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Pridgen B, von Rabenau L, Luan A, Gu AJ, Wang DS, Langlotz C, Chang J, Do B. Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning. Plast Reconstr Surg 2024; 153:1138e-1141e. [PMID: 37467052 DOI: 10.1097/prs.0000000000010928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
SUMMARY Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was used for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic curve and the associated area under the curve were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the subgroup of normal wrist radiographs and 91.3% among the subgroup of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, a specificity of 93.3%, and an accuracy of 93.4%. The area under the curve was 0.986. The authors have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.
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Affiliation(s)
- Brian Pridgen
- From the Division of Plastic Surgery, Department of Surgery
- The Buncke Clinic
| | | | - Anna Luan
- From the Division of Plastic Surgery, Department of Surgery
| | | | - David S Wang
- Department of Radiology, Stanford University School of Medicine
| | - Curtis Langlotz
- Department of Radiology, Stanford University School of Medicine
| | - James Chang
- From the Division of Plastic Surgery, Department of Surgery
| | - Bao Do
- Department of Radiology, Stanford University School of Medicine
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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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Ding H, Chen X, Wang H, Zhang L, Wang F, He L. Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on un-enhanced chest computed tomography. Transl Pediatr 2023; 12:2191-2202. [PMID: 38197102 PMCID: PMC10772833 DOI: 10.21037/tp-23-309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/02/2023] [Indexed: 01/11/2024] Open
Abstract
Background Children with primary immunodeficiency diseases (PIDs) are particularly vulnerable to infection of Mycobacterium tuberculosis (Mtb). Chest computed tomography (CT) is an important examination diagnosing pulmonary tuberculosis (PTB), and there are some differences between primary immunocompromised and immunocompetent cases with PTB. Therefore, this study aimed to use the radiomics analysis based on un-enhanced CT for identifying immunodeficiency status in children with PTB. Methods This retrospective study enrolled a total of 173 patients with diagnosis of PTB and available immunodeficiency status. Based on their immunodeficiency status, the patients were divided into PIDs (n=72) and no-PIDs (n=101). The samplings were randomly divided into training and testing groups according to a ratio of 3:1. Regions of interest were obtained by segmenting lung lesions on un-enhanced CT images to extract radiomics features. The optimal radiomics features were identified after dimensionality reduction in the training group, and a logistic regression algorithm was used to establish radiomics model. The model was validated in the training and testing groups. Diagnostic efficiency of the model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, calibration curve, and decision curve. Results The radiomics model was constructed using nine optimal features. In the training set, the model achieved an AUC of 0.837, sensitivity of 0.783, specificity of 0.780, and F1 score of 0.749. The cross-validation of the model in the training set showed an AUC of 0.774, sensitivity of 0.834, specificity of 0.720, and F1 score of 0.749. In the test set, the model achieved an AUC of 0.746, sensitivity of 0.722, specificity of 0.692, and F1 score of 0.823. Calibration curves indicated a strong predictive performance by the model, and decision curve analysis demonstrated its clinical utility. Conclusions The CT-based radiomics model demonstrates good discriminative efficacy in identifying the presence of PIDs in children with PTB, and shows promise in accurately identifying the immunodeficiency status in this population.
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Affiliation(s)
- Hao Ding
- Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Haoru Wang
- Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ling He
- Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Zhong Y, Pei Y, Nie K, Zhang Y, Xu T, Zha H. Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3690-3701. [PMID: 37566502 DOI: 10.1109/tmi.2023.3304557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.
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Chen X, Tan Z, Huo Y, Song J, Xu Q, Yang C, Jhanji V, Li J, Hou J, Zou H, Ali Khan G, Alzogool M, Wang R, Wang Y. Localized Corneal Biomechanical Alteration Detected In Early Keratoconus Based on Corneal Deformation Using Artificial Intelligence. Asia Pac J Ophthalmol (Phila) 2023; 12:574-581. [PMID: 37973045 DOI: 10.1097/apo.0000000000000644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/27/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE This study aimed to develop a novel method to diagnose early keratoconus by detecting localized corneal biomechanical changes based on dynamic deformation videos using machine learning. DESIGN Diagnostic research study. METHODS We included 917 corneal videos from the Tianjin Eye Hospital (Tianjin, China) and Shanxi Eye Hospital (Xi'an, China) from February 6, 2015, to August 25, 2022. Scheimpflug technology was used to obtain dynamic deformation videos under forced puffs of air. Fourteen new pixel-level biomechanical parameters were calculated based on a spline curve equation fitting by 115,200-pixel points from the corneal contour extracted from videos to characterize localized biomechanics. An ensemble learning model was developed, external validation was performed, and the diagnostic performance was compared with that of existing clinical diagnostic indices. The performance of the developed machine learning model was evaluated using precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS The ensemble learning model successfully diagnosed early keratoconus (area under the curve = 0.9997) with 95.73% precision, 95.61% recall, and 95.50% F1 score in the sample set (n=802). External validation on an independent dataset (n=115) achieved 91.38% precision, 92.11% recall, and 91.18% F1 score. Diagnostic accuracy was significantly better than that of existing clinical diagnostic indices (from 86.28% to 93.36%, all P <0.01). CONCLUSIONS Localized corneal biomechanical changes detected using dynamic deformation videos combined with machine learning algorithms were useful for diagnosing early keratoconus. Focusing on localized biomechanical changes may guide ophthalmologists, aiding the timely diagnosis of early keratoconus and benefiting the patient's vision.
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Affiliation(s)
- Xuan Chen
- School of Medicine, Nankai University, Tianjin, China
| | - Zuoping Tan
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Yan Huo
- School of Medicine, Nankai University, Tianjin, China
| | - Jiaxin Song
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Can Yang
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Vishal Jhanji
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Jing Li
- Shanxi Eye Hospital, Xi'an People's Hospital, Xi'an, China
| | - Jie Hou
- Jinan Mingshui Eye Hospital, Ji'nan, Shandong, China
| | - Haohan Zou
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Gauhar Ali Khan
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | | | - Riwei Wang
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Yan Wang
- School of Medicine, Nankai University, Tianjin, China
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
- Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin Eye Institute, Nankai University Affiliated Eye Hospital, Tianjin, China
- Nankai Eye Institute, Nankai University, Tianjin, China
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Cao K, Yeung J, Arafat Y, Wei MYK, Yeung JMC, Baird PN. Identification of Differences in Body Composition Measures Using 3D-Derived Artificial Intelligence from Multiple CT Scans across the L3 Vertebra Compared to a Single Mid-Point L3 CT Scan. Radiol Res Pract 2023; 2023:1047314. [PMID: 37881809 PMCID: PMC10597731 DOI: 10.1155/2023/1047314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023] Open
Abstract
Purpose Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures. Methods A total of 2203 axial CT slices of the entire L3 level (4-46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately. Results Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007). Conclusion Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.
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Affiliation(s)
- Ke Cao
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
| | - Josephine Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
| | - Yasser Arafat
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
- Department of Colorectal Surgery, Western Health, University of Melbourne, Melbourne, Australia
| | - Matthew Y. K. Wei
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
- Department of Colorectal Surgery, Western Health, University of Melbourne, Melbourne, Australia
| | - Justin M. C. Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
- Department of Colorectal Surgery, Western Health, University of Melbourne, Melbourne, Australia
| | - Paul N. Baird
- Department of Surgery, University of Melbourne, Melbourne, Australia
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Ravigopal SR, Sarma A, Brumfiel TA, Desai JP. Real-time Pose Tracking for a Continuum Guidewire Robot under Fluoroscopic Imaging. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2023; 5:230-241. [PMID: 38250652 PMCID: PMC10798677 DOI: 10.1109/tmrb.2023.3260273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Atherosclerosis is a medical condition that causes buildup of plaque in the blood vessels and narrowing of the arteries. Surgeons often treat this condition through angioplasty with catheter placements. Continuum guidewire robots offer significant advantages for catheter placements due to their dexterity. Tracking these guidewire robots and their surrounding workspace under fluoroscopy in real-time can be useful for visualization and accurate control. This paper discusses algorithms and methods to track the shape and orientation of the guidewire and the surrounding workspaces of phantom vasculatures in real-time under C-arm fluoroscopy. The shape of continuum guidewires is found through a semantic segmentation architecture based on MobileNetv2 with a Tversky loss function to deal with class imbalances. This shape is refined through medial axis filtering and parametric curve fitting to quantitatively describe the guidewire's pose. Using a constant curvature assumption for the guidewire's bending segments, the parameters that describe the joint variables are estimated in real-time for a tendon-actuated COaxially Aligned STeerable (COAST) guidewire robot and tracked through its traversal of an aortic bifurcation phantom. The accuracy of the tracking is ~90% and the execution times are within 100ms, and hence this method is deemed suitable for real-time tracking.
