRLM who received systemic therapy with or without DEB-TACE as first-line treatment were included in the study. The primary endpoint was progression-free survival (PFS). Secondary endpoints were tumor response, conversion rate and adverse events. Results Ninety-eight patients were enrolled in this study, including 46 patients who received systemic therapy combined with DEB-TACE (DEB-TACE group) and 52 patients who received systemic therapy alone (control group). The median PFS was elevated in the DEB-TACE group compared with the control group (12.1 months vs 8.4 months, p = 0.008). The disease control rate was increased in the DEB-TACE group compared with the control group (87.0% vs 67.3%, p = 0.022). Overall response rates (39.1% vs 25.0%; p = 0.133) and conversion rate to liver resection (33.8% vs 25.0%; p = 0.290) were no different between the two groups. The multivariate analysis showed that treatment options, size of liver metastasis, number of liver metastasis, synchronous metastases, and extrahepatic metastases were independent prognostic factor of PFS. Further subgroup analyses illustrated that PFS was beneficial with the DEB-TACE group in patients with age ≥ 60, male, left colon, synchronous metastases, bilobar, number of liver metastasis > 5, extrahepatic metastases, non-extrahepatic metastases, CEA level < 5 (ng/ml), and KRAS wild-type. No grade 4 or 5 toxicities related to DEB-TACE procedures were observed. Conclusion In patients with unresectable CRLM, systemic chemotherapy with DEB-TACE as first-line treatment may improve progression-free survival and disease control rate outcomes over systemic chemotherapy alone with manageable safety profile.
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Affiliation(s)
- Fuquan Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Lei Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Chai Bin
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Yanyan Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Jihua Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Guofeng Zhou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
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Westhölter D, Haubold J, Welsner M, Salhöfer L, Wienker J, Sutharsan S, Straßburg S, Taube C, Umutlu L, Schaarschmidt BM, Koitka S, Zensen S, Forsting M, Nensa F, Hosch R, Opitz M. Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis. Sci Rep 2024; 14:9465. [PMID: 38658613 PMCID: PMC11043331 DOI: 10.1038/s41598-024-59622-2] [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: 01/30/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024] Open
Abstract
A poor nutritional status is associated with worse pulmonary function and survival in people with cystic fibrosis (pwCF). CF transmembrane conductance regulator modulators can improve pulmonary function and body weight, but more data is needed to evaluate its effects on body composition. In this retrospective study, a pre-trained deep-learning network was used to perform a fully automated body composition analysis on chest CTs from 66 adult pwCF before and after receiving elexacaftor/tezacaftor/ivacaftor (ETI) therapy. Muscle and adipose tissues were quantified and divided by bone volume to obtain body size-adjusted ratios. After receiving ETI therapy, marked increases were observed in all adipose tissue ratios among pwCF, including the total adipose tissue ratio (+ 46.21%, p < 0.001). In contrast, only small, but statistically significant increases of the muscle ratio were measured in the overall study population (+ 1.63%, p = 0.008). Study participants who were initially categorized as underweight experienced more pronounced effects on total adipose tissue ratio (p = 0.002), while gains in muscle ratio were equally distributed across BMI categories (p = 0.832). Our findings suggest that ETI therapy primarily affects adipose tissues, not muscle tissue, in adults with CF. These effects are primarily observed among pwCF who were initially underweight. Our findings may have implications for the future nutritional management of pwCF.
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Affiliation(s)
- Dirk Westhölter
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Johannes Haubold
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Matthias Welsner
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Luca Salhöfer
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Wienker
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Sivagurunathan Sutharsan
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Svenja Straßburg
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Christian Taube
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sven Koitka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
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9
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Lee MW, Jeon SK, Paik WH, Yoon JH, Joo I, Lee JM, Lee SH. Prognostic value of initial and longitudinal changes in body composition in metastatic pancreatic cancer. J Cachexia Sarcopenia Muscle 2024; 15:735-745. [PMID: 38332658 PMCID: PMC10995276 DOI: 10.1002/jcsm.13437] [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: 05/05/2023] [Revised: 11/24/2023] [Accepted: 12/27/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Sarcopenia or visceral adipose tissue has been reported to be related to pancreatic cancer prognosis. However, clinical relevance of the comprehensive analysis of body compositions and their longitudinal changes is lacking. This study analysed the association between body composition changes after chemotherapy and survival in patients with metastatic pancreatic cancer. METHODS We retrospectively included 456 patients (mean age ± standard deviation, 61.2 ± 10.0 years; 272 males and 184 females) with metastatic pancreatic cancer who received palliative chemotherapy from May 2011 to December 2019. Using deep learning-based, fully automated segmentation of contrast-enhanced computed tomography (CT) at the time of diagnosis, cross-sectional areas of muscle, subcutaneous adipose tissue and visceral adipose tissue were extracted from a single axial image of the portal venous phase at L3 level. Skeletal muscle index (SMI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI) and mean skeletal muscle attenuation (MA) were calculated, and their effect on overall survival (OS) was analysed. Longitudinal changes in body composition and prognostic values were also analysed in a subgroup of patients with 2- and 6-month follow-up CT (n = 349). RESULTS A total of 452 deaths occurred during follow-up in the entire