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Eltorai AEM, McKinney SE, Rockenbach MABC, Karuppiah S, Bizzo BC, Andriole KP. Primary care provider perspectives on the value of opportunistic CT screening. Clin Imaging 2024; 112:110210. [PMID: 38850710 DOI: 10.1016/j.clinimag.2024.110210] [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: 02/08/2024] [Revised: 05/10/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS. METHODS A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation. RESULTS 62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns. CONCLUSION PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.
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Affiliation(s)
- Adam E M Eltorai
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Suzannah E McKinney
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | | | - Saby Karuppiah
- Department of Family Medicine, HCA Healthcare, Kansas City, MO, United States of America
| | - Bernardo C Bizzo
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | - Katherine P Andriole
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
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Maurya AK, Aggarwal LM, Choudhary S. Body Composition Analysis Techniques and Its Application in Oncology: A Review. Nutr Cancer 2024; 76:666-675. [PMID: 38757446 DOI: 10.1080/01635581.2024.2353942] [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: 07/30/2023] [Revised: 05/06/2024] [Accepted: 05/06/2024] [Indexed: 05/18/2024]
Abstract
The oncology community has shown growing interest to understand how body composition measures can be utilized to improve cancer treatment and survivorship care for about 20 million individuals diagnosed with cancer annually. Recent observational studies demonstrate that muscle and adipose tissue distribution are risk factors for clinical outcomes such as postoperative complications, and worse overall survival. There is an emergent recognition that body mass index (BMI) is neither adequate to identify patients with adverse health outcomes due to poor muscle health or excess adiposity, nor does BMI accurately classify the distribution of adiposity. Abdominal CT is a most frequently imaging examination for a wide variety of clinical indications, but it is only used to diagnose the immediate problem. Additionally, each CT examination contains very robust data on body composition which generally goes unused in routine clinical practice. The field is eager to identify therapeutic interventions that modify body composition and reduce the incidence of poor clinical outcomes in this population. Large scale population based screening is feasible now by making all of these relevant biometric measures fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment.
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Affiliation(s)
- Anil Kumar Maurya
- Department of Radiotherapy & Radiation Medicine, IMS, BHU, Varanasi, Uttar Pradesh, India
- Department of Radiation Oncology, Moti Lal Nehru Medical College, Prayagraj, Uttar Pradesh, India
| | - Lalit Mohan Aggarwal
- Department of Radiotherapy & Radiation Medicine, IMS, BHU, Varanasi, Uttar Pradesh, India
| | - Sunil Choudhary
- Department of Radiotherapy & Radiation Medicine, IMS, BHU, Varanasi, Uttar Pradesh, India
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3
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Bunk SA, Ipema J, Sidorenkov G, Bennink E, Vliegenthart R, de Jong PA, Pompe E, Charbonnier JP, Luijk BH, Aerts J, Groen HJ, Mohamed Hoesein FA. The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers. ERJ Open Res 2024; 10:00749-2023. [PMID: 38444665 PMCID: PMC10910310 DOI: 10.1183/23120541.00749-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/17/2023] [Indexed: 03/07/2024] Open
Abstract
Introduction Differences in body composition in patients with COPD may have important prognostic value and may provide opportunities for patient-specific management. We investigated the relation of thoracic fat and muscle with computed tomography (CT)-measured emphysema and bronchial wall thickening. Methods Low-dose baseline chest CT scans from 1031 male lung cancer screening participants from one site were quantified for emphysema, bronchial wall thickening, subcutaneous fat, visceral fat and skeletal muscle. Body composition measurements were performed by segmenting the first slice above the aortic arch using Hounsfield unit thresholds with region growing and manual corrections. COPD presence and severity were evaluated with pre-bronchodilator spirometry testing. Results Participants had a median age of 61.5 years (58.6-65.6, 25th-75th percentile) and median number of 38.0 pack-years (28.0-49.5); 549 (53.2%) were current smokers. Overall, 396 (38.4%) had COPD (256 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1, 140 GOLD 2-3). Participants with COPD had less subcutaneous fat, visceral fat and skeletal muscle (p<0.001 for all). With increasing GOLD stages, subcutaneous (p=0.005) and visceral fat values (p=0.004) were higher, and skeletal muscle was lower (p=0.004). With increasing severity of CT-derived emphysema, subcutaneous fat, visceral fat and skeletal muscle values were lower (p<0.001 for all). With increasing CT-derived bronchial wall thickness, subcutaneous and visceral fat values were higher (p<0.001 for both), without difference in skeletal muscle. All statistical relationships remained when adjusted for age, pack-years and smoking status. Conclusion COPD presence and emphysema severity are associated with smaller amounts of thoracic fat and muscle, whereas bronchial wall thickening is associated with fat accumulation.
