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The value of chest CT-derived body composition analysis combined with amide proton transfer-weighted imaging in predicting functional outcomes in patients with acute ischemic stroke
MA Changjun  ZHANG Qinhe  HUANG Yiping  LIU Jiahui  WANG Xiulin  LI Ying  CONG Fengyu  LIU Ailian  LIU Jing  WANG Jiazheng 

Cite this article as: MA C J, ZHANG Q H, HUANG Y P, et al. The value of chest CT-derived body composition analysis combined with amide proton transfer-weighted imaging in predicting functional outcomes in patients with acute ischemic stroke[J]. Chin J Magn Reson Imaging, 2026, 17(9): 5-15. DOI:10.12015/issn.1674-8034.2026.09.002.


[Abstract] Objective To explore the value of chest CT-derived body composition analysis combined with amide proton transfer-weighted (APTw) imaging in assessing the functional prognosis of patients with acute ischemic stroke (AIS).Materials and Methods This prospective study enrolled 108 AIS patients (age 62.96 ± 9.55 years). All patients underwent both head APTw imaging and chest CT scans. Functional outcomes were assessed using the 90-day modified Rankin scale (mRS) and patients were dichotomized into a favorable prognosis group (mRS ≤ 2, n = 58) and an unfavorable prognosis group (mRS > 2, n = 50). Body composition analysis based on non-contrast chest CT images included epicardial adipose tissue (EAT), visceral adipose tissue at the 12th thoracic vertebra level (VAT_T12), subcutaneous adipose tissue at the 12th thoracic vertebra level (SAT_T12), and skeletal muscle at the 12th thoracic vertebra level (SM_T12). Quantitative parameters derived from the APTw images [including the maximum values of APT (APTwmax_lesion and APTwmax_contralateral side), minimum values (APTwmin_lesion and APTwmin_contralateral side), average values (APTwmean_lesion and APTwmean_contralateral side), and the APTwmax-min value in the infarct core and contralateral normal white matter] were assessed. Differences in quantitative parameters between groups were evaluated using independent samples t-tests or the Mann-Whitney U test. Univariate and multivariate logistic regression analyses were performed to screen for independent predictors associated with AIS functional prognosis, and a nomogram model was constructed accordingly. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, calculating the area under the curve (AUC), accuracy, sensitivity, and specificity. Differences in AUC values between models were compared using DeLong's test. Calibration curves were used to evaluate the deviation between predicted and actual probabilities, and the Hosmer-Lemeshow test was employed to assess model goodness-of-fit (P > 0.05 indicating good fit). The clinical utility of the model was evaluated using decision curve analysis (DCA). The importance of each feature within the nomogram model was interpreted using SHapley additive explanations (SHAP).Results Based on 90-day mRS scores, patients were categorized into the favorable prognosis group (n = 58, mRS: 0-2) and the unfavorable prognosis group (n = 50, mRS: 3-6). Apart from admission mRS (t = 3.782, P < 0.001) and National Institutes of Health Stroke Scale (NIHSS) (t = 2.743, P = 0.006), other clinical characteristics showed no significant differences between groups. Body composition analysis revealed that patients in the unfavorable prognosis group had significantly higher epicardial adipose tissue volume (EATV) and visceral adipose tissue area at the 12th thoracic vertebra level (VAT_T12) (t = 4.948, 3.765, P < 0.001), but lower attenuation values for EAT, VAT_T12, and SM_T12 (t = -3.025, P = 0.002; t = -2.434, P = 0.015; t = -2.350, P = 0.021, respectively) compared to the favorable prognosis group. Analysis of APTw-derived quantitative parameters showed significantly lower APTwmin_lesion (t = -3.036, P = 0.002) and higher APTwmax-min_lesion (t = 2.365, P = 0.018) values in the unfavorable prognosis group. After screening for multicollinearity [variance inflation factor (VIF) < 5], multivariate logistic regression identified admission mRS score [OR = 2.726 (95% CI: 1.347 to 5.517), P = 0.005], EATV [OR = 1.019 (95% CI: 1.002 to 1.036), P = 0.026], and APTwmin_lesion [OR = 0.236 (95% CI: 0.084 to 0.662), P = 0.006] as independent predictors of unfavorable prognosis. A nomogram model was subsequently constructed. The AUC values for predicting AIS functional prognosis were 0.704, 0.777, 0.670, and 0.873 for the individual independent predictors and the combined nomogram model, respectively. The calibration curve (Hosmer-Lemeshow test X-squared = 10.533, P = 0.230) and DeLong's test (P < 0.05) confirmed that the nomogram model's predicted probabilities were highly consistent with the actual outcomes and that its performance was superior to that of individual predictors. The DCA curve demonstrated that the nomogram model provided the optimal net clinical benefit across all threshold probabilities. SHAP analysis revealed that the order of feature contribution to the nomogram model was EATV, followed by APTwmin_lesion, and then admission mRS score.Conclusions The combination of chest CT-derived body composition analysis and APTw imaging provides a comprehensive assessment of AIS functional prognosis from dual perspectives: local brain injury characteristics and systemic metabolic reserve status. This approach offers a more reliable multi-dimensional imaging basis for the early and precise identification of patients at high risk for unfavorable outcomes, thereby providing a basis for risk stratification and health management in AIS patients.
[Keywords] acute ischemic stroke；body composition analysis；quantitative computed tomography；magnetic resonance imaging；amide proton transfer-weighted imaging；functional outcome

