Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Insights into Imaging
Published in: Insights into Imaging 1/2023

Open Access 01-12-2023 | NSCLC | Original Article

Prognostic utility of body composition parameters based on computed tomography analysis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors

Authors: Ji Eun Park, Jaemin Jo, Jeonghwan Youk, Miso Kim, Soon Ho Yoon, Bhumsuk Keam, Tae Min Kim, Dong-Wan Kim

Published in: Insights into Imaging | Issue 1/2023

Login to get access

Abstract

Objective

The purpose of this study was to evaluate the prognostic impact of body composition parameters based on computed tomography (CT) in patients with non-small cell lung cancer (NSCLC) who received ICI treatment.

Methods

This retrospective study analyzed the data from advanced NSCLC patients treated with ICI therapy between 2013 and 2019. We included patients with NSCLC who underwent baseline CT scans. The exclusion criteria included patients who received three or more lines of chemotherapy, those with insufficient clinical information, or those without treatment response evaluation.

Results

A total of 136 patients were enrolled. Among the volumetric body composition parameters, patients in the highest quartiles (Q2–4) of the visceral fat index (VFI) exhibited a higher response rate to ICI therapy than those in the lowest quartile (Q1) of VFI (Q1 vs. Q2–4: 18.2% vs. 43.1%, p = 0.012). Patients with a VFI in Q2–4 had significantly prolonged progression-free survival (PFS) and overall survival (OS) (PFS, Q1 vs. Q2–4: 3.0 months vs. 6.4 months, p = 0.043; OS, Q1 vs. Q2–4: 5.6 months vs. 16.3 months, p = 0.004). Kaplan–Meier analysis based on the VFI and visceral fat Hounsfield unit (HU) revealed that patients with VFI in Q1 and HU in Q2–4 had the worst prognosis.

Conclusions

Visceral fat volume is significantly associated with treatment outcomes in ICI-treated patients with NSCLC. Moreover, fat quality may impact the treatment outcomes. This finding underscores the potential significance of both fat compartments and fat quality as prognostic indicators.

Critical relevance statement

Visceral fat volume is significantly associated with treatment outcomes in ICI-treated patients with non-small cell lung cancer. Moreover, fat quality may impact the treatment outcomes. This finding underscores the potential significance of both fat compartments and fat quality as prognostic indicators.