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Affiliation(s)
- Stijn A.O. Bunk
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jetty Ipema
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Grigory Sidorenkov
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rozemarijn Vliegenthart
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Groningen, The Netherlands
| | - Pim A. de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Esther Pompe
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Bart H.D. Luijk
- Department of Pulmonology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joachim Aerts
- Department of Respiratory Medicine, ErasmusMC, Rotterdam, The Netherlands
| | - Harry J.M. Groen
- Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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4
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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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5
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Al-Sawaf O, Weiss J, Skrzypski M, Lam JM, Karasaki T, Zambrana F, Kidd AC, Frankell AM, Watkins TBK, Martínez-Ruiz C, Puttick C, Black JRM, Huebner A, Bakir MA, Sokač M, Collins S, Veeriah S, Magno N, Naceur-Lombardelli C, Prymas P, Toncheva A, Ward S, Jayanth N, Salgado R, Bridge CP, Christiani DC, Mak RH, Bay C, Rosenthal M, Sattar N, Welsh P, Liu Y, Perrimon N, Popuri K, Beg MF, McGranahan N, Hackshaw A, Breen DM, O'Rahilly S, Birkbak NJ, Aerts HJWL, Jamal-Hanjani M, Swanton C. Body composition and lung cancer-associated cachexia in TRACERx. Nat Med 2023; 29:846-858. [PMID: 37045997 PMCID: PMC7614477 DOI: 10.1038/s41591-023-02232-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/24/2023] [Indexed: 04/14/2023]
Abstract
Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC.
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Affiliation(s)
- Othman Al-Sawaf
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Diagnostic and Interventional Radiology, University Freiburg, Freiburg, Germany
| | - Marcin Skrzypski
- Department of Oncology and Radiotherapy, Medical University of Gdańsk, Gdańsk, Poland
| | - Jie Min Lam
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Andrew C Kidd
- Institute of Infection, Immunity & Inflammation, University of Glasgow, Glasgow, UK
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Clare Puttick
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mateo Sokač
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Susie Collins
- Early Clinical Development, Pfizer UK Ltd, Cambridge, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Neil Magno
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | - Paulina Prymas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Nick Jayanth
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | | | - David C Christiani
- Department of Medicine, Massachusetts General Hospital/Harvard Medicine School, and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Camden Bay
- Department of Radiology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA
| | - Michael Rosenthal
- Department of Radiology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Ying Liu
- Department of Genetics, Harvard Medical School, Boston, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, USA
| | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, Boston, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, USA
| | - Karteek Popuri
- Department of Computer Science, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Burnaby, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, British Colombia, Canada
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Allan Hackshaw
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | - Danna M Breen
- Internal Medicine Research Unit, Pfizer, Cambridge, MA, USA
| | - Stephen O'Rahilly
- Wellcome Trust-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Nicolai J Birkbak
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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Humphrey TJ, Salimy MS, Jancuska JM, Egan CR, Melnic CM, Alpaugh K, Bedair HS. Sarcopenia is an independent risk factor for failure to achieve the 1-year MCID of the KOOS, JR and PROMIS PF-SF10a after TKA. Knee 2023; 42:64-72. [PMID: 36913864 DOI: 10.1016/j.knee.2023.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/07/2023] [Accepted: 02/28/2023] [Indexed: 03/15/2023]
Abstract