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Affiliation(s)
- Sharan R Ravigopal
- Medical Robotics and Automation (RoboMed) Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Achraj Sarma
- Medical Robotics and Automation (RoboMed) Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Timothy A Brumfiel
- Medical Robotics and Automation (RoboMed) Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Jaydev P Desai
- Medical Robotics and Automation (RoboMed) Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
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Les T, Markiewicz T, Dziekiewicz M, Gallego J, Swiderska-Chadaj Z, Lorent M. Localization of spleen and kidney organs from CT scans based on classification of slices in rotational views. Sci Rep 2023; 13:5709. [PMID: 37029169 PMCID: PMC10082200 DOI: 10.1038/s41598-023-32741-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 03/31/2023] [Indexed: 04/09/2023] Open
Abstract
This article presents a novel multiple organ localization and tracking technique applied to spleen and kidney regions in computed tomography images. The proposed solution is based on a unique approach to classify regions in different spatial projections (e.g., side projection) using convolutional neural networks. Our procedure merges classification results from different projection resulting in a 3D segmentation. The proposed system is able to recognize the contour of the organ with an accuracy of 88-89% depending on the body organ. Research has shown that the use of a single method can be useful for the detection of different organs: kidney and spleen. Our solution can compete with U-Net based solutions in terms of hardware requirements, as it has significantly lower demands. Additionally, it gives better results in small data sets. Another advantage of our solution is a significantly lower training time on an equally sized data set and more capabilities to parallelize calculations. The proposed system enables visualization, localization and tracking of organs and is therefore a valuable tool in medical diagnostic problems.
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Affiliation(s)
- Tomasz Les
- University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland.
| | - Tomasz Markiewicz
- University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland
- Military Institute of Medicine, Szaserów 128, 04-141, Warsaw, Poland
| | | | - Jaime Gallego
- University of Barcelona, Gran Via de les Corts Catalanes, 08007, Barcelona, Spain
| | | | - Malgorzata Lorent
- Military Institute of Medicine, Szaserów 128, 04-141, Warsaw, Poland
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Overcoming barriers to timely recognition and treatment of cancer cachexia: Sharing Progress in Cancer Care Task Force Position Paper and Call to Action. Crit Rev Oncol Hematol 2023; 185:103965. [PMID: 36931616 DOI: 10.1016/j.critrevonc.2023.103965] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Cachexia is a life-threatening disorder affecting an estimated 50-80% of cancer patients. The loss of skeletal muscle mass in patients with cachexia is associated with an increased risk of anticancer treatment toxicity, surgical complications and reduced response. Despite international guidelines, the identification and management of cancer cachexia remains a significant unmet need owing in part to the lack of routine screening for malnutrition and suboptimal integration of nutrition and metabolic care into clinical oncology practice. In June 2020, Sharing Progress in Cancer Care (SPCC) convened a multidisciplinary task force of medical experts and patient advocates to examine the barriers preventing the timely recognition of cancer cachexia, and provide practical recommendations to improve clinical care. This position paper summarises the key points and highlights available resources to support the integration of structured nutrition care pathways.
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [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: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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Santhanam P, Nath T, Peng C, Bai H, Zhang H, Ahima RS, Chellappa R. Artificial intelligence and body composition. Diabetes Metab Syndr 2023; 17:102732. [PMID: 36867973 DOI: 10.1016/j.dsx.2023.102732] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
AIMS Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. METHODS We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. RESULTS AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. CONCLUSIONS AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.
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Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - Tanmay Nath
- Department Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Cheng Peng
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Harrison Bai
- Department of Radiology and Radiology Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Helen Zhang
- The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Rama Chellappa
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
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Shen H, He P, Ren Y, Huang Z, Li S, Wang G, Cong M, Luo D, Shao D, Lee EYP, Cui R, Huo L, Qin J, Liu J, Hu Z, Liu Z, Zhang N. A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment. Quant Imaging Med Surg 2023; 13:1384-1398. [PMID: 36915346 PMCID: PMC10006126 DOI: 10.21037/qims-22-330] [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: 04/06/2022] [Accepted: 11/27/2022] [Indexed: 02/12/2023]
Abstract
Background Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.
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Affiliation(s)
- Hao Shen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Pin He
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ya Ren
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zhengyong Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shuluan Li
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Guoshuai Wang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Minghua Cong
- Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Dan Shao
- Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Elaine Yuen-Phin Lee
- Department of Diagnostic Radiology, Clinical School of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Ruixue Cui
- Nuclear Medicine Department, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Li Huo
- Nuclear Medicine Department, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Qin
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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13
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Leng H, Deng R, Asad Z, Womick RM, Yang H, Wan L, Huo Y. An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12471:124710Q. [PMID: 38606193 PMCID: PMC11008744 DOI: 10.1117/12.2653651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less computational complexity. However, the patch-wise segmentation paradigm still applies to Omni-Seg, and the pipeline is time-consuming when providing segmentation for Whole Slide Images (WSIs). In this paper, we propose an enhanced version of the Omni-Seg pipeline in order to reduce the repetitive computing processes and utilize a GPU to accelerate the model's prediction for both better model performance and faster speed. Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time. The proposed accelerated implementation reduced the average processing time (at the testing stage) on a standard needle biopsy WSI from 2.3 hours to 22 minutes, using 35 WSIs from the Kidney Tissue Atlas (KPMP) Datasets. The source code and the Docker have been made publicly available at https://github.com/ddrrnn123/Omni-Seg.
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Affiliation(s)
- Haoju Leng
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235 USA
| | - Ruining Deng
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235 USA
| | - Zuhayr Asad
- College of Arts and Science, Vanderbilt University, Nashville, TN, 37235 USA
| | - R. Michael Womick
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Haichun Yang
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN 37215, USA
| | - Lipeng Wan
- Department of Computer Science, Georgia State University, Atlanta, GA, 30302 USA
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, 37235 USA
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14
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Yao N, Li X, Wang L, Cheng X, Yu A, Li C, Wu K. Deep learning for automatic segmentation of paraspinal muscle on computed tomography. Acta Radiol 2023; 64:596-604. [PMID: 35354336 DOI: 10.1177/02841851221090594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Muscle quantification is an essential step in sarcopenia evaluation. PURPOSE To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on either abdominal or lumbar (L) computed tomography (CT) scans. MATERIAL AND METHODS A novel deep neural network algorithm for automated segmentation of paraspinous muscle was developed, CT scans of 504 consecutive patients conducted between January 2019 and February 2020 were assembled. The muscle was manually segmented at L3 vertebra level by three radiologists as ground truth, divided into training and testing subgroups. Muscle cross-sectional area (CSA) was recorded. Dice similarity coefficients (DSCs) and CSA errors were calculated to evaluate system performance. The degree of muscle fat infiltration (MFI) recording by percentage value was the fat area within the region of interest divided by the muscle area. An analysis of the factors influencing the performance of the V-net-based segmentation system was also implemented. RESULTS The mean DSCs for paraspinous muscles were high for both the training (0.963, 0.970, 0.941, and 0.968, respectively) and testing (0.950, 0.960, 0.929, and 0.961, respectively) datasets, while the CSA errors were low for both training (1.9%, 1.6%, 3.1%, and 1.3%, respectively) and testing (3.4%, 3.0%, 4.6%, and 1.9%, respectively) datasets. MFI and muscle area index (MI) were major factors affecting DSCs of the posterior paraspinous and paraspinous muscle groups. CONCLUSION The ML algorithm for the measurement of paraspinous muscles was compared favorably to manual ground truth measurements.