Graphical Abstract

Key points

• We found that visceral fat volume positively correlated with treatment response and survival in patients with non-small cell lung cancer receiving immune checkpoint inhibitors.
• Additionally, a trend toward a negative correlation between visceral fat attenuation and survival was observed.
• The findings highlight the prognostic utility of fat compartments and fat quality.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–49CrossRefPubMed
2.
Horn L, Spigel DR, Vokes EE et al (2017) Nivolumab versus docetaxel in previously treated patients with advanced non-small-cell lung cancer: two-year outcomes from two randomized, open-label, phase III trials (CheckMate 017 and CheckMate 057). J Clin Oncol 35(35):3924–33PubMedPubMedCentral
3.
Borghaei H, Paz-Ares L, Horn L et al (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med 373(17):1627–39PubMedPubMedCentral
4.
Chen P-L, Roh W, Reuben A et al (2016) Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov 6(8):827–37PubMedPubMedCentral
5.
O’Donnell JS, Teng MWL, Smyth MJ (2019) Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol 16(3):151–67PubMed
6.
Cohen R, Duval A, Svrcek M (2019) Primary resistance to immune checkpoint inhibitors in metastatic colorectal cancer-beyond the misdiagnosis-in reply. JAMA Oncol 5(5):741PubMed
7.
Horvath L, Thienpont B, Zhao L, Wolf D, Pircher A (2020) Overcoming immunotherapy resistance in non-small cell lung cancer (NSCLC) - novel approaches and future outlook. Mol Cancer 19(1):141PubMedPubMedCentral
8.
Walsh RJ, Soo RA (2020) Resistance to immune checkpoint inhibitors in non-small cell lung cancer: biomarkers and therapeutic strategies. Ther Adv Med Oncol 12:1758835920937902PubMedPubMedCentral
9.
Bruno TC (2020) New predictors for immunotherapy responses sharpen our view of the tumour microenvironment. Nature 577(7791):474–6PubMedPubMedCentral
10.
Xiao Q, Nobre A, Piñeiro P et al (2020) Genetic and epigenetic biomarkers of immune checkpoint blockade response. J Clin Med 9(1):286PubMedPubMedCentral
11.
Lee PC, Chao Y, Chen MH et al (2020) Predictors of response and survival in immune checkpoint inhibitor-treated unresectable hepatocellular carcinoma. Cancers (Basel) 12(1):182PubMed
12.
13.
Conforti F, Pala L, Bagnardi V et al (2018) Cancer immunotherapy efficacy and patients’ sex: a systematic review and meta-analysis. Lancet Oncol 19(6):737–46PubMed
14.
Mojibi Y, Seif F, Mojibi N, Aghamajidi A, Mohsenzadegan M, Torang HA (2022) Efficacy of immunotherapy in obese patients with cancer. Immunopharmacol Immunotoxicol 44(4):471–83PubMed
15.
Erbe R, Wang Z, Wu S et al (2021) Evaluating the impact of age on immune checkpoint therapy biomarkers. Cell Reports 36(8):109599PubMed
16.
Cortellini A, Bersanelli M, Buti S et al (2019) A multicenter study of body mass index in cancer patients treated with anti-PD-1/PD-L1 immune checkpoint inhibitors: when overweight becomes favorable. J Immunother Cancer 7(1):57PubMedPubMedCentral
17.
McQuade JL, Daniel CR, Hess KR et al (2018) Association of body-mass index and outcomes in patients with metastatic melanoma treated with targeted therapy, immunotherapy, or chemotherapy: a retrospective, multicohort analysis. Lancet Oncol 19(3):310–22PubMedPubMedCentral
18.
De Giorgi U, Procopio G, Giannarelli D et al (2019) Association of systemic inflammation index and body mass index with survival in patients with renal cell cancer treated with nivolumab. Clin Cancer Res 25(13):3839–46PubMed
19.
Martini DJ, Kline MR, Liu Y et al (2020) Adiposity may predict survival in patients with advanced stage cancer treated with immunotherapy in phase 1 clinical trials. Cancer 126(3):575–82PubMed
20.
Cortellini A, Bersanelli M, Santini D et al (2020) Another side of the association between body mass index (BMI) and clinical outcomes of cancer patients receiving programmed cell death protein-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) checkpoint inhibitors: a multicentre analysis of immune-related adverse events. Eur J Cancer 128:17–26PubMed
21.
Wang Z, Aguilar EG, Luna JI et al (2019) Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat Med 25(1):141–51PubMed
22.
Ichihara E, Harada D, Inoue K et al (2020) The impact of body mass index on the efficacy of anti-PD-1/PD-L1 antibodies in patients with non-small cell lung cancer. Lung Cancer 139:140–5PubMed
23.
Popinat G, Cousse S, Goldfarb L et al (2019) Sub-cutaneous fat mass measured on multislice computed tomography of pretreatment PET/CT is a prognostic factor of stage IV non-small cell lung cancer treated by nivolumab. Oncoimmunology 8(5):e1580128-e
24.
Magri V, Gottfried T, Di Segni M et al (2019) Correlation of body composition by computerized tomography and metabolic parameters with survival of nivolumab-treated lung cancer patients. Cancer Manag Res 11:8201–7PubMedPubMedCentral
25.
Vithayathil M, D’Alessio A, Fulgenzi CAM et al (2023) Impact of body mass index in patients receiving atezolizumab plus bevacizumab for hepatocellular carcinoma. Hepatol Int 17(4):904–14PubMed
26.
Yu D, Zheng W, Johansson M et al (2018) Overall and central obesity and risk of lung cancer: a pooled analysis. J Natl Cancer Inst 110(8):831–42PubMedPubMedCentral
27.
Romero-Corral A, Somers VK, Sierra-Johnson J et al (2008) Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond) 32(6):959–66PubMed
28.
Song X, Jousilahti P, Stehouwer CDA et al (2013) Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr 67(12):1298–302PubMed
29.
Elhakim T, Trinh K, Mansur A, Bridge C, Daye D (2023) Role of machine learning-based CT body composition in risk prediction and prognostication: current state and future directions. Diagnostics (Basel) 13(5):968PubMed
30.
Tolonen A, Pakarinen T, Sassi A et al (2021) Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review. Eur J Radiol 145:109943PubMed