MA Changjun1, 2, 3   ZHANG Qinhe2, 3, 4, 5   HUANG Yiping2, 3   LIU Jiahui2, 3   WANG Xiulin2, 3   LI Ying2, 3   CONG Fengyu1   LIU Ailian4, 5   LIU Jing2, 3*   WANG Jiazheng6  

1 Medical Department, Dalian University of Technology, Dalian 116024, China

2 Stem Cell Clinical Research Center, National Joint Engineering Laboratory, Regenerative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

3 Dalian Innovation Institute of Stem Cell and Precision Medicine, Dalian 116023, China

4 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

5 Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian 116011, China

6 Beijing Branch, Philips (China) Investment Co., Ltd, Beijing 100016, China

Corresponding author: LIU J, E-mail: liujing@dmu.edu.cn

Conflicts of interest   None.

Received  2026-01-02
Accepted  2026-04-16
DOI: 10.12015/issn.1674-8034.2026.09.002
Cite this article as: MA C J, ZHANG Q H, HUANG Y P, et al. The value of chest CT-derived body composition analysis combined with amide proton transfer-weighted imaging in predicting functional outcomes in patients with acute ischemic stroke[J]. Chin J Magn Reson Imaging, 2026, 17(9): 5-15. DOI:10.12015/issn.1674-8034.2026.09.002.