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Affiliation(s)
- Ning Yao
- Department of Radiology, 66526Beijing Jishuitan Hospital, Beijing, PR China
| | - Xintong Li
- Department of Radiology, 66526Beijing Jishuitan Hospital, Beijing, PR China
| | - Ling Wang
- Department of Radiology, 66526Beijing Jishuitan Hospital, Beijing, PR China
| | - Xiaoguang Cheng
- Department of Radiology, 66526Beijing Jishuitan Hospital, Beijing, PR China
| | - Aihong Yu
- Department of Radiology, 159333Beijing Anding Hospital, Capital Medical University, Beijing, PR China
| | - Chenwei Li
- Healthcare Software Business, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, PR China
| | - Ke Wu
- Cri-center Research institute, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, PR China
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15
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Bonaldi L, Pretto A, Pirri C, Uccheddu F, Fontanella CG, Stecco C. Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering (Basel) 2023; 10:bioengineering10020137. [PMID: 36829631 PMCID: PMC9952222 DOI: 10.3390/bioengineering10020137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.
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Affiliation(s)
- Lorenza Bonaldi
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
| | - Andrea Pretto
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
| | - Carmelo Pirri
- Department of Neuroscience, University of Padova, Via A. Gabelli 65, 35121 Padova, Italy
| | - Francesca Uccheddu
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
| | - Chiara Giulia Fontanella
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
- Correspondence: ; Tel.: +39-049-8276754
| | - Carla Stecco
- Department of Neuroscience, University of Padova, Via A. Gabelli 65, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
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16
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Zeng P, Qu C, Liu J, Cui J, Liu X, Xiu D, Yuan H. Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Acta Radiol 2022:2841851221142552. [DOI: 10.1177/02841851221142552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation. Purpose To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC. Material and Methods In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values. Results Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively). Conclusion Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.
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Affiliation(s)
- Piaoe Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Chao Qu
- Department of General Surgery, Peking University Third Hospital, Beijing, PR China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, PR China
| | - Xiaoming Liu
- Department of Research and Development, Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, PR China
| | - Dianrong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing, PR China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
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17
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Pu L, Ashraf SF, Gezer NS, Ocak I, Dresser DE, Leader JK, Dhupar R. Estimating 3-D whole-body composition from a chest CT scan. Med Phys 2022; 49:7108-7117. [PMID: 35737963 PMCID: PMC10084085 DOI: 10.1002/mp.15821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 06/06/2022] [Accepted: 06/07/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. METHODS A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R2 ) were used as the performance metrics to assess the model performance. RESULTS The R2 values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. CONCLUSION Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.
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Affiliation(s)
- Lucy Pu
- Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,North Allegheny Senior High School, Wexford, Pennsylvania, USA
| | - Syed F Ashraf
- Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Naciye S Gezer
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Iclal Ocak
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Daniel E Dresser
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Joseph K Leader
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Rajeev Dhupar
- Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Surgical Services Division, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
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18
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DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs. BMC Bioinformatics 2022; 23:426. [PMID: 36241969 PMCID: PMC9569056 DOI: 10.1186/s12859-022-04935-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. RESULT Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3-11 (children), 12-18 (teens), 19-25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. CONCLUSIONS We introduced a novel deep learning approach DENSEN to estimate a subject's age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.
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19
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Yang Y. Artificial intelligence-based organizational human resource management and operation system. Front Psychol 2022; 13:962291. [PMID: 35936267 PMCID: PMC9355249 DOI: 10.3389/fpsyg.2022.962291] [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: 06/06/2022] [Accepted: 07/05/2022] [Indexed: 12/05/2022] Open
Abstract
The trend of globalization, marketization, and informatization continues to strengthen, in today’s development environment, how to seize the opportunity and obtain a competitive advantage in human resources is an important issue that needs to be explored. Human resource management refers to the effective use of relevant human resources inside and outside the organization through management forms under the guidance of economics and humanistic thinking. It is a general term for a series of activities that ensure the achievement of organizational goals and the maximization of member development. With the rapid development of society and economy, the competition between enterprises has intensified. If an enterprise wants to adapt to social development, it is necessary to strengthen the internal management of the organization. The internal management also needs to rely on human resource management. The purpose of this paper is to study an organization’s human resource management and operation system based on artificial intelligence. It expects to use artificial intelligence technology to design the human resource management system and to improve the quality of employees to make the enterprise develop toward a more scientific and reasonable method. It uses artificial intelligence technology to mine the relevant data of enterprises, understand the situation of enterprises in a timely manner, and adjust unreasonable rules. This paper establishes a dynamic capability evaluation model and an early warning model for human resource management and further studies the improvement approach based on human resource management. This paper analyzes the application, feasibility, and practical significance of data mining technology in human resource management systems. It focuses on the commonly used algorithms in the field of data mining and proposes specific algorithm application scenarios and implementation ideas combined with the needs of human resource management practices. The experimental results of this paper show that the average working life of incumbent employees is 3.5 years, the average length of employees who leave the company is 5 years, and some employees are 5–6 years old. From this data, it can be seen that the average number of years of on-the-job employees is short, and the work experience has yet to be accumulated.
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20
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Alavi DH, Sakinis T, Henriksen HB, Beichmann B, Fløtten A, Blomhoff R, Lauritzen PM. Body composition assessment by artificial intelligence from routine computed tomography scans in colorectal cancer: Introducing BodySegAI. JCSM CLINICAL REPORTS 2022. [DOI: 10.1002/crt2.53] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Dena Helene Alavi
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Tomas Sakinis
- Medical Division, Radiology and Nuclear Medicine, Neuroimaging Research Group Oslo University Hospital Oslo Norway
| | - Hege Berg Henriksen
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Benedicte Beichmann
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Ann‐Monica Fløtten
- Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway
| | - Rune Blomhoff
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
- Department of Clinical Service, Division of Cancer Medicine Oslo University Hospital Oslo Norway
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21
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Lee SB, Cho YJ, Yoon SH, Lee YY, Kim SH, Lee S, Choi YH, Cheon JE. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network. Eur Radiol 2022; 32:8463-8472. [PMID: 35524785 DOI: 10.1007/s00330-022-08829-w] [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: 12/20/2021] [Revised: 03/21/2022] [Accepted: 04/20/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children. METHODS For model development, we utilized transfer learning with a pre-trained model based on adult patients. We used CT images of 31 pediatric patients under 19 years of age (mean age, 9.6 years) who underwent PET-CT from institution #1 for transfer learning. Two radiologists manually labeled the skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs, and central nervous system in each CT slice and used these as references. For external validation, we collected 14 pediatric PET/CT scans from institution #2 (mean age, 9.1 years). The Dice similarity coefficients (DSCs), sensitivities, and precision were compared between the algorithms before and after transfer learning. In addition, we evaluated segmentation performance according to sex, age (≤ 8 vs. > 8 years), and body mass index (BMI, ≤ 20 vs. > 20 kg/m2). RESULTS The algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p < 0.001). The average DSC, sensitivity, and precision of each algorithm before and after transfer learning were 98.23% and 99.28%, 98.16% and 99.28%, and 98.29% and 99.28%, respectively. The segmentation performance of the algorithm was generally not affected by age, sex, or BMI, except for precision in the body muscle compartment. CONCLUSION The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children. KEY POINTS • We utilized transfer learning with a pre-trained segmentation algorithm for adult to develop an algorithm for automated segmentation of pediatric whole-body CT. • This algorithm showed excellent performance and was not affected by sex, age, or body mass index, except for precision in body muscle.
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Affiliation(s)
- Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Yun Young Lee
- Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju, 61469, Republic of Korea
| | - Soo-Hyun Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
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22
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Marcus C, Tajmir SH, Rowe SP, Sheikhbahaei S, Solnes LB. 18F-FDG PET/CT for Response Assessment in Lung Cancer. Semin Nucl Med 2022; 52:662-672. [DOI: 10.1053/j.semnuclmed.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
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23
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Bedrikovetski S, Seow W, Kroon HM, Traeger L, Moore JW, Sammour T. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis. Eur J Radiol 2022; 149:110218. [DOI: 10.1016/j.ejrad.2022.110218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/30/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
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24
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The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer. DISEASE MARKERS 2022; 2022:1819841. [PMID: 35392497 PMCID: PMC8983171 DOI: 10.1155/2022/1819841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 02/15/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022]
Abstract
Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.
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Roblot V, Giret Y, Mezghani S, Auclin E, Arnoux A, Oudard S, Duron L, Fournier L. Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma. Eur Radiol 2022; 32:4728-4737. [PMID: 35304638 DOI: 10.1007/s00330-022-08579-9] [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: 08/22/2021] [Revised: 11/23/2021] [Accepted: 12/24/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations. METHODS A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87). RESULTS Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033). CONCLUSION A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS. KEY POINTS • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients.
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Affiliation(s)
- Victoire Roblot
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France.
| | | | - Sarah Mezghani
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France
| | - Edouard Auclin
- Department of Medical Oncology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France
| | - Armelle Arnoux
- Informatics and Clinical Research Unit, Department of Biostatistics, Hôpital européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France
| | - Stéphane Oudard
- Department of Medical Oncology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France
| | - Loïc Duron
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France
- Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France
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26
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Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment. Clin Radiol 2022; 77:e363-e371. [DOI: 10.1016/j.crad.2022.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 01/07/2022] [Indexed: 11/22/2022]
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27
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McSweeney DM, Henderson EG, van Herk M, Weaver J, Bromiley PA, Green A, McWilliam A. Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks. Med Phys 2022; 49:3107-3120. [PMID: 35170063 PMCID: PMC9313817 DOI: 10.1002/mp.15533] [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: 08/19/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto‐segmentation models. Purpose There is a need to define methodologies for applying models to different domains (e.g., anatomical regions or imaging modalities) without dramatically increasing data annotation. Methods To address this problem, we empirically evaluate the generalizability of various source tasks for transfer learning: natural image classification, natural image segmentation, unsupervised image reconstruction, and self‐supervised jigsaw solving. Axial CT slices at L3 were extracted from PET‐CT scans for 204 oesophago‐gastric cancer patients and the skeletal muscle manually delineated by an expert. Features were transferred and segmentation models trained on subsets (n=5,10,25,50,75,100,125) of the manually annotated training set. Four‐fold cross‐validation was performed to evaluate model generalizability. Human‐level performance was established by performing an inter‐observer study consisting of ten trained radiographers. Results We find that accurate segmentation models can be trained on a fraction of the data required by current approaches. The Dice similarity coefficient and root mean square distance‐to‐agreement were calculated for each prediction and used to assess model performance. Models pre‐trained on a segmentation task and fine‐tuned on 10 images produce delineations that are comparable to those from trained observers and extract reliable measures of muscle health. Conclusions Appropriate transfer learning can generate convolutional neural networks for abdominal muscle segmentation that achieve human‐level performance while decreasing the required data by an order of magnitude, compared to previous methods (n=160→10). This work enables the development of future models for assessing skeletal muscle at other anatomical sites where large annotated data sets are scarce and clinical needs are yet to be addressed.
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Affiliation(s)
- Dónal M McSweeney
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK.,Radiotherapy Related Research, The Christie Foundation Trust, Manchester, M20 4BX, UK
| | - Edward G Henderson
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK.,Radiotherapy Related Research, The Christie Foundation Trust, Manchester, M20 4BX, UK
| | - Marcel van Herk
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK.,Radiotherapy Related Research, The Christie Foundation Trust, Manchester, M20 4BX, UK
| | - Jamie Weaver
- Department of Medical Oncology, The Christie Hospital NHS Foundation Trust, Manchester, M20 4BX, UK
| | - Paul A Bromiley
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PL, UK
| | - Andrew Green
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK.,Radiotherapy Related Research, The Christie Foundation Trust, Manchester, M20 4BX, UK
| | - Alan McWilliam
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK.,Radiotherapy Related Research, The Christie Foundation Trust, Manchester, M20 4BX, UK
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28
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Bland KA, Kouw IWK, van Loon LJC, Zopf EM, Fairman CM. Exercise-Based Interventions to Counteract Skeletal Muscle Mass Loss in People with Cancer: Can We Overcome the Odds? Sports Med 2022; 52:1009-1027. [PMID: 35118634 DOI: 10.1007/s40279-021-01638-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/27/2021] [Indexed: 12/15/2022]
Abstract
Addressing skeletal muscle mass loss is an important focus in oncology research to improve clinical outcomes, including cancer treatment tolerability and survival. Exercise is likely a necessary component of muscle-mass-preserving interventions for people with cancer. However, randomized controlled trials with exercise that include people with cancer with increased susceptibility to more rapid and severe muscle mass loss are limited. The aim of the current review is to highlight features of cancer-related skeletal muscle mass loss, discuss the impact in patients most at risk, and describe the possible role of exercise as a management strategy. We present current gaps within the exercise oncology literature and offer several recommendations for future studies to support research translation, including (1) utilizing accurate and reliable body composition techniques to assess changes in skeletal muscle mass, (2) incorporating comprehensive assessments of patient health status to allow personalized exercise prescription, (3) coupling exercise with robust nutritional recommendations to maximize the impact on skeletal muscle outcomes, and (4) considering key exercise intervention features that may improve exercise efficacy and adherence. Ultimately, the driving forces behind skeletal muscle mass loss are complex and may impede exercise tolerability and efficacy. Our recommendations are intended to foster the design of high-quality patient-centred research studies to determine whether exercise can counteract muscle mass loss in people with cancer and, as such, improve knowledge on this topic.
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Affiliation(s)
- Kelcey A Bland
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia.,The Szalmuk Family Department of Medical Oncology, Cabrini Cancer Institute, Cabrini Health, Melbourne, VIC, Australia
| | - Imre W K Kouw
- Intensive Care Unit, Royal Adelaide Hospital, Adelaide, SA, Australia.,Centre of Research Excellence in Translating Nutritional Science To Good Health, The University of Adelaide, Adelaide, SA, Australia.,Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Luc J C van Loon
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia.,Department of Human Biology, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Eva M Zopf
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia.,The Szalmuk Family Department of Medical Oncology, Cabrini Cancer Institute, Cabrini Health, Melbourne, VIC, Australia
| | - Ciaran M Fairman
- Exercise Science Department, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, PHRC 220, Columbia, SC, 29208, USA.
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29
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Aiello M, Baldi D, Esposito G, Valentino M, Randon M, Salvatore M, Cavaliere C. Evaluation of AI-Based Segmentation Tools for COVID-19 Lung Lesions on Conventional and Ultra-low Dose CT Scans. Dose Response 2022; 20:15593258221082896. [PMID: 35422680 PMCID: PMC9002358 DOI: 10.1177/15593258221082896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/04/2022] [Indexed: 11/16/2022] Open
Abstract
A reliable diagnosis and accurate monitoring are pivotal steps for treatment and prevention of COVID-19. Chest computed tomography (CT) has been considered a crucial diagnostic imaging technique for the injury assessment of the viral pneumonia. Furthermore, the automatization of the segmentation methods for lung alterations helps to speed up the diagnosis and lighten radiologists' workload. Considering the assiduous pathology monitoring, ultra-low dose (ULD) chest CT protocols have been implemented to drastically reduce the radiation burden. Unfortunately, the available AI technologies have not been trained on ULD-CT data and validated and their applicability deserves careful evaluation. Therefore, this work aims to compare the results of available AI tools (BCUnet, CORADS AI, NVIDIA CLARA Train SDK and CT Pneumonia Analysis) on a dataset of 73 CT examinations acquired both with conventional dose (CD) and ULD protocols. COVID-19 volume percentage, resulting from each tool, was statistically compared. This study demonstrated high comparability of the results on CD-CT and ULD-CT data among the four AI tools, with high correlation between the results obtained on both protocols (R > .68, P < .001, for all AI tools).
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Affiliation(s)
| | | | | | - Marika Valentino
- Istituto di Scienze Applicate e
Sistemi Intelligenti “Eduardo Caianiello” (ISASI-CNR), Pozzuoli, Italy
- Università Degli Studi di Napoli
Federico II, Dip. di Ingegneria Elettrica e Delle Tecnologie
Dell'Informazione, Italy
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30
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Autonomous localization and segmentation for body composition quantization on abdominal CT. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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31
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Ha J, Park T, Kim HK, Shin Y, Ko Y, Kim DW, Sung YS, Lee J, Ham SJ, Khang S, Jeong H, Koo K, Lee J, Kim KW. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep 2021; 11:21656. [PMID: 34737340 PMCID: PMC8568923 DOI: 10.1038/s41598-021-00161-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/22/2021] [Indexed: 12/15/2022] Open
Abstract
As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38-3.10 cm2. A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.
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Affiliation(s)
- Jiyeon Ha
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Taeyong Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Hong-Kyu Kim
- Health Screening and Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Youngbin Shin
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Yousun Ko
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Dong Wook Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Yu Sub Sung
- Clinical Research Center, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea
| | - Jiwoo Lee
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Su Jung Ham
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Seungwoo Khang
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Heeryeol Jeong
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Kyoyeong Koo
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Jeongjin Lee
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea.
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32
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Dreizin D, Rosales R, Li G, Syed H, Chen R. Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort. Can Assoc Radiol J 2021; 72:854-861. [PMID: 32910695 PMCID: PMC8011455 DOI: 10.1177/0846537120952508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
METHODS This work is a retrospective secondary analysis of a single institution cohort used in the development of the Baltimore CT prediction model. The cohort includes 115 consecutive patients that underwent admission contrast-enhanced CT of the abdomen and pelvis for blunt trauma with pelvic ring disruption followed by conventional angiography. Major arterial injury requiring angioembolization served as the outcome variable. Angioembolization was required in 73/115 patients (63% of the cohort). Average age was 46.9 years (±SD 20.4). Body composition measurements were determined as 2-dimensional (2D) or 3-dimensional (3D) parameters and included mid-L3 trabecular bone attenuation, abdominal visceral fat area or volume, and percent muscle fat fraction (as a marker of sarcopenia) measured using segmentation and histogram analysis. RESULTS Models incorporating 2D (Model B) or 3D markers (model C) of body composition showed improvement over the original Baltimore model (model A) in all parameters of performance, quality, and fit (area under the receiver-operating curve [AUC], Akaike information criterion, Brier score, Hosmer-Lemeshow test, and adjusted-R2). Area under the receiver-operating curve increased from 0.83 (A), to 0.86 (B), and 0.88 (C). The greatest improvement was seen with 3D parameters. CONCLUSION Once automated, quantitative visualization tools providing "free" 3D body composition information can be expected to improve personalized precision diagnostics, outcome prediction, and decision support in patients with bleeding pelvic fractures.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Remberto Rosales
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guang Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hassan Syed
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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33
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Nishiyama D, Iwasaki H, Taniguchi T, Fukui D, Yamanaka M, Harada T, Yamada H. Deep generative models for automated muscle segmentation in computed tomography scanning. PLoS One 2021; 16:e0257371. [PMID: 34506602 PMCID: PMC8432798 DOI: 10.1371/journal.pone.0257371] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/28/2021] [Indexed: 11/18/2022] Open
Abstract
Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.
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Affiliation(s)
- Daisuke Nishiyama
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
- * E-mail:
| | - Hiroshi Iwasaki
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
| | - Takaya Taniguchi
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
| | - Daisuke Fukui
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
| | - Manabu Yamanaka
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
| | - Teiji Harada
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
| | - Hiroshi Yamada
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan
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34
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Jullien M, Tessoulin B, Ghesquières H, Oberic L, Morschhauser F, Tilly H, Ribrag V, Lamy T, Thieblemont C, Villemagne B, Gressin R, Bouabdallah K, Haioun C, Damaj G, Fornecker LM, Schiano De Colella JM, Feugier P, Hermine O, Cartron G, Bonnet C, André M, Bailly C, Casasnovas RO, Le Gouill S. Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years. Cancers (Basel) 2021; 13:cancers13184503. [PMID: 34572728 PMCID: PMC8466314 DOI: 10.3390/cancers13184503] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Cachexia is a major cause of mortality in cancer patients and is characterized by a continuous skeletal muscle loss. Muscle depletion assessed by computed tomography (CT) is a predictive marker in solid tumors but has never been assessed in non-Hodgkin’s lymphoma. Despite software improvements, its measurement remains highly time-consuming and cannot be performed in clinical practice. We report the development of a CT segmentation algorithm based on convolutional neural networks. It automates the extraction of anthropometric data from pretherapeutic CT to assess precise body composition of young diffuse large B cell lymphoma (DLBCL) patients at the time of diagnosis. In this population, muscle hypodensity appears to be an independent risk factor for mortality, and can be estimated at diagnosis with this new tool. Abstract Background. Muscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin’s lymphomas. Despite software improvements, MD measurement remains highly time-consuming and cannot be used in clinical practice. Methods. This study reports the development of a Deep-Learning automatic segmentation algorithm (DLASA) to measure MD, and investigate its predictive value in a cohort of 656 diffuse large B cell lymphoma (DLBCL) patients included in the GAINED phase III prospective trial (NCT01659099). Results. After training on a series of 190 patients, the DLASA achieved a Dice coefficient of 0.97 ± 0.03. In the cohort, the median skeletal muscle index was 50.2 cm2/m2 and median muscle attenuation (MA) was 36.1 Hounsfield units (HU). No impact of sarcopenia was found on either progression free survival (PFS) or overall survival (OS). Muscular hypodensity, defined as MA below the tenth percentile according to sex, was associated with a lower OS and PFS, respectively (HR = 2.80 (95% CI 1.58–4.95), p < 0.001, and HR = 2.22 (95% CI 1.43–3.45), p < 0.001). Muscular hypodensity appears to be an independent risk factor for mortality in DLBCL and because of DLASA can be estimated in routine practice.
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Affiliation(s)
- Maxime Jullien
- Department of Hematology, Nantes University Hospital, INSERM CRCINA Nantes-Angers, NeXT Université de Nantes, 44000 Nantes, France; (M.J.); (B.T.)
| | - Benoit Tessoulin
- Department of Hematology, Nantes University Hospital, INSERM CRCINA Nantes-Angers, NeXT Université de Nantes, 44000 Nantes, France; (M.J.); (B.T.)
| | - Hervé Ghesquières
- Department of Hematology, Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, Claude Bernard Lyon-1 University, 69310 Pierre Bénite, France;
| | - Lucie Oberic
- Department of Hematology, IUC Toulouse Oncopole, 31000 Toulouse, France;
| | - Franck Morschhauser
- Department of Hematology, Univ. Lille, CHU Lille, EA 7365-GRITA-Groupe de Recherche sur les Formes Injectables et les Technologies Associées, 59000 Lille, France;
| | - Hervé Tilly
- Department of Hematology, Centre H. Becquerel, 76000 Rouen, France;
| | - Vincent Ribrag
- Department of Hematology, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
| | - Thierry Lamy
- Department of Hematology, University Hospital of Rennes, 35000 Rennes, France;
| | - Catherine Thieblemont
- Department of Hematology, APHP, Hopital Saint Louis, Université Paris Diderot, 75011 Paris, France;
| | - Bruno Villemagne
- Department of Hematology, Hopital Departemental de Vendée, 85000 La Roche sur Yon, France;
| | - Rémy Gressin
- Department of Hematology, CHU Grenoble, 38000 Grenoble, France;
| | - Kamal Bouabdallah
- Department of Hematology, University Hospital of Bordeaux, F-33000 Bordeaux, France;
| | - Corinne Haioun
- Lymphoïd Malignancies Unit, Hôpital Henri Mondor, AP-HP, 94000 Créteil, France;
| | - Gandhi Damaj
- Department of Hematology, Institut D’hématologie de Basse Normandie, 14000 Caen, France;
| | - Luc-Matthieu Fornecker
- Department of Hematology, Institut de Cancérologie Strasbourg Europe (ICANS), University Hospital of Strasbourg, 67000 Strasbourg, France;
| | | | - Pierre Feugier
- Department of Hematology, University Hospital of Nancy, 54000 Nancy, France;
| | - Olivier Hermine
- Department of Hematology, Hopital Necker, F-75015 Paris, France;
| | - Guillaume Cartron
- Department of Clinical Hematology, University Hospital of Montpellier, UMR-CNRS 5535, 34000 Montpellier, France;
| | - Christophe Bonnet
- Department of Hematology, CHU Liege, Liege University, 4000 Liege, Belgium;
| | - Marc André
- Department of Hematology, CHU UCL Namur, Université Catholique de Louvain, 5000 Namur, Belgium;
| | - Clément Bailly
- Department of Nuclear Medicine, University Hospital of Nantes, 44000 Nantes, France;
| | - René-Olivier Casasnovas
- Department of Hematology, University Hospital F. Mitterrand and Inserm UMR 1231, 21000 Dijon, France;
| | - Steven Le Gouill
- Department of Hematology, Nantes University Hospital, INSERM CRCINA Nantes-Angers, NeXT Université de Nantes, 44000 Nantes, France; (M.J.); (B.T.)
- Correspondence: ; Tel.: +33-(0)1-44-32-41-00
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Han J, Tang M, Lu C, Shen L, She J, Wu G. Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with cachexia. Clin Nutr 2021; 40:5156-5161. [PMID: 34461589 DOI: 10.1016/j.clnu.2021.08.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND & AIMS Adipose tissue loss is one of the features in patients with cancer cachexia. However, whether subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) contribute differently to the progress of cancer cachexia in gastric cancer patients with cachexia remains unclear. This study aim to investigate the effect of SAT and VAT in gastric cancer patients with cachexia. METHODS Gastric cancer patients who underwent surgery were divided into cancer cachexia group and non-cachexia group. A new deep learning system was developed to segment SAT and VAT from the computed tomography images at the third lumbar vertebra. Indexes of SAT (SATI) and VAT (VATI) were compared between cachexia and non-cachexia groups. The prognostic values of SATI and VATI for patients with gastric cancer cachexia were analyzed by Kaplan-Meier method and Cox regression. RESULTS A total of 1627 gastric cancer patients (411 cachexia and 1216 non-cachexia) were included in this study. A new V-Net-Based segmentation deep learning system was developed to quickly (0.02 s/image) and accurately segment SAT (dice scores = 0.96) and VAT (dice scores = 0.98). The SATI of gastric cancer patients with cachexia were significantly lower than non-cachexia patients (44.91 ± 0.90 vs. 50.92 ± 0.71 cm2/m2, P < 0.001), whereas no significant difference was detected in VATI (35.98 ± 0.84 VS. 37.90 ± 0.45 cm2/m2, P = 0.076). Cachexia patients with low SATI showed poor survival than those with high SATI (HR = 1.35; 95% CI = 1.06-1.74). In contrast, VATI did not show close correlation with survival in patients with cachexia (HR = 1.18; 95% CI = 0.92-1.51). CONCLUSION SAT and VAT showed different effects on gastric cancer patients with cachexia. More attention should be paid to the loss of SAT during the progress of cancer cachexia.
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Affiliation(s)
- Jun Han
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai, China; Shanghai Clinical Nutrition Research Center, Shanghai, China
| | - Min Tang
- Department of Radiology, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Chaocheng Lu
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Lei Shen
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Jiaqi She
- Department of Radiology, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Guohao Wu
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai, China; Shanghai Clinical Nutrition Research Center, Shanghai, China.
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Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: a review. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2021; 12:878-901. [PMID: 34476169 PMCID: PMC8372315 DOI: 10.3762/bjnano.12.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/23/2021] [Indexed: 05/13/2023]
Abstract
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
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Affiliation(s)
- Ido Azuri
- Weizmann Institute of Science, Department of Life Sciences Core Facilities, Rehovot 76100, Israel
| | - Irit Rosenhek-Goldian
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
| | - Neta Regev-Rudzki
- Weizmann Institute of Science, Department of Biomolecular Sciences, Rehovot 76100, Israel
| | - Georg Fantner
- École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland
| | - Sidney R Cohen
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
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Best TD, Roeland EJ, Horick NK, Van Seventer EE, El-Jawahri A, Troschel AS, Johnson PC, Kanter KN, Fish MG, Marquardt JP, Bridge CP, Temel JS, Corcoran RB, Nipp RD, Fintelmann FJ. Muscle Loss Is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss. Oncologist 2021; 26:e963-e970. [PMID: 33818860 PMCID: PMC8176987 DOI: 10.1002/onco.13774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background Survival in patients with metastatic colorectal cancer (mCRC) has been associated with tumor mutational status, muscle loss, and weight loss. We sought to explore the combined effects of these variables on overall survival. Materials and Methods We performed an observational cohort study, prospectively enrolling patients receiving chemotherapy for mCRC. We retrospectively assessed changes in muscle (using computed tomography) and weight, each dichotomized as >5% or ≤5% loss, at 3, 6, and 12 months after diagnosis of mCRC. We used regression models to assess relationships between tumor mutational status, muscle loss, weight loss, and overall survival. Additionally, we evaluated associations between muscle loss, weight loss, and tumor mutational status. Results We included 226 patients (mean age 59 ± 13 years, 53% male). Tumor mutational status included 44% wild type, 42% RAS‐mutant, and 14% BRAF‐mutant. Patients with >5% muscle loss at 3 and 12 months experienced worse survival controlling for mutational status and weight (3 months hazard ratio, 2.66; p < .001; 12 months hazard ratio, 2.10; p = .031). We found an association of >5% muscle loss with BRAF‐mutational status at 6 and 12 months. Weight loss was not associated with survival nor mutational status. Conclusion Increased muscle loss at 3 and 12 months may identify patients with mCRC at risk for decreased overall survival, independent of tumor mutational status. Specifically, >5% muscle loss identifies patients within each category of tumor mutational status with decreased overall survival in our sample. Our findings suggest that quantifying muscle loss on serial computed tomography scans may refine survival estimates in patients with mCRC. Implications for Practice In this study of 226 patients with metastatic colorectal cancer, it was found that losing >5% skeletal muscle at 3 and 12 months after the diagnosis of metastatic disease was associated with worse overall survival, independent of tumor mutational status and weight loss. Interestingly, results did not show a significant association between weight loss and overall survival. These findings suggest that muscle quantification on serial computed tomography may refine survival estimates in patients with metastatic colorectal cancer beyond mutational status. Cancer cachexia has traditionally been defined using weight loss; however, loss of skeletal muscle may be a more objective measure. This article reports the results of a retrospective study that assessed whether skeletal muscle loss is associated with overall survival in patients with metastatic colorectal cancer, independent of tumor mutational status and weight loss.
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Affiliation(s)
- Till Dominik Best
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts
| | - Eric J Roeland
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Nora K Horick
- Department of Statistics, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Emily E Van Seventer
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Areej El-Jawahri
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Amelie S Troschel
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts
| | - Patrick C Johnson
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Katie N Kanter
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Madeleine G Fish
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - J Peter Marquardt
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts.,School of Medicine, RWTH Aachen University, Aachen, Germany
| | | | - Jennifer S Temel
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan B Corcoran
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan D Nipp
- Department of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital Cancer Center & Harvard Medical School, Boston, Massachusetts, USA
| | - Florian J Fintelmann
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts
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Ackermans LLGC, Volmer L, Wee L, Brecheisen R, Sánchez-González P, Seiffert AP, Gómez EJ, Dekker A, Ten Bosch JA, Olde Damink SMW, Blokhuis TJ. Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients. SENSORS 2021; 21:s21062083. [PMID: 33809710 PMCID: PMC8002279 DOI: 10.3390/s21062083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/28/2021] [Accepted: 03/10/2021] [Indexed: 12/15/2022]
Abstract
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.
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Affiliation(s)
- Leanne L. G. C. Ackermans
- Department of Traumatology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (J.A.T.B.); (T.J.B.)
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (R.B.); (S.M.W.O.D.)
- Correspondence: (L.L.G.C.A.); (L.V.); Tel.: +31-433-877-489 (L.L.G.C.A.); +31-884-456-00 (L.V.)
| | - Leroy Volmer
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (L.W.); (A.D.)
- Correspondence: (L.L.G.C.A.); (L.V.); Tel.: +31-433-877-489 (L.L.G.C.A.); +31-884-456-00 (L.V.)
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (L.W.); (A.D.)
- Clinical Data Science, Faculty of Health Medicine and Lifesciences, Maastricht University, Paul Henri Spaaklaan 1, 6229 GT Maastricht, The Netherlands
| | - Ralph Brecheisen
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (R.B.); (S.M.W.O.D.)
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (A.P.S.); (E.J.G.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Alexander P. Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (A.P.S.); (E.J.G.)
| | - Enrique J. Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain; (P.S.-G.); (A.P.S.); (E.J.G.)
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (L.W.); (A.D.)
- Clinical Data Science, Faculty of Health Medicine and Lifesciences, Maastricht University, Paul Henri Spaaklaan 1, 6229 GT Maastricht, The Netherlands
| | - Jan A. Ten Bosch
- Department of Traumatology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (J.A.T.B.); (T.J.B.)
| | - Steven M. W. Olde Damink
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (R.B.); (S.M.W.O.D.)
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, 52074 Aachen, Germany
| | - Taco J. Blokhuis
- Department of Traumatology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (J.A.T.B.); (T.J.B.)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp 2021; 5:11. [PMID: 33694046 PMCID: PMC7947128 DOI: 10.1186/s41747-021-00210-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 02/11/2021] [Indexed: 12/12/2022] Open
Abstract
Background Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images. Methods Ethical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3 days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations. Results The accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p < 0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of ± 20%. Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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Rozynek M, Kucybała I, Urbanik A, Wojciechowski W. Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives. Nutrition 2021; 89:111227. [PMID: 33930789 DOI: 10.1016/j.nut.2021.111227] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 01/28/2021] [Accepted: 02/25/2021] [Indexed: 01/10/2023]
Abstract
Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point, evaluating sarcopenia with imaging methods has been time-consuming and dependent on the skills of the physician. The solution for this problem may be found in artificial intelligence, which may assist radiologists in repetitive tasks such as muscle segmentation and body-composition analysis. The major aim of this review was to find and present the current status and future perspectives of artificial intelligence in the imaging of sarcopenia. We searched the PubMed database to find articles concerning the use of artificial intelligence in diagnostic imaging and especially in body-composition analysis in the context of sarcopenia. We found that artificial-intelligence systems could potentially help with evaluating sarcopenia and better predicting outcomes in a vast range of clinical situations, which could get us closer to the true era of precision medicine.
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Affiliation(s)
- Miłosz Rozynek
- Jagiellonian University Medical College, Department of Radiology, Krakow, Poland
| | - Iwona Kucybała
- Jagiellonian University Medical College, Department of Radiology, Krakow, Poland
| | - Andrzej Urbanik
- Jagiellonian University Medical College, Department of Radiology, Krakow, Poland
| | - Wadim Wojciechowski
- Jagiellonian University Medical College, Department of Radiology, Krakow, Poland.
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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Sarcopenia and Myosteatosis Predict Adverse Outcomes After Emergency Laparotomy: A Multi-Centre Observational Cohort Study. Ann Surg 2021; 275:1103-1111. [PMID: 33914486 DOI: 10.1097/sla.0000000000004781] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To determine the relationship between body composition (BC), specifically low skeletal muscle mass (sarcopenia) and poor muscle quality (myosteatosis) and outcomes in emergency laparotomy patients. BACKGROUND Emergency laparotomy has one of the highest morbidity and mortality rates of all surgical interventions. BC objectively identifies patients at risk of adverse outcomes in elective cancer cohorts, however evidence is lacking in emergency surgery. METHODS An observational cohort study of patients undergoing emergency laparotomy at ten English hospitals was performed. BC analyses were performed at the third lumbar vertebrae level using pre-operative CT images to quantify skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SM-RA). Sex-specific SMI and SM-RA were determined, with the lower tertile splits defining sarcopenia (low SMI) and myosteatosis (low SM-RA). Accuracy of mortality risk prediction, incorporating SMI and SM-RA variables into risk models was assessed with regression modelling. RESULTS Six hundred and ten patients were included. Sarcopenia and myosteatosis were both associated with increased risk of morbidity (52.1% vs. 45.1%, p = 0.028; 57.5% vs. 42.6%, p = 0.014), 30-day (9.5% vs. 3.6%, p = 0.010; 14.9% vs. 3.4%, p < 0.001), and 1-year mortality (27.4% vs. 11.5%, p < 0.001; 29.7% vs.12.5%, p < 0.001). Risk-adjusted 30-day mortality was significantly increased by sarcopenia (OR 2.56 (95%CI 1.12-5.84), p = 0.026) and myosteatosis (OR 4.26 (2.01-9.06), p < 0.001), similarly at 1-year (OR 2.66 (95%CI 1.57-4.52), p < 0.001; OR 2.08 (95%CI 1.26-3.41), p = 0.004). BC data increased discrimination of an existing mortality risk-prediction model (AUC 0.838, 95%CI 0.835-0.84). CONCLUSION Sarcopenia and myosteatosis are associated with increased adverse outcomes in emergency laparotomy patients.
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20S proteasomes secreted by the malaria parasite promote its growth. Nat Commun 2021; 12:1172. [PMID: 33608523 PMCID: PMC7895969 DOI: 10.1038/s41467-021-21344-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/20/2021] [Indexed: 12/16/2022] Open
Abstract
Mature red blood cells (RBCs) lack internal organelles and canonical defense mechanisms, making them both a fascinating host cell, in general, and an intriguing choice for the deadly malaria parasite Plasmodium falciparum (Pf), in particular. Pf, while growing inside its natural host, the human RBC, secretes multipurpose extracellular vesicles (EVs), yet their influence on this essential host cell remains unknown. Here we demonstrate that Pf parasites, cultured in fresh human donor blood, secrete within such EVs assembled and functional 20S proteasome complexes (EV-20S). The EV-20S proteasomes modulate the mechanical properties of naïve human RBCs by remodeling their cytoskeletal network. Furthermore, we identify four degradation targets of the secreted 20S proteasome, the phosphorylated cytoskeletal proteins β-adducin, ankyrin-1, dematin and Epb4.1. Overall, our findings reveal a previously unknown 20S proteasome secretion mechanism employed by the human malaria parasite, which primes RBCs for parasite invasion by altering membrane stiffness, to facilitate malaria parasite growth. Plasmodium falciparum secretes extracellular vesicles (EVs) while growing inside red blood cells (RBCs). Here the authors show that these EVs contain assembled and functional 20S proteasome complexes that remodel the cytoskeleton of naïve human RBCs, priming the RBCs for parasite invasion.
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Drukker K, Yan P, Sibley A, Wang G. Biomedical imaging and analysis through deep learning. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Coombs C, Hislop D, Taneva SK, Barnard S. The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. JOURNAL OF STRATEGIC INFORMATION SYSTEMS 2020. [DOI: 10.1016/j.jsis.2020.101600] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Blanc-Durand P, Schiratti JB, Schutte K, Jehanno P, Herent P, Pigneur F, Lucidarme O, Benaceur Y, Sadate A, Luciani A, Ernst O, Rouchaud A, Creze M, Dallongeville A, Banaste N, Cadi M, Bousaid I, Lassau N, Jegou S. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagn Interv Imaging 2020; 101:789-794. [DOI: 10.1016/j.diii.2020.04.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/26/2020] [Accepted: 04/28/2020] [Indexed: 12/18/2022]
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Magudia K, Bridge CP, Bay CP, Babic A, Fintelmann FJ, Troschel FM, Miskin N, Wrobel WC, Brais LK, Andriole KP, Wolpin BM, Rosenthal MH. Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves. Radiology 2020; 298:319-329. [PMID: 33231527 DOI: 10.1148/radiol.2020201640] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival. Materials and Methods After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using χ2 tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI). Results External validation showed excellent correlation (R = 0.99) and equivalency (P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables (P < .001 except for subcutaneous fat area vs age [P = .003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used (P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival (P = .04) in combined models that included BMI. Conclusion Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Summers in this issue.
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Affiliation(s)
- Kirti Magudia
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Christopher P Bridge
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Camden P Bay
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Ana Babic
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Florian J Fintelmann
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Fabian M Troschel
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Nityanand Miskin
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - William C Wrobel
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Lauren K Brais
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Katherine P Andriole
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Brian M Wolpin
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
| | - Michael H Rosenthal
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (K.M., C.P. Bay, N.M., W.C.W., M.H.R.); MGH & BWH Center for Clinical Data Science, Boston, Mass (C.P. Bridge, K.P.A.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Mass (A.B., L.K.B., B.M.W.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (F.J.F., F.M.T.)
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Yune S, Lee H, Kim M, Tajmir SH, Gee MS, Do S. Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model. J Digit Imaging 2020; 32:665-671. [PMID: 30478479 PMCID: PMC6646498 DOI: 10.1007/s10278-018-0148-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model to predict sex based on hand radiographs of children and adults aged between 5 and 70 years. Of the 1531 radiographs tested, the algorithm predicted sex correctly in 95.9% (κ = 0.92) of the cases. Two human radiologists achieved 58% (κ = 0.15) and 46% (κ = − 0.07) accuracy. The class activation maps (CAM) showed that the model mostly focused on the 2nd and 3rd metacarpal base or thumb sesamoid in women, and distal radioulnar joint, distal radial physis and epiphysis, or 3rd metacarpophalangeal joint in men. The radiologists reviewed 70 cases (35 females and 35 males) labeled with sex along with heat maps generated by CAM, but they could not find any patterns that distinguish the two sexes. A small sample of patients (n = 44) with sexual developmental disorders or transgender identity was selected for a preliminary exploration of application of the model. The model prediction agreed with phenotypic sex in only 77.8% (κ = 0.54) of these cases. To the best of our knowledge, this is the first study that demonstrated a machine learning model to perform a task in which human experts could not fulfill.
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Affiliation(s)
- Sehyo Yune
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA
| | - Hyunkwang Lee
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA
| | - Myeongchan Kim
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA
| | - Shahein H Tajmir
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA
| | - Synho Do
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street Suite 400B, Boston, MA, 02114, USA.
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49
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Fu Y, Ippolito JE, Ludwig DR, Nizamuddin R, Li HH, Yang D. Technical Note: Automatic segmentation of CT images for ventral body composition analysis. Med Phys 2020; 47:5723-5730. [PMID: 32969050 DOI: 10.1002/mp.14465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/28/2020] [Accepted: 09/04/2020] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Body composition is known to be associated with many diseases including diabetes, cancers, and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscle. Three additional compartments - the ventral cavity, lung, and bones - were also segmented during the segmentation process to assist segmentation of the major compartments. METHODS A convolutional neural network (CNN) model with densely connected layers was developed to perform ventral cavity segmentation. An image processing workflow was developed to segment the ventral cavity in any patient's computed tomography (CT) using the CNN model, then further segment the body tissue into multiple compartments using hysteresis thresholding followed by morphological operations. It is important to segment ventral cavity firstly to allow accurate separation of compartments with similar Hounsfield unit (HU) inside and outside the ventral cavity. RESULTS The ventral cavity segmentation CNN model was trained and tested with manually labeled ventral cavities in 60 CTs. Dice scores (mean ± standard deviation) for ventral cavity segmentation were 0.966 ± 0.012. Tested on CT datasets with intravenous (IV) and oral contrast, the Dice scores were 0.96 ± 0.02, 0.94 ± 0.06, 0.96 ± 0.04, 0.95 ± 0.04, and 0.99 ± 0.01 for bone, VAT, SAT, muscle, and lung, respectively. The respective Dice scores were 0.97 ± 0.02, 0.94 ± 0.07, 0.93 ± 0.06, 0.91 ± 0.04, and 0.99 ± 0.01 for non-contrast CT datasets. CONCLUSION A body tissue decomposition procedure was developed to automatically segment multiple compartments of the ventral body. The proposed method enables fully automated quantification of three-dimensional (3D) ventral body composition metrics from CT images.
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Affiliation(s)
- Yabo Fu
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
| | - Joseph E Ippolito
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
| | - Daniel R Ludwig
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
| | - Rehan Nizamuddin
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
| | - Harold H Li
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
| | - Deshan Yang
- Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA
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50
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Lidén M, Hjelmgren O, Vikgren J, Thunberg P. Multi-Reader-Multi-Split Annotation of Emphysema in Computed Tomography. J Digit Imaging 2020; 33:1185-1193. [PMID: 32779016 PMCID: PMC7572947 DOI: 10.1007/s10278-020-00378-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 06/23/2020] [Accepted: 07/23/2020] [Indexed: 10/28/2022] Open
Abstract
Emphysema is visible on computed tomography (CT) as low-density lesions representing the destruction of the pulmonary alveoli. To train a machine learning model on the emphysema extent in CT images, labeled image data is needed. The provision of these labels requires trained readers, who are a limited resource. The purpose of the study was to test the reading time, inter-observer reliability and validity of the multi-reader-multi-split method for acquiring CT image labels from radiologists. The approximately 500 slices of each stack of lung CT images were split into 1-cm chunks, with 17 thin axial slices per chunk. The chunks were randomly distributed to 26 readers, radiologists and radiology residents. Each chunk was given a quick score concerning emphysema type and severity in the left and right lung separately. A cohort of 102 subjects, with varying degrees of visible emphysema in the lung CT images, was selected from the SCAPIS pilot, performed in 2012 in Gothenburg, Sweden. In total, the readers created 9050 labels for 2881 chunks. Image labels were compared with regional annotations already provided at the SCAPIS pilot inclusion. The median reading time per chunk was 15 s. The inter-observer Krippendorff's alpha was 0.40 and 0.53 for emphysema type and score, respectively, and higher in the apical part than in the basal part of the lungs. The multi-split emphysema scores were generally consistent with regional annotations. In conclusion, the multi-reader-multi-split method provided reasonably valid image labels, with an estimation of the inter-observer reliability.
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Affiliation(s)
- Mats Lidén
- Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
| | - Ola Hjelmgren
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jenny Vikgren
- Department of Radiology, Sahlgrenska University Hospital and Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Per Thunberg
- Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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