31.
Lee YS, Hong N, Witanto JN et al (2021) Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment. Clin Nutr 40(8):5038–46PubMed
32.
Ross R, Neeland IJ, Yamashita S et al (2020) Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR Working Group on Visceral Obesity. Nature Reviews Endocrinology 16(3):177–89PubMedPubMedCentral
33.
Kim BY, Kang SM, Kang JH et al (2021) 2020 Korean society for the study of obesity guidelines for the management of obesity in Korea. J Obes Metab Syndr 30(2):81–92PubMedPubMedCentral
34.
Khaddour K, Gomez-Perez SL, Jain N, Patel JD, Boumber Y (2020) Obesity, sarcopenia, and outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors and tyrosine kinase inhibitors. Front Oncol 10:576314PubMedPubMedCentral
35.
Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M (2019) Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism 92:121–35PubMed
36.
Lee DH, Giovannucci EL (2019) The obesity paradox in cancer: epidemiologic insights and perspectives. Curr Nutr Rep 8(3):175–81PubMed
37.
Sinicrope FA, Foster NR, Yothers G et al (2013) Body mass index at diagnosis and survival among colon cancer patients enrolled in clinical trials of adjuvant chemotherapy. Cancer 119(8):1528–36PubMed
38.
Wong AL, Seng KY, Ong EM et al (2014) Body fat composition impacts the hematologic toxicities and pharmacokinetics of doxorubicin in Asian breast cancer patients. Breast Cancer Res Treat 144(1):143–52PubMed
39.
Woodall MJ, Neumann S, Campbell K, Pattison ST, Young SL (2020) The effects of obesity on anti-cancer immunity and cancer immunotherapy. Cancers (Basel) 12(5):1230PubMed
40.
Donnelly D, Bajaj S, Yu J et al (2019) The complex relationship between body mass index and response to immune checkpoint inhibition in metastatic melanoma patients. J Immunother Cancer 7(1):222PubMedPubMedCentral
41.
Takemura K, Yonekura S, Downey LE, Evangelopoulos D, Heng DYC (2022) Impact of body mass index on survival outcomes of patients with metastatic renal cell carcinoma in the immuno-oncology era: a systematic review and meta-analysis. Eur Urol Open Sci 39:62–71PubMedPubMedCentral
42.
Imai H, Naito E, Yamaguchi O et al (2022) Pretreatment body mass index predicts survival among patients administered nivolumab monotherapy for pretreated non-small cell lung cancer. Thorac Cancer 13(10):1479–89PubMedPubMedCentral
43.
Nie RC, Chen GM, Wang Y et al (2021) Association between body mass index and survival outcomes in patients treated with immune checkpoint inhibitors: meta-analyses of individual patient data. J Immunother 44(9):371–5PubMedPubMedCentral
44.
Wang Z, Monjazeb AM, Murphy WJ (2019) The complicated effects of obesity on cancer and immunotherapy. Immunotherapy 11(1):11–4PubMed
45.
Prado CM, Lieffers JR, McCargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9(7):629–35PubMed
46.
Martin L, Birdsell L, Macdonald N et al (2013) Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 31(12):1539–47PubMed
47.
Donohoe CL, Doyle SL, Reynolds JV (2011) Visceral adiposity, insulin resistance and cancer risk. Diabetol Metab Syndr 3:12PubMedPubMedCentral
48.
Khan A, Welman CJ, Abed A et al (2023) Association of computed tomography measures of muscle and adipose tissue and progressive changes throughout treatment with clinical endpoints in patients with advanced lung cancer treated with immune checkpoint inhibitors. Cancers (Basel) 15(5):1382PubMed
49.
Crombé A, Kind M, Toulmonde M, Italiano A, Cousin S (2020) Impact of CT-based body composition parameters at baseline, their early changes and response in metastatic cancer patients treated with immune checkpoint inhibitors. Eur J Radiol 133:109340PubMed
50.
Wolfs MG, Rensen SS, Bruin-Van Dijk EJ et al (2010) Co-expressed immune and metabolic genes in visceral and subcutaneous adipose tissue from severely obese individuals are associated with plasma HDL and glucose levels: a microarray study. BMC Med Genomics 3:34PubMedPubMedCentral
51.
Spoto B, Di Betta E, Mattace-Raso F et al (2014) Pro- and anti-inflammatory cytokine gene expression in subcutaneous and visceral fat in severe obesity. Nutr Metab Cardiovasc Dis 24(10):1137–43PubMed
52.
Rosenquist KJ, Massaro JM, Pedley A et al (2015) Fat quality and incident cardiovascular disease, all-cause mortality, and cancer mortality. J Clin Endocrinol Metab 100(1):227–34PubMed
53.
Li S, Wang T, Tong G, Li X, You D, Cong M (2021) Prognostic impact of sarcopenia on clinical outcomes in malignancies treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Front Oncol 11:726257
54.
Bernardi L, Roesel R, Vagelli F, Majno-Hurst P, Cristaudi A (2022) Imaging based body composition profiling and outcomes after oncologic liver surgery. Front Oncol 12:1007771
Metadata
Title
Prognostic utility of body composition parameters based on computed tomography analysis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors
Authors
Ji Eun Park
Jaemin Jo
Jeonghwan Youk
Miso Kim
Soon Ho Yoon
Bhumsuk Keam
Tae Min Kim
Dong-Wan Kim
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01532-4

Other articles of this Issue 1/2023

Insights into Imaging 1/2023 Go to the issue

Original Article

Three-dimensional multifrequency magnetic resonance elastography improves preoperative assessment of proliferative hepatocellular carcinoma

Original Article

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Original Article

Complications of cochlear implants with MRI scans in different body regions: type, frequency and impact

Original Article

Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features

Original Article

Anatomical variation of the sacroiliac joints: an MRI study with synthetic CT images

Statement

ESR paper on structured reporting in radiology—update 2023