[1]
BALAMI J S, HADLEY G, SUTHERLAND B A, et al. The exact science of stroke thrombolysis and the quiet art of patient selection[J]. Brain, 2013, 136(Pt 12): 3528-3553. DOI: 10.1093/brain/awt201.
[2]
WILSON J T L, HAREENDRAN A, GRANT M, et al. Improving the assessment of outcomes in stroke: use of a structured interview to assign grades on the modified rankin scale[J]. Stroke, 2002, 33(9): 2243-2246. DOI: 10.1161/01.str.0000027437.22450.bd.
[3]
SUN P Z, WANG E F, CHEUNG J S. Imaging acute ischemic tissue acidosis with pH-sensitive endogenous amide proton transfer (APT) MRI: correction of tissue relaxation and concomitant RF irradiation effects toward mapping quantitative cerebral tissue pH[J]. Neuroimage, 2012, 60(1): 1-6. DOI: 10.1016/j.neuroimage.2011.11.091.
[4]
SONG G D, CHEN Y H, LUO X J, et al. Amide proton transfer-weighted MRI features of acute ischemic stroke subtypes[J/OL]. NMR Biomed, 2023, 36(10): e4983 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/37259224/. DOI: 10.1002/nbm.4983.
[5]
ZHANG Q Y, LIU J S, TIAN S F, et al. The value of amide proton transfer weighted combined with dynamic contrast-enhanced MRI in evaluating cervical cancer nerve invasion[J]. Chin J Magn Reson Imaging, 2024, 15(8): 39-45. DOI: 10.12015/issn.1674-8034.2024.08.006.
[6]
DONG W, CHEN A L, LIU A L, et al. Comparation of amide proton transfer-weighted and T2 mapping in quantifying rectal cancer with and without chemotherapy: a preliminary study[J]. Chin J Magn Reson Imaging, 2021, 12(7): 24-28. DOI: 10.12015/issn.1674-8034.2021.07.005.
[7]
MA C J, LIU A L, TIAN S F, et al. Preliminary study of APT combined with T2 mapping sequence in preoperative risk assessment of endometrial carcinoma[J]. Chin J Magn Reson Imaging, 2021, 12(9): 69-72. DOI: 10.12015/issn.1674-8034.2021.09.016.
[8]
FAN J H, LI X G, YU X Y, et al. Global burden, risk factor analysis, and prediction study of ischemic stroke, 1990-2030[J/OL]. Neurology, 2023, 101(2): e137-e150 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/37197995/. DOI: 10.1212/WNL.0000000000207387.
[9]
PRADO C M, LIEFFERS J R, MCCARGAR L J, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study[J]. Lancet Oncol, 2008, 9(7): 629-635. DOI: 10.1016/S1470-2045(08)70153-0.
[10]
PICKHARDT P J. Value-added opportunistic CT screening: state of the art[J]. Radiology, 2022, 303(2): 241-254. DOI: 10.1148/radiol.211561.
[11]
FITZGIBBONS T P, CZECH M P. Epicardial and perivascular adipose tissues and their influence on cardiovascular disease: basic mechanisms and clinical associations[J/OL]. J Am Heart Assoc, 2014, 3(2): e000582 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/24595191/. DOI: 10.1161/jaha.113.000582.
[12]
LIU L, JIA C Y, XING C F, et al. Predictive value of epicardial adipose tissue for hemorrhagic transformation and functional outcomes in acute ischemic stroke patients undergoing intravenous thrombolysis therapy[J]. J Inflamm Res, 2024, 17: 11915-11929. DOI: 10.2147/JIR.S499351.
[13]
CHO K I, KIM B J, CHO S H, et al. Epicardial fat thickness and free fatty acid level are predictors of acute ischemic stroke with atrial fibrillation[J]. J Cardiovasc Imaging, 2018, 26(2): 65-74. DOI: 10.4250/jcvi.2018.26.e1.
[14]
Chinese Medical Association Society of Neurology, Cerebrovascular Disease Group of Chinese Medical Association Society of Neurology. Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018[J]. Chin J Neurol, 2018, 51(9): 666-682. DOI: 10.3760/cma.j.issn.1006-7876.2018.09.004.
[15]
ADAMS H P, BENDIXEN B H, KAPPELLE L J, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment[J]. Stroke, 1993, 24(1): 35-41. DOI: 10.1161/01.str.24.1.35.
[16]
ZHOU H Y, PAN Y X, DU J, et al. Association of epicardial fat volume with the severity of coronary artery disease: a preliminary study on risk prediction of obstructive coronary heart disease[J/OL]. BMC Cardiovasc Disord, 2025, 25(1): 293 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/40247180/. DOI: 10.1186/s12872-025-04743-3.
[17]
WANG M, WEI T L, SUN L, et al. Incremental predictive value of liver fat fraction based on spectral detector CT for major adverse cardiovascular events in T2DM patients with suspected coronary artery disease[J/OL]. Cardiovasc Diabetol, 2025, 24(1): 151 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/40176017/. DOI: 10.1186/s12933-025-02704-w.
[18]
HUANG Y L, CUN H X, MOU Z L, et al. Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer[J/OL]. Insights Imaging, 2025, 16(1): 32 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/39912982/. DOI: 10.1186/s13244-025-01910-0.
[19]
BORYS K, LODDE G, LIVINGSTONE E, et al. Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients[J/OL]. J Transl Med, 2025, 23(1): 532 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/40355935/. DOI: 10.1186/s12967-025-06507-1.
[20]
CHEN X S, CHEN X L, ZHAO X M, et al. Subcutaneous adipose tissue [18F] FDG uptake and CT-derived body composition variables for predicting survival outcomes in patients with locally advanced gastric cancer[J]. Eