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Brath MSG, Sahakyan M, Mark EB, Rasmussen HH, Østergaard LR, Frøkjær JB, Weinreich UM, Jørgensen ME. Ethnic differences in CT derived abdominal body composition measures: a comparative retrospect pilot study between European and Inuit study population. Int J Circumpolar Health 2024; 83:2312663. [PMID: 38314517 PMCID: PMC10846476 DOI: 10.1080/22423982.2024.2312663] [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: 11/09/2023] [Accepted: 01/28/2024] [Indexed: 02/06/2024] Open
Abstract
Understanding ethnic variations in body composition is crucial for assessing health risks. Universal models may not suit all ethnicities, and there is limited data on the Inuit population. This study aimed to compare body composition between Inuit and European adults using computed tomography (CT) scans and to investigate the influence of demographics on these measurements. A retrospective analysis was conducted on 50 adults (29 Inuit and 21 European) who underwent standard trauma CT scans. Measurements focused on skeletal muscle index (SMI), various fat indices, and densities at the third lumbar vertebra level, analyzed using the Wilcoxon-Mann-Whitney test and multiple linear regression. Inuit women showed larger fat tissue indices and lower muscle and fat densities than European women. Differences in men were less pronouncehd, with only Intramuscular fat density being lower among Inuit men. Regression indicated that SMI was higher among men, and skeletal muscle density decreased with Inuit ethnicity and age, while visceral fat index was positively associated with age. This study suggests ethnic differences in body composition measures particularly among women, and indicates the need for Inuit-specific body composition models. It higlights the importance of further research into Inuit-specific body composition measurements for better health risk assessment.
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Affiliation(s)
- Mia Solholt Godthaab Brath
- Department of Respiratory Medicine, Aalborg University Hospital, Aalborg, Denmark
- Respiratory Research Aalborg, Reaal, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Marina Sahakyan
- Department of Radiology, Aalborg University Hospital, Aalborg, Denmark
| | - Esben Bolvig Mark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Mech-Sense, Department. of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Henrik Højgaard Rasmussen
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Danish Nutrition Science Center, Department. of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
- Center for Nutrition and Intestinal Failure, Department. of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
- The Dietitians and Nutritional Research Unit, EATEN, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
| | - Lasse Riis Østergaard
- Medical Informatics group, Department. of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Jens Brøndum Frøkjær
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Radiology, Aalborg University Hospital, Aalborg, Denmark
| | - Ulla Møller Weinreich
- Department of Respiratory Medicine, Aalborg University Hospital, Aalborg, Denmark
- Respiratory Research Aalborg, Reaal, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Marit Eika Jørgensen
- Clinical Epidemiology, Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Health and Nature, University of Greenland, Nuuk, Greenland
- Steno Diabetes Center Greenland, Queen Ingrid’s Hospital, Nuuk, Greenland
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Querido NR, Bours MJL, Brecheisen R, Valkenburg-van Iersel L, Breukink SO, Janssen-Heijnen MLG, Keulen ETP, Konsten JLM, de Vos-Geelen J, Weijenberg MP, Simons CCJM. Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images. Clin Nutr ESPEN 2024; 63:659-667. [PMID: 39098602 DOI: 10.1016/j.clnesp.2024.07.1054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 07/17/2024] [Accepted: 07/27/2024] [Indexed: 08/06/2024]
Abstract
BACKGROUND & AIMS Several automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation. METHODS Diagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement. RESULTS A total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900 to 0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement. CONCLUSIONS AutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.
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Affiliation(s)
- Nadira R Querido
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.
| | - Martijn J L Bours
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Ralph Brecheisen
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Liselot Valkenburg-van Iersel
- Department of Internal Medicine, Division of Medical Oncology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Stephanie O Breukink
- Department of Surgery, GROW Research Institute for Oncology and Reproduction, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Maryska L G Janssen-Heijnen
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands; Department of Clinical Epidemiology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Eric T P Keulen
- Department of Internal Medicine and Gastroenterology, Zuyderland Medical Centre Sittard- Geleen, Geleen, the Netherlands
| | - Joop L M Konsten
- Department of Surgery, VieCuri Medical Centre, Venlo, the Netherlands
| | - Judith de Vos-Geelen
- Department of Internal Medicine, Division of Medical Oncology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Matty P Weijenberg
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Colinda C J M Simons
- Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
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Bai LN, Zhang LX. Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer. World J Gastrointest Surg 2024; 16:2135-2144. [PMID: 39087125 PMCID: PMC11287686 DOI: 10.4240/wjgs.v16.i7.2135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/11/2024] [Accepted: 06/04/2024] [Indexed: 07/22/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is a prevalent cancer type in clinical settings; its early signs can be difficult to detect, which often results in late-stage diagnoses in many patients. The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates. Recently, imaging techniques have been hypothesized to be essential in managing CRC, with magnetic resonance imaging (MRI) and spiral computed tomography (SCT) playing a significant role in enhancing diagnostic and treatment approaches. AIM To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment. METHODS Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC. The precision of MRI and SCT for the presurgical classification of CRC was assessed, and pathological staging was used as a reference. Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume, blood flow, time to peak, permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction on the prognosis of patients with CRC. RESULTS Pathological biopsies confirmed the following CRC stages: 23, 23, 32, and 17 at T1, T2, T3, and T4, respectively. There were 39 cases at the N0 stage, 22 at N1, 34 at N2, 44 at M0 stage, and 51 at M1. Using pathological findings as the benchmark, the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity, specificity, and accuracy compared with either modality alone, with a statistically significant difference in accuracy (P < 0.05). Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis, as indicated by the areas under the curve for blood volume, blood flow, time to peak, and permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction were 0.750, 0.683, 0.772, 0.761, 0.709, 0.719, and 0.910, respectively. The corresponding sensitivity and specificity values were also obtained (P < 0.05). CONCLUSION MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.
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Affiliation(s)
- Lu-Na Bai
- Department of Radiology, Zhongshan Hospital Xiamen University, Xiamen 361004, Fujian Province, China
| | - Lu-Xian Zhang
- Department of Radiology, Zhongshan Hospital Xiamen University, Xiamen 361004, Fujian Province, China
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Thai ST, Lund JL, Poole C, Buse JB, Stürmer T, Harmon CA, Al-Obaidi M, Williams GR. Skeletal muscle density performance for screening frailty in older adults with cancer and the impact of diabetes: The CARE Registry. J Geriatr Oncol 2024; 15:101815. [PMID: 38896951 PMCID: PMC11346769 DOI: 10.1016/j.jgo.2024.101815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
INTRODUCTION Skeletal muscle density (SMD) measurements from imaging scans identify myosteatosis and could screen patients for geriatric assessment. We assessed SMD performance as a screening tool to identify older adults with cancer likely to be frail and who could benefit from in-depth assessment; we compared performance by sex and diabetes status. MATERIALS AND METHODS We analyzed patients in the Cancer & Aging Resilience Evaluation (CARE) Registry. Frailty and diabetes were captured using a patient-reported geriatric assessment (CARE tool). Frailty was defined using CARE frailty index (CARE-FI) based on principles of deficit accumulation. SMD was calculated from computed tomography scans (L3 vertebrae). Analyses were conducted by sex and diabetes status. Scatterplots and linear regression described crude associations between SMD and frailty score. Classification performance (frail vs. non-frail) was analyzed with (1) area under the receiver operating characteristic curves (AUC) and confidence intervals (CIs); and (2) sensitivity/specificity for sex-specific SMD quartile cut-offs (Q1, median, Q3). Performance was compared between patients with and without diabetes using differences and estimated CIs (2000 bootstrap replicates). We additionally calculated positive and negative likelihood ratios (LR+, LR-). RESULTS The analytic cohort included 872 patients (39% female, median age 68 years, 27% with diabetes) with predominately stage III/IV gastrointestinal cancer; >60% planning to initiate first-line chemotherapy. SMD was negatively associated with frailty score; models were best fit in male patients with diabetes. AUC estimates for female (range: 0.58-0.62) and male (0.58-0.68) patients were low. Q3 cut-offs had high sensitivity (range: 0.76-0.89), but poor specificity (0.25-0.34). Diabetes did not impact estimates for female patients. Male patients with diabetes had greater sensitivity estimates compared to those without (sensitivity differences: 0.23 [0.07, 0.38], 0.08 [-0.07, 0.24], and 0.11 [0.00, 0.22] for Q1, median, Q3, respectively). LR estimates were most notable for male patients with diabetes (LR+ = 2.92, Q1 cut-off; LR- = 0.46, Q3 cut-off). DISCUSSION Using SMD alone to screen older patients for geriatric assessment requires improvement. High-sensitivity cut-off points could miss 11-24% of patients with frailty, and many non-frail patients may be flagged. Screening with SMD is practical but work is needed to understand clinical andresource impacts of different cut-off points. Future research should evaluate performance with additional clinical data and in subgroups.
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Affiliation(s)
- Sydney T Thai
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jennifer L Lund
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles Poole
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John B Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christian A Harmon
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mustafa Al-Obaidi
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Grant R Williams
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, USA; Division of Hematology/Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
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Brown LR, Sousa MS, Yule MS, Baracos VE, McMillan DC, Arends J, Balstad TR, Bye A, Dajani O, Dolan RD, Fallon MT, Greil C, Hjermstad MJ, Jakobsen G, Maddocks M, McDonald J, Ottestad IO, Phillips I, Sayers J, Simpson MR, Vagnildhaug OM, Solheim TS, Laird BJ, Skipworth RJ. Body weight and composition endpoints in cancer cachexia clinical trials: Systematic Review 4 of the cachexia endpoints series. J Cachexia Sarcopenia Muscle 2024; 15:816-852. [PMID: 38738581 PMCID: PMC11154800 DOI: 10.1002/jcsm.13478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/12/2024] [Accepted: 03/16/2024] [Indexed: 05/14/2024] Open
Abstract
Significant variation exists in the outcomes used in cancer cachexia trials, including measures of body composition, which are often selected as primary or secondary endpoints. To date, there has been no review of the most commonly selected measures or their potential sensitivity to detect changes resulting from the interventions being examined. The aim of this systematic review is to assess the frequency and diversity of body composition measures that have been used in cancer cachexia trials. MEDLINE, Embase and Cochrane Library databases were systematically searched between January 1990 and June 2021. Eligible trials examined adults (≥18 years) who had received an intervention aiming to treat or attenuate the effects of cancer cachexia for >14 days. Trials were also of a prospective controlled design and included body weight or at least one anthropometric, bioelectrical or radiological endpoint pertaining to body composition, irrespective of the modality of intervention (e.g., pharmacological, nutritional, physical exercise and behavioural) or comparator. Trials with a sample size of <40 patients were excluded. Data extraction used Covidence software, and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance. This review was prospectively registered (PROSPERO: CRD42022276710). A total of 84 clinical trials, comprising 13 016 patients, were eligible for inclusion. Non-small-cell lung cancer and pancreatic cancer were studied most frequently. The majority of trial interventions were pharmacological (52%) or nutritional (34%) in nature. The most frequently reported endpoints were assessments of body weight (68 trials, n = 11 561) followed by bioimpedance analysis (BIA)-based estimates (23 trials, n = 3140). Sixteen trials (n = 3052) included dual-energy X-ray absorptiometry (DEXA)-based endpoints, and computed tomography (CT) body composition was included in eight trials (n = 841). Discrepancies were evident when comparing the efficacy of interventions using BIA-based estimates of lean tissue mass against radiological assessment modalities. Body weight, BIA and DEXA-based endpoints have been most frequently used in cancer cachexia trials. Although the optimal endpoints cannot be determined from this review, body weight, alongside measurements from radiological body composition analysis, would seem appropriate. The choice of radiological modality is likely to be dependent on the trial setting, population and intervention in question. CT and magnetic resonance imaging, which have the ability to accurately discriminate tissue types, are likely to be more sensitive and provide greater detail. Endpoints are of particular importance when aligned with the intervention's mechanism of action and/or intended patient benefit.
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Affiliation(s)
- Leo R. Brown
- Clinical SurgeryThe University of Edinburgh, Royal Infirmary of EdinburghEdinburghUK
| | - Mariana S. Sousa
- Improving Palliative, Aged and Chronic Care Through Clinical Research and Translation (IMPACCT)University of Technology SydneySydneyAustralia
| | - Michael S. Yule
- Clinical SurgeryThe University of Edinburgh, Royal Infirmary of EdinburghEdinburghUK
- Institute of Genetics and CancerThe University of Edinburgh, Western General HospitalEdinburghUK
- St Columba's Hospice CareEdinburghUK
| | | | - Donald C. McMillan
- Academic Unit of SurgeryUniversity of Glasgow, Glasgow Royal InfirmaryGlasgowUK
| | - Jann Arends
- Department of Medicine I, Medical Centre—University of Freiburg Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Trude R. Balstad
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Department of Clinical Medicine, Clinical Nutrition Research GroupUiT The Arctic University of NorwayTromsøNorway
| | - Asta Bye
- Department of OncologyOslo University HospitalOsloNorway
- Department of Nursing and Health Promotion, Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Olav Dajani
- Department of OncologyOslo University HospitalOsloNorway
| | - Ross D. Dolan
- Academic Unit of SurgeryUniversity of Glasgow, Glasgow Royal InfirmaryGlasgowUK
| | - Marie T. Fallon
- Institute of Genetics and CancerThe University of Edinburgh, Western General HospitalEdinburghUK
- St Columba's Hospice CareEdinburghUK
| | - Christine Greil
- Department of Medicine I, Medical Centre—University of Freiburg Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | | | - Gunnhild Jakobsen
- Department of Public Health and Nursing, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Cancer ClinicSt. Olav's Hospital, Trondheim University HospitalTrondheimNorway
| | - Matthew Maddocks
- Cicely Saunders Institute of Palliative Care, Policy and RehabilitationKing's College LondonLondonUK
| | - James McDonald
- Institute of Genetics and CancerThe University of Edinburgh, Western General HospitalEdinburghUK
- St Columba's Hospice CareEdinburghUK
| | - Inger O. Ottestad
- Department of Nutrition, Institute of Basic Medical SciencesUniversity of OsloOsloNorway
- The Clinical Nutrition Outpatient Clinic, Section of Clinical Nutrition, Department of Clinical Service, Division of Cancer MedicineOslo University HospitalOsloNorway
| | - Iain Phillips
- Edinburgh Cancer CentreWestern General HospitalEdinburghUK
| | - Judith Sayers
- Clinical SurgeryThe University of Edinburgh, Royal Infirmary of EdinburghEdinburghUK
- Institute of Genetics and CancerThe University of Edinburgh, Western General HospitalEdinburghUK
- St Columba's Hospice CareEdinburghUK
| | - Melanie R. Simpson
- Department of Nursing and Health Promotion, Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Ola M. Vagnildhaug
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Department of Public Health and Nursing, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Tora S. Solheim
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Department of Public Health and Nursing, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Barry J.A. Laird
- Institute of Genetics and CancerThe University of Edinburgh, Western General HospitalEdinburghUK
- St Columba's Hospice CareEdinburghUK
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Feng T, Hu S, Song C, Zhong M. Establishment of a novel weight reduction model after laparoscopic sleeve gastrectomy based on abdominal fat area. Front Surg 2024; 11:1390045. [PMID: 38826810 PMCID: PMC11140024 DOI: 10.3389/fsurg.2024.1390045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/17/2024] [Indexed: 06/04/2024] Open
Abstract
In light of ongoing research elucidating the intricacies of obesity and metabolic syndrome, the role of abdominal fat (especially visceral fat) has been particularly prominent. Studies have revealed that visceral adipose tissue can accelerate the development of metabolic syndrome by releasing various bioactive compounds and hormones, such as lipocalin, leptin and interleukin. A retrospective analysis was performed on the clinical data of 167 patients with obesity. Among them, 105 patients who satisfied predefined inclusion and exclusion criteria were included. The parameters evaluated included total abdominal fat area (TAFA), laboratory indicators and anthropometric measurements. Weight reduction was quantified through percent total weight loss (%TWL) and percent excess weight loss (%EWL) postoperatively. Binary logistic regression analysis and receiver operating characteristic (ROC) curve analysis were employed to identify predictors of weight loss. Binary logistic regression analysis emphasized that total abdominal fat area was an independent predictor of %EWL ≥75% (p < 0.001). Total abdominal fat area (p = 0.033) and BMI (p = 0.003) were independent predictors of %TWL ≥30%. In our cohort, %TWL ≥30% at 1 year after surgery was closely related to the abdominal fat area and BMI. Based on these results, we formulated a novel model based on these factors, exhibiting superior predictive value for excellent weight loss.
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Affiliation(s)
- Tianyi Feng
- Department of General Surgery, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Sanyuan Hu
- Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
| | - Changrong Song
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
| | - Mingwei Zhong
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
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Yao L, Petrosyan A, Chaudhari AJ, Lenchik L, Boutin RD. Clinical, functional, and opportunistic CT metrics of sarcopenia at the point of imaging care: analysis of all-cause mortality. Skeletal Radiol 2024; 53:515-524. [PMID: 37684434 PMCID: PMC10841085 DOI: 10.1007/s00256-023-04438-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 08/23/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
PURPOSE This study examines clinical, functional, and CT metrics of sarcopenia and all-cause mortality in older adults undergoing outpatient imaging. METHODS The study included outpatients ≥ 65 years of age undergoing CT or PET/CT at a tertiary care institution. Assessments included screening questionnaires for sarcopenia (SARC-F) and frailty (FRAIL scale), and measurements of grip strength and usual gait speed (6 m course). Skeletal muscle area (SMA), index (SMI, area/height2) and density (SMD) were measured on CT at T12 and L3. A modified SMI was also examined (SMI-m, area/height). Mortality risk was studied with Cox proportional hazard analysis. RESULTS The study included 416 patients; mean age 73.8 years [sd 6.2]; mean follow-up 2.9 years (sd 1.34). Abnormal grip, SARC-F, and FRAIL scale assessments were associated with higher mortality risk (HR [95%CI] = 2.0 [1.4-2.9], 1.6 [1.1-2.3], 2.0 [1.4-2.8]). Adjusting for age, higher L3-SMA, T12-SMA, T12-SMI and T12-SMI-m were associated with lower mortality risk (HR [95%CI] = 0.80 [0.65-0.90], 0.76 [0.64-0.90], 0.84 [0.70-1.00], and 0.80 [0.67-0.90], respectively). T12-SMD and L3-SMD were not predictive of mortality. After adjusting for abnormal grip strength and FRAIL scale assessments, T12-SMA and T12-SMI-m remained predictive of mortality risk (HR [95%CI] = 0.83 [0.70-1.00] and 0.80 [0.67-0.97], respectively). CONCLUSION CT areal metrics were weaker predictors of all-cause mortality than clinical and functional metrics of sarcopenia in our older patient cohort; a CT density metric (SMD) was not predictive. Of areal CT metrics, SMI (area/height2) appeared to be less effective than non-normalized SMA or SMA normalized by height1.
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Affiliation(s)
- Lawrence Yao
- Radiology and Imaging Sciences/CC/NIH, 10 Center Drive, Bethesda, MD, 20892, USA.
| | | | - Abhijit J Chaudhari
- University of California, Davis 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA
| | - Leon Lenchik
- Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Robert D Boutin
- Stanford University School of Medicine, 300 Pasteur Drive, MC-5105, Stanford, CA, 94305, USA
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Chang CY, Lenchik L, Blankemeier L, Chaudhari AS, Boutin RD. Biomarkers of Body Composition. Semin Musculoskelet Radiol 2024; 28:78-91. [PMID: 38330972 DOI: 10.1055/s-0043-1776430] [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: 02/10/2024]
Abstract
The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.
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Affiliation(s)
- Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Leon Lenchik
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Akshay S Chaudhari
- Department of Radiology and of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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9
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Lu F, Fan J, Li F, Liu L, Chen Z, Tian Z, Zuo L, Yu D. Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms. Abdom Radiol (NY) 2024; 49:560-574. [PMID: 37847262 DOI: 10.1007/s00261-023-04062-1] [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: 07/20/2023] [Revised: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 10/18/2023]
Abstract
Diabetic kidney disease (DKD) is a significant healthcare burden worldwide that substantially increases the risk of kidney failure and cardiovascular events. To reduce the prevalence of DKD, extensive research is being conducted to determine the risk factors and consequently implement early interventions. Patients with type 2 diabetes mellitus (T2DM) are more likely to be obese. Abdominal adiposity is associated with a greater risk of kidney damage than general obesity. Abdominal adipose tissue can be divided into different fat depots according to the location and function, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PAT), and renal sinus adipose tissue (RSAT), which can be accurately measured by radiology techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). Abdominal fat depots may affect the development of DKD through different mechanisms, and radiologic abdominal adipose characteristics may serve as imaging indicators of DKD risk. This review will first describe the CT/MRI-based assessment of abdominal adipose depots and subsequently describe the current studies on abdominal adipose tissue and DKD development, as well as the underlying mechanisms in patients of T2DM with DKD.
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Affiliation(s)
- Fei Lu
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Jinlei Fan
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Fangxuan Li
- Department of Ultrasound, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Lijing Liu
- Department of Imaging, Yantaishan Hospital, Yantai, 264001, Shandong, China
| | - Zhiyu Chen
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Ziyu Tian
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Liping Zuo
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Dexin Yu
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China.
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
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10
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Hess DL, Harmon C, Bhatia S, Williams GR, Giri S. SARC-F as a screening tool to detect computed tomography-based sarcopenia and myosteatosis among older adults with cancer. Cancer Med 2023; 12:20690-20698. [PMID: 37916460 PMCID: PMC10709718 DOI: 10.1002/cam4.6599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/04/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND The European Working Group on Sarcopenia in Older People (EWGSOP) recommends SARC-F as a tool for identifying sarcopenia among older adults. However, the role of SARC-F among older adults with cancer remains unexplored. We aimed to evaluate the diagnostic utility of SARC-F to identify those with sarcopenia, or low muscle mass (using skeletal muscle index [SMI]), and myosteatosis (using skeletal muscle density [SMD]) from computed tomography (CT) imaging and the association of SARC-F with all-cause mortality. METHODS Older adults (≥60 years) presenting for initial consultation at UAB medical oncology clinic who underwent geriatric assessment were enrolled in a prospective cohort study. We identified study participants who completed SARC-F screening and had available CT imaging within 60 days of study enrollment. Using single-slice CT images at the L3 vertebral level, we computed SMI and SMD using published methods. Sarcopenia and myosteatosis were defined using published cutpoints. We calculated the sensitivity and specificity of SARC-F for detecting low muscle mass and low muscle density using published thresholds. Finally, we computed the impact of SARC-F and CT measures on overall survival using Kaplan-Meier curves and Cox regression models, after adjusting for age, sex, cancer type, and cancer stage. RESULTS We identified 212 older adults with a median age of 68.8 years; with 60.8% males, 76.6% whites, and pancreatic cancer (21.2%) being the most common malignancy. In the overall cohort, 30.7% had abnormal SARC-F using published cutpoints. SARC-F ≥ 4 had a sensitivity of 35% and a specificity of 76% to identify low muscle mass. SARC-F ≥ 4 had a sensitivity of 38% and a specificity of 74% to identify low muscle density. Those with SARC-F ≥ 4 and low SMI/SMD had worse survival compared to those with low SMI/SMD alone. Incorporating SARC-F improved survival prognostication beyond SMI and SMD (HR = 3.1; p < 0.001; Harrel's C from 0.73 to 0.76). CONCLUSIONS SARC-F as a screening tool has limited diagnostic utility for identifying older adults with low muscle mass and/or density. However, SARC-F retains prognostic value independent of CT-based muscle measures in predicting mortality among older adults with cancer.
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Affiliation(s)
- Daniel L. Hess
- Department of MedicineUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Christian Harmon
- Institute for Cancer Outcomes and SurvivorshipUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Smita Bhatia
- Institute for Cancer Outcomes and SurvivorshipUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Division of Pediatric Hematology‐Oncology, Department of PediatricsUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Grant R. Williams
- Institute for Cancer Outcomes and SurvivorshipUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Division of Hematology and Oncology, Department of MedicineUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Smith Giri
- Institute for Cancer Outcomes and SurvivorshipUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Division of Hematology and Oncology, Department of MedicineUniversity of Alabama at BirminghamBirminghamAlabamaUSA
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11
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Beavers KM, Avery AE, Shankaran M, Evans WJ, Lynch SD, Dwyer C, Howard M, Beavers DP, Weaver AA, Lenchik L, Cawthon PM. Application of the D 3 -creatine muscle mass assessment tool to a geriatric weight loss trial: A pilot study. J Cachexia Sarcopenia Muscle 2023; 14:2350-2358. [PMID: 37668075 PMCID: PMC10570063 DOI: 10.1002/jcsm.13322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Traditionally, weight loss (WL) trials utilize dual energy X-ray absorptiometry (DXA) to measure lean mass. This method assumes lean mass, as the sum of all non-bone and non-fat tissue, is a reasonable proxy for muscle mass. In contrast, the D3 -creatine (D3 Cr) dilution method directly measures whole body skeletal muscle mass, although this method has yet to be applied in the context of a geriatric WL trial. The purpose of this project was to (1) describe estimates of change and variability in D3 Cr muscle mass in older adults participating in an intentional WL intervention and (2) relate its change to other measures of body composition as well as muscle function and strength. METHODS The INVEST in Bone Health trial (NCT04076618), used as a scaffold for this ancillary pilot project, is a three-armed, 12-month randomized, controlled trial designed to determine the effects of resistance training or weighted vest use during intentional WL on a battery of musculoskeletal health outcomes among 150 older adults living with obesity. A convenience sample of 24 participants (n = 8/arm) are included in this analysis. At baseline and 6 months, participants were weighed, ingested a 30 mg D3 Cr tracer dose, provided a fasted urine sample 3-6 days post-dosage, underwent DXA (total body fat and lean masses, appendicular lean mass) and computed tomography (mid-thigh and trunk muscle/intermuscular fat areas) scans, and performed 400-m walk, stair climb, knee extensor strength, and grip strength tests. RESULTS Participants were older (68.0 ± 4.4 years), mostly White (75.0%), predominantly female (66.7%), and living with obesity (body mass index: 33.8 ± 2.7 kg/m2 ). Six month total body WL was -10.3 (95% confidence interval, CI: -12.7, -7.9) kg. All DXA and computed tomography-derived body composition measures were significantly decreased from baseline, yet D3 Cr muscle mass did not change [+0.5 (95% CI: -2.0, 3.0) kg]. Of muscle function and strength measures, only grip strength significantly changed [+2.5 (95% CI: 1.0, 4.0) kg] from baseline. CONCLUSIONS Among 24 older adults, significant WL with or without weighted vest use or resistance training over a 6-month period was associated with significant declines in all bioimaging metrics, while D3 Cr muscle mass and muscle function and strength were preserved. Treatment assignment for the trial remains blinded; therefore, full interpretation of these findings is limited. Future work in this area will assess change in D3 Cr muscle mass by parent trial treatment group assignment in all study participants.
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Affiliation(s)
- Kristen M. Beavers
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Allison E. Avery
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | | | | | - S. Delanie Lynch
- Department of Biomedical EngineeringWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Caitlyn Dwyer
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Marjorie Howard
- Department of Biostatistics and Data ScienceWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Daniel P. Beavers
- Department of Statistical SciencesWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Ashley A. Weaver
- Department of Biomedical EngineeringWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Leon Lenchik
- Department of RadiologyWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Peggy M. Cawthon
- Research InstituteCalifornia Pacific Medical CenterSan FranciscoCaliforniaUSA
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12
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Shah UA, Ballinger TJ, Bhandari R, Dieli-Conwright CM, Guertin KA, Hibler EA, Kalam F, Lohmann AE, Ippolito JE. Imaging modalities for measuring body composition in patients with cancer: opportunities and challenges. J Natl Cancer Inst Monogr 2023; 2023:56-67. [PMID: 37139984 PMCID: PMC10157788 DOI: 10.1093/jncimonographs/lgad001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/15/2022] [Accepted: 12/30/2022] [Indexed: 05/05/2023] Open
Abstract
Body composition assessment (ie, the measurement of muscle and adiposity) impacts several cancer-related outcomes including treatment-related toxicities, treatment responses, complications, and prognosis. Traditional modalities for body composition measurement include body mass index, body circumference, skinfold thickness, and bioelectrical impedance analysis; advanced imaging modalities include dual energy x-ray absorptiometry, computerized tomography, magnetic resonance imaging, and positron emission tomography. Each modality has its advantages and disadvantages, thus requiring an individualized approach in identifying the most appropriate measure for specific clinical or research situations. Advancements in imaging approaches have led to an abundance of available data, however, the lack of standardized thresholds for classification of abnormal muscle mass or adiposity has been a barrier to adopting these measurements widely in research and clinical care. In this review, we discuss the different modalities in detail and provide guidance on their unique opportunities and challenges.
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Affiliation(s)
- Urvi A Shah
- Department of Medicine, Myeloma Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Tarah J Ballinger
- Department of Medicine, Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN, USA
| | - Rusha Bhandari
- Department of Pediatrics, City of Hope, Duarte, CA, USA
- Department of Population Science, City of Hope, Duarte, CA, USA
| | - Christina M Dieli-Conwright
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin A Guertin
- Department of Public Health Sciences, University of Connecticut Health, Farmington, CT, USA
| | - Elizabeth A Hibler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Faiza Kalam
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ana Elisa Lohmann
- Department of Medical Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Joseph E Ippolito
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA
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13
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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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