BACKGROUND Sarcopenia, which is a progressive and multifactorial condition of decreased muscle strength, has been identified as an independent predictor for falls, revision, infection, and readmissions following total knee arthroplasty (TKA), but its association to patient reported outcomes (PROMs) is less studied. The aim of this study is to determine if sarcopenia and other measures of body composition are correlated with ability to achieve the 1-year minimal clinically important difference (MCID) of the KOOS JR and PROMIS-PF-SF10a following primary TKA. METHODS A multicenter retrospective case-control study was performed. Inclusion criteria consisted of patients over the age of 18 undergoing primary TKA, body composition metrics determined by computed tomography (CT), and available pre- and post-operative PROM scores. Predictors of achievement of the 1-year MCID of the KOOS JR and PROMIS PF-SF-10a were determined through a multivariate linear regression. RESULTS 140 primary TKAs met inclusion criteria. 74 (52.85%) patients achieved the 1-year KOOS, JR MCID and 108 (77.41%) patients achieved the 1-year MCID for the PROMIS PF-SF10a. Sarcopenia was independently associated with decreased odds of achieving the MCID of both the KOOS, JR (OR 0.31, 95%CI 0.10-0.97, p = 0.04) and the PROMIS-PF-SF10a (OR 0.32, 95%CI 0.12-0.85, p = 0.02) CONCLUSIONS: In our study, sarcopenia was independently associated with increased odds of failure to achieve the 1-year MCID of the KOOS, JR and PROMIS PF-SF10a after TKA. Early identification of sarcopenic patients may be beneficial for arthroplasty surgeons so that targeted nutritional counseling and exercises can be recommended prior to TKA.
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Affiliation(s)
- Tyler J Humphrey
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
| | - Mehdi S Salimy
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States.
| | - Jeffrey M Jancuska
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
| | - Cameron R Egan
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
| | - Christopher M Melnic
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
| | - Kyle Alpaugh
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
| | - Hany S Bedair
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States; Kaplan Joint Center, Newton-Wellesley Hospital, Newton, MA, United States.
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7
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Ye Z, Saraf A, Ravipati Y, Hoebers F, Zha Y, Zapaishchykova A, Likitlersuang J, Tishler RB, Schoenfeld JD, Margalit DN, Haddad RI, Mak RH, Naser M, Wahid KA, Sahlsten J, Jaskari J, Kaski K, Mäkitie AA, Fuller CD, Aerts HJ, Kann BH. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.01.23286638. [PMID: 36945519 PMCID: PMC10029039 DOI: 10.1101/2023.03.01.23286638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Purpose Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes. Materials and Methods 899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression. Results DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r β 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis. Conclusion We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC. SUMMARY STATEMENT In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.
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Affiliation(s)
- Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Frank Hoebers
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Yining Zha
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Roy B. Tishler
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Jonathan D. Schoenfeld
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Danielle N. Margalit
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Robert I. Haddad
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Raymond H. Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Mohamed Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Antti A. Mäkitie
- Department Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hugo J.W.L. Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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8
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Aparisi Gómez MP, Aparisi F, Morganti AG, Fanti S, Bazzocchi A. Effects of Radiation Therapy and Chemotherapy on the Musculoskeletal System. Semin Musculoskelet Radiol 2022; 26:338-353. [PMID: 35654099 DOI: 10.1055/s-0041-1740995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The effects of radiation and chemotherapy on the musculoskeletal (MSK) system are diverse, and interpretation may be challenging. The different lines of treatment have effects on diseased and normal marrow, and they may lead to complications that must be differentiated from recurrence or progression. This review analyzes the changes induced by radiotherapy and chemotherapy in the MSK system in the adult and pediatric population, and the expected associated imaging findings. Treatments are often combined, so the effects may blend. Awareness of the spectrum of changes, complications, and their imaging appearances is paramount for the correct diagnosis. The assessment of body composition during and after treatment allows potential interventions to implement long-term outcomes and personalize treatments. Imaging techniques such as computed tomography or magnetic resonance imaging provide information on body composition that can be incorporated into clinical pathways. We also address future perspectives in posttreatment assessment.
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Affiliation(s)
- Maria Pilar Aparisi Gómez
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand.,Department of Radiology, IMSKE, Valencia, Spain
| | - Francisco Aparisi
- Department of Radiology, Hospital Vithas Nueve de Octubre, Valencia, Spain
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, University of Bologna, Bologna, Italy.,Department of Experimental, Diagnostic and Specialty Medicine - DIMES, Alma Mater Studiroum Bologna University, Bologna, Italy
| | - Stefano Fanti
- Department of Experimental, Diagnostic and Specialty Medicine - DIMES, Alma Mater Studiroum Bologna University, Bologna, Italy.,Nuclear Medicine Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, University of Bologna, Bologna, Italy
| | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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9
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Xu K, Gao R, Tang Y, Deppen SA, Sandler KL, Kammer MN, Antic SL, Maldonado F, Huo Y, Khan MS, Landman BA. Extending the value of routine lung screening CT with quantitative body composition assessment. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:120321L. [PMID: 36303578 PMCID: PMC9604426 DOI: 10.1117/12.2611784] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with incidental lung cancer risk in the screening population is still unclear. We study the feasibility of body composition analysis using chest low dose computed tomography (LDCT). A two-stage fully automatic pipeline is developed to assess the cross-sectional area of body composition components including subcutaneous adipose tissue (SAT), muscle, visceral adipose tissue (VAT), and bone on T5, T8 and T10 vertebral levels. The pipeline is developed using 61 cases of the VerSe'20 dataset, 40 annotated cases of NLST, and 851 inhouse screening cases. On a test cohort consisting of 30 cases from the inhouse screening cohort (age 55 - 73, 50% female) and 42 cases of NLST (age 55 - 75, 59.5% female), the pipeline achieves a root mean square error (RMSE) of 7.25 mm (95% CI: [6.61, 7.85]) for the vertebral level identification and mean Dice similarity score (DSC) 0.99 ± 0.02, 0.96 ± 0.03, and 0.95 ± 0.04 for SAT, muscle, and VAT, respectively for body composition segmentation. The pipeline is generalized to the CT arm of the NLST dataset (25,205 subjects, 40.8% female, 1,056 lung cancer incidences). Time-to-event analysis for lung cancer incidence indicates inverse association between measured muscle cross-sectional area and incidental lung cancer risks (p < 0.001 female, p < 0.001 male). In conclusion, automatic body composition analysis using routine lung screening LDCT is feasible.
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Affiliation(s)
- Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville TN, USA 37235
| | - Riqiang Gao
- Department of Computer Science, Vanderbilt University, Nashville TN, USA 37235
| | - Yucheng Tang
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville TN, USA 37235
| | - Steve A. Deppen
- Vanderbilt University Medical Center, Nashville TN, USA 37235
| | - Kim L. Sandler
- Vanderbilt University Medical Center, Nashville TN, USA 37235
| | | | - Sanja L. Antic
- Vanderbilt University Medical Center, Nashville TN, USA 37235
| | | | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville TN, USA 37235
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville TN, USA 37235
| | - Mirza S. Khan
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville TN, USA 37235
- Vanderbilt University Medical Center, Nashville TN, USA 37235
- Department of Biomedical Informatics, Vanderbilt University, Nashville TN, USA 37235
- U.S. Department of Veterans Affairs, Nashville TN, USA 37212
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville TN, USA 37235
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville TN, USA 37235
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10
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Laur O, Wang B. Musculoskeletal trauma and artificial intelligence: current trends and projections. Skeletal Radiol 2022; 51:257-269. [PMID: 34089338 DOI: 10.1007/s00256-021-03824-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/02/2023]
Abstract
Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.
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Affiliation(s)
- Olga Laur
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA
| | - Benjamin Wang
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA.
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11
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Beetz NL, Maier C, Segger L, Shnayien S, Trippel TD, Lindow N, Bousabarah K, Westerhoff M, Fehrenbach U, Geisel D. First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine. JCSM CLINICAL REPORTS 2021. [DOI: 10.1002/crt2.44] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Nick Lasse Beetz
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin Berlin Germany
| | - Christoph Maier
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
| | - Laura Segger
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
| | - Seyd Shnayien
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
| | - Tobias Daniel Trippel
- DZHK (German Centre for Cardiovascular Research), partner site Berlin Berlin Germany
- Department of Internal Medicine – Cardiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
| | | | | | | | - Uli Fehrenbach
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
| | - Dominik Geisel
- Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany
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12
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Hsu TMH, Schawkat K, Berkowitz SJ, Wei JL, Makoyeva A, Legare K, DeCicco C, Paez SN, Wu JSH, Szolovits P, Kikinis R, Moser AJ, Goehler A. Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer- A recipe for your local application. Eur J Radiol 2021; 142:109834. [PMID: 34252866 DOI: 10.1016/j.ejrad.2021.109834] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 06/06/2021] [Accepted: 06/21/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Body composition is associated with mortality; however its routine assessment is too time-consuming. PURPOSE To demonstrate the value of artificial intelligence (AI) to extract body composition measures from routine studies, we aimed to develop a fully automated AI approach to measure fat and muscles masses, to validate its clinical discriminatory value, and to provide the code, training data and workflow solutions to facilitate its integration into local practice. METHODS We developed a neural network that quantified the tissue components at the L3 vertebral body level using data from the Liver Tumor Challenge (LiTS) and a pancreatic cancer cohort. We classified sarcopenia using accepted skeletal muscle index cut-offs and visceral fat based its median value. We used Kaplan Meier curves and Cox regression analysis to assess the association between these measures and mortality. RESULTS Applying the algorithm trained on LiTS data to the local cohort yielded good agreement [>0.8 intraclass correlation (ICC)]; when trained on both datasets, it had excellent agreement (>0.9 ICC). The pancreatic cancer cohort had 136 patients (mean age: 67 ± 11 years; 54% women); 15% had sarcopenia; mean visceral fat was 142 cm2. Concurrent with prior research, we found a significant association between sarcopenia and mortality [mean survival of 15 ± 12 vs. 22 ± 12 (p < 0.05), adjusted HR of 1.58 (95% CI: 1.03-3.33)] but no association between visceral fat and mortality. The detector analysis took 1 ± 0.5 s. CONCLUSIONS AI body composition analysis can provide meaningful imaging biomarkers from routine exams demonstrating AI's ability to further enhance the clinical value of radiology reports.
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Affiliation(s)
- Tzu-Ming Harry Hsu
- MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States
| | - Khoschy Schawkat
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Seth J Berkowitz
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Jesse L Wei
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Alina Makoyeva
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Kaila Legare
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Corinne DeCicco
- The Pancreas and Liver Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - S Nicolas Paez
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Jim S H Wu
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Peter Szolovits
- MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States
| | - Ron Kikinis
- Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St. Boston, MA 02215, United States
| | - Arthur J Moser
- The Pancreas and Liver Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Alexander Goehler
- MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States; Center for Evidence Based Imaging, Department of Radiology, Brigham and Women's Hospital, 20 Kent Street, Brookline, MA 02445, United States.
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13
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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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14
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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: 83] [Impact Index Per Article: 20.8] [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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15
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Roeland EJ, Phull H, Hagmann C, Sera C, Dullea AD, El-Jawahri A, Nelson S, Gallivan A, Ma JD, Nipp RD, Baracos VE. FIT: Functional and imaging testing for patients with metastatic cancer. Support Care Cancer 2020; 29:2771-2775. [PMID: 32990784 DOI: 10.1007/s00520-020-05730-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 08/28/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND Selecting study endpoints in prospective cancer cachexia trials remains poorly defined. The aim of this study was to further evaluate associations in changes in weight, body composition, functional outcomes, and patient-reported outcomes (PROs) in patients with metastatic cancer. METHODS We completed a 2-year (2016-2018) observational study in patients with metastatic solid cancer and ECOG performance status 0 to 2 while receiving chemotherapy and/or immunotherapy. We completed assessments at study enrollment and 3 months from enrollment. We analyzed longitudinal changes in weight and body composition using validated methods. Functional assessments included the 6-Min Walk Test, Timed Up and Go Test, and Short Physical Performance Battery. PROs included the Functional Assessment of Anorexia/Cachexia Therapy and Functional Assessment of Cancer Therapy Fatigue. We analyzed changes in body composition and functional assessment using paired t tests. Additionally, we utilized linear regression models to assess relationships between changes in body composition and function outcomes and PROs, adjusting for age and sex. RESULTS A total of 57 patients completed baseline assessments, but 19 patients did not complete 3-month assessments (5 died, 1 hospice, 13 withdrew). Of the 38 patients with complete data, the mean age was 61.8 years and 47% were female. Metastatic cancer types included 71% gastrointestinal, 13% lung, and 8% gynecologic. Half received chemotherapy, 16% immunotherapy, and 34% a combination. From enrollment to 3 months, we did not observe a change in weight or skeletal muscle but did find an increase in total adipose tissue (16.9 ± 52.4 cm2, 95% CI - 33.79-0.63; p = 0.059; ~ 1.5 pounds). We did not observe any association with changes in weight with any functional outcomes or PROs. However, greater losses in skeletal muscle were associated with greater declines in physical function (6-Min Walk Test [B = 0.04, p = 0.01], Short Physical Performance Battery [B = 2.44, p < 0.01]). CONCLUSIONS Patients with metastatic cancer receiving cancer-directed therapy may not experience a change in body weight. However, we found an association between losses in skeletal muscle and greater declines in physical function. Therefore, when selecting study endpoints, prospective cancer cachexia studies may consider selecting changes in body composition over weight.
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Affiliation(s)
- Eric J Roeland
- Massachusetts General Hospital Cancer Center, 55 Fruit Street, Yawkey 7E, Boston, MA, 02114, USA.
- University of California San Diego Moores Cancer Center, San Diego, CA, USA.
| | - H Phull
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - C Hagmann
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - C Sera
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - A D Dullea
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - A El-Jawahri
- Massachusetts General Hospital Cancer Center, 55 Fruit Street, Yawkey 7E, Boston, MA, 02114, USA
| | - S Nelson
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - A Gallivan
- University of Alberta Edmonton, Edmonton, Canada
| | - J D Ma
- University of California San Diego Moores Cancer Center, San Diego, CA, USA
| | - R D Nipp
- Massachusetts General Hospital Cancer Center, 55 Fruit Street, Yawkey 7E, Boston, MA, 02114, USA
| | - V E Baracos
- University of Alberta Edmonton, Edmonton, Canada
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16
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Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur Radiol 2020; 31:1795-1804. [PMID: 32945971 PMCID: PMC7979624 DOI: 10.1007/s00330-020-07147-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/18/2020] [Accepted: 08/04/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. METHODS Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. RESULTS The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. CONCLUSIONS Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. KEY POINTS • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.
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17
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Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, Huh J, Lee TY, Park T, Lee J, Kim KW. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography. Korean J Radiol 2020; 21:88-100. [PMID: 31920032 PMCID: PMC6960305 DOI: 10.3348/kjr.2019.0470] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/15/2019] [Indexed: 12/22/2022] Open
Abstract
Objective We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.
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Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yongbin Shin
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Jisuk Park
- Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyosang Kim
- Department of Nephrology, Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - In Seob Lee
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong Woo Seo
- Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jimi Huh
- Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital, Suwon, Korea
| | - Tae Young Lee
- Department of Radiology, Ulsan University Hospital, Ulsan, Korea
| | - TaeYong Park
- 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 Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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18
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Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol 2020; 5:698-710. [PMID: 32135127 PMCID: PMC7380506 DOI: 10.1016/s2468-1253(19)30416-9] [Citation(s) in RCA: 243] [Impact Index Per Article: 60.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 10/30/2019] [Accepted: 11/05/2019] [Indexed: 02/07/2023]
Abstract
Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3-15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.
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Affiliation(s)
- Stephen P Pereira
- Institute for Liver and Digestive Health, University College London, London, UK
| | - Lucy Oldfield
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Alexander Ney
- Institute for Liver and Digestive Health, University College London, London, UK
| | - Phil A Hart
- Division of Gastroenterology, Hepatology, and Nutrition, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Margaret G Keane
- Division of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen J Pandol
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William Greenhalf
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Christie Y Jeon
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Eugene J Koay
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Christopher V Almario
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Christopher Halloran
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Anne Marie Lennon
- Division of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, MD, USA
| | - Eithne Costello
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK.
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Liu T, Pan J, Torigian DA, Xu P, Miao Q, Tong Y, Udupa JK. ABCNet: A new efficient 3D dense-structure network for segmentation and analysis of body tissue composition on body-torso-wide CT images. Med Phys 2020; 47:2986-2999. [PMID: 32170754 DOI: 10.1002/mp.14141] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 12/16/2022] Open
Abstract
PURPOSE Quantification of body tissue composition is important for research and clinical purposes, given the association between the presence and severity of several disease conditions, such as the incidence of cardiovascular and metabolic disorders, survival after chemotherapy, etc., with the quantity and quality of body tissue composition. In this work, we aim to automatically segment four key body tissues of interest, namely subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, and skeletal structures from body-torso-wide low-dose computed tomography (CT) images. METHOD Based on the idea of residual Encoder-Decoder architecture, a novel neural network design named ABCNet is proposed. The proposed system makes full use of multiscale features from four resolution levels to improve the segmentation accuracy. This network is built on a uniform convolutional unit and its derived units, which makes the ABCNet easy to implement. Several parameter compression methods, including Bottleneck, linear increasing feature maps in Dense Blocks, and memory-efficient techniques, are employed to lighten the network while making it deeper. The strategy of dynamic soft Dice loss is introduced to optimize the network in coarse-to-fine tuning. The proposed segmentation algorithm is accurate, robust, and very efficient in terms of both time and memory. RESULTS A dataset composed of 38 low-dose unenhanced CT images, with 25 male and 13 female subjects in the age range 31-83 yr and ranging from normal to overweight to obese, is utilized to evaluate ABCNet. We compare four state-of-the-art methods including DeepMedic, 3D U-Net, V-Net, Dense V-Net, against ABCNet on this dataset. We employ a shuffle-split fivefold cross-validation strategy: In each experimental group, 18, 5, and 15 CT images are randomly selected out of 38 CT image sets for training, validation, and testing, respectively. The commonly used evaluation metrics - precision, recall, and F1-score (or Dice) - are employed to measure the segmentation quality. The results show that ABCNet achieves superior performance in accuracy of segmenting body tissues from body-torso-wide low-dose CT images compared to other state-of-the-art methods, reaching 92-98% in common accuracy metrics such as F1-score. ABCNet is also time-efficient and memory-efficient. It costs about 18 h to train and an average of 12 sec to segment four tissue components from a body-torso-wide CT image, on an ordinary desktop with a single ordinary GPU. CONCLUSIONS Motivated by applications in body tissue composition quantification on large population groups, our goal in this paper was to create an efficient and accurate body tissue segmentation method for use on body-torso-wide CT images. The proposed ABCNet achieves peak performance in both accuracy and efficiency that seems hard to improve any more. The experiments performed demonstrate that ABCNet can be run on an ordinary desktop with a single ordinary GPU, with practical times for both training and testing, and achieves superior accuracy compared to other state-of-the-art segmentation methods for the task of body tissue composition analysis from low-dose CT images.
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Affiliation(s)
- Tiange Liu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
| | - Junwen Pan
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.,College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
| | - Drew A Torigian
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Pengfei Xu
- School of Information Science and Technology, Northwest University, Xi'an, 710127, China
| | - Qiguang Miao
- School of Computer Science and Technology, Xidian University, Xi'an, 710126, China
| | - Yubing Tong
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Jayaram K Udupa
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
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20
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Madariaga MLL, Troschel FM, Best TD, Knoll SJ, Gaissert HA, Fintelmann FJ. Low Thoracic Skeletal Muscle Area Predicts Morbidity After Pneumonectomy for Lung Cancer. Ann Thorac Surg 2020; 109:907-913. [DOI: 10.1016/j.athoracsur.2019.10.041] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 10/03/2019] [Accepted: 10/14/2019] [Indexed: 12/20/2022]
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21
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Blanc-Durand P, Campedel L, Mule S, Jegou S, Luciani A, Pigneur F, Itti E. Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. Eur Radiol 2020; 30:3528-3537. [PMID: 32055950 DOI: 10.1007/s00330-019-06630-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/12/2019] [Accepted: 12/13/2019] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC). METHODS A convolutional neural network was trained to perform automatic segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscular body mass (MBM) from low-dose CT images in 189 patients with NSCLC who underwent pretherapy PET/CT. After a fivefold cross-validation in a subset of 35 patients, anthropometric measures extracted by deep learning were normalized to the body surface area (BSA) to control the various patient morphologies. VAT/SAT ratio and clinical parameters were included in a Cox proportional-hazards model for progression-free survival (PFS) and overall survival (OS). RESULTS Inference time for a whole volume was about 3 s. Mean Dice similarity coefficients in the validation set were 0.95, 0.93, and 0.91 for SAT, VAT, and MBM, respectively. For PFS prediction, T-stage, N-stage, chemotherapy, radiation therapy, and VAT/SAT ratio were associated with disease progression on univariate analysis. On multivariate analysis, only N-stage (HR = 1.7 [1.2-2.4]; p = 0.006), radiation therapy (HR = 2.4 [1.0-5.4]; p = 0.04), and VAT/SAT ratio (HR = 10.0 [2.7-37.9]; p < 0.001) remained significant prognosticators. For OS, male gender, smoking status, N-stage, a lower SAT/BSA ratio, and a higher VAT/SAT ratio were associated with mortality on univariate analysis. On multivariate analysis, male gender (HR = 2.8 [1.2-6.7]; p = 0.02), N-stage (HR = 2.1 [1.5-2.9]; p < 0.001), and the VAT/SAT ratio (HR = 7.9 [1.7-37.1]; p < 0.001) remained significant prognosticators. CONCLUSION The BSA-normalized VAT/SAT ratio is an independent predictor of both PFS and OS in NSCLC patients. KEY POINTS • Deep learning will make CT-derived anthropometric measures clinically usable as they are currently too time-consuming to calculate in routine practice. • Whole-body CT-derived anthropometrics in non-small-cell lung cancer are associated with progression-free survival and overall survival. • A priori medical knowledge can be implemented in the neural network loss function calculation.
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Affiliation(s)
- Paul Blanc-Durand
- Department of Nuclear Medicine, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France. .,INSERM IMRB, Team 8, U-PEC, Créteil, F-94000, France. .,Université Paris-Est Créteil (U-PEC), F-94000, Créteil, France.
| | - Luca Campedel
- Department of Oncology, Groupe Hospitalier Pitié Salpêtrière C. Foix/AP-HP, Paris, F-75013, France
| | - Sébastien Mule
- Université Paris-Est Créteil (U-PEC), F-94000, Créteil, France.,Department of Radiology, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France
| | | | - Alain Luciani
- Université Paris-Est Créteil (U-PEC), F-94000, Créteil, France.,Department of Radiology, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France
| | - Frédéric Pigneur
- Department of Radiology, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France
| | - Emmanuel Itti
- Department of Nuclear Medicine, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France.,INSERM IMRB, Team 8, U-PEC, Créteil, F-94000, France.,Université Paris-Est Créteil (U-PEC), F-94000, Créteil, France
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22
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Paris MT. Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning. Lifestyle Genom 2019; 13:28-31. [PMID: 31822001 DOI: 10.1159/000503996] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/04/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provide an excellent opportunity to precisely measure the quantity and quality of skeletal muscle and adipose tissue. However, manual analysis of CT scans is costly and time-intensive, limiting the widespread adoption of CT-based measurements of body composition. SUMMARY Advances in deep learning have demonstrated excellent success in biomedical image analysis. Several recent publications have demonstrated excellent accuracy in comparison to human raters for the measurement of skeletal muscle, visceral adipose, and subcutaneous adipose tissue from the lumbar vertebrae region, indicating that analysis of body composition may be successfully automated using deep neural networks. Key Messages: The high accuracy and drastically improved speed of CT body composition analysis (<1 s/scan for neural networks vs. 15 min/scan for human analysis) suggest that neural networks may aid researchers and clinicians in better understanding the role of body composition in clinical populations by enabling cost-effective, large-scale research studies. As the role of body composition in clinical settings and the field of automated analysis advance, it will be critical to examine how clinicians interact with these systems and to evaluate whether these technologies are beneficial in improving treatment and health outcomes for patients.
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Affiliation(s)
- Michael T Paris
- Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada,
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