cohort. The survival rate was 49.3% (95% confidence interval [CI], 44.9-54.2) at 1 year and 3.7% (95% CI, 2.0-6.8) at 5 years. In multivariable analysis, higher MA (≥44.4 HU in males and ≥34.8 HU in females) at initial CT was significantly associated with better OS in both males and females (adjusted hazard ratio [HR], 0.706; 95% CI, 0.538-0.925; P = 0.012 for males, and HR, 0.656; 95% CI, 0.475-0.906; P = 0.010 for females), whereas higher SATI (≥42.8 cm2/m2 in males and ≥65.8 cm2/m2 in females) was significantly associated with better OS in female patients only (adjusted HR, 0.568; 95% CI, 0.388-0.830; P = 0.003). In longitudinal analysis, SMI, VATI and SATI significantly decreased between initial and 2-month follow-up CT, whereas mean MA significantly decreased between 2- and 6-month follow-up CT. In multivariable Cox regression analysis of longitudinal changes, which was stratified by disease control state, SATI change was significantly associated with OS in male patients (adjusted HR, 0.513; 95% CI, 0.354-0.745; P < 0.001), while other body composition parameters were not. CONCLUSIONS In patients with metastatic pancreatic cancer, body composition mostly changed during the first 2 months after starting chemotherapy, and the prognostic factors associated with OS differed between males and females. Initial and longitudinal changes of body composition are associated with OS of metastatic pancreatic cancer.
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Affiliation(s)
- Min Woo Lee
- Department of Internal Medicine and Liver Research InstituteSeoul National University Hospital, Seoul National University College of MedicineSeoulSouth Korea
- Department of Internal MedicineArmed Forces Capital HospitalSeongnamSouth Korea
| | - Sun Kyung Jeon
- Department of RadiologySeoul National University HospitalSeoulSouth Korea
| | - Woo Hyun Paik
- Department of Internal Medicine and Liver Research InstituteSeoul National University Hospital, Seoul National University College of MedicineSeoulSouth Korea
| | - Jeong Hee Yoon
- Department of RadiologySeoul National University HospitalSeoulSouth Korea
| | - Ijin Joo
- Department of RadiologySeoul National University HospitalSeoulSouth Korea
| | - Jeong Min Lee
- Department of RadiologySeoul National University HospitalSeoulSouth Korea
- Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulSouth Korea
| | - Sang Hyub Lee
- Department of Internal Medicine and Liver Research InstituteSeoul National University Hospital, Seoul National University College of MedicineSeoulSouth Korea
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10
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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. Toward Explainable Artificial Intelligence for Precision Pathology. ANNUAL REVIEW OF PATHOLOGY 2024; 19:541-570. [PMID: 37871132 DOI: 10.1146/annurev-pathmechdis-051222-113147] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.
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Affiliation(s)
- Frederick Klauschen
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Jonas Dippel
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
| | - Philipp Keyl
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Philipp Jurmeister
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Andreas Mock
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Oliver Buchstab
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Maximilian Alber
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Aignostics, Berlin, Germany
| | | | - Grégoire Montavon
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
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11
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Keyl J, Bucher A, Jungmann F, Hosch R, Ziller A, Armbruster R, Malkomes P, Reissig TM, Koitka S, Tzianopoulos I, Keyl P, Kostbade K, Albers D, Markus P, Treckmann J, Nassenstein K, Haubold J, Makowski M, Forsting M, Baba HA, Kasper S, Siveke JT, Nensa F, Schuler M, Kaissis G, Kleesiek J, Braren R. Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study. ESMO Open 2024; 9:102219. [PMID: 38194881 PMCID: PMC10837775 DOI: 10.1016/j.esmoop.2023.102219] [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: 09/03/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.
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Affiliation(s)
- J Keyl
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute of Pathology, University Hospital Essen (AöR), Essen, Germany.
| | - A Bucher
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany; German Cancer Consortium (DKTK), Frankfurt partner site, Heidelberg, Germany
| | - F Jungmann
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany
| | - A Ziller
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Armbruster
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - P Malkomes
- Department of General, Visceral and Transplant Surgery, Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - T M Reissig
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - S Koitka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany
| | - I Tzianopoulos
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - P Keyl
- Institute of Pathology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - K Kostbade
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - D Albers
- Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
| | - P Markus
- Department of General Surgery and Traumatology, Elisabeth Hospital Essen, Essen, Germany
| | - J Treckmann
- West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; Department of General, Visceral and Transplant Surgery, University Hospital Essen, Essen, Germany
| | - K Nassenstein
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J Haubold
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - M Forsting
- German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - H A Baba
- Institute of Pathology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - S Kasper
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J T Siveke
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - F Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Schuler
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; National Center for Tumor Diseases (NCT), NCT West, Essen, Germany
| | - G Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - J Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - R Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
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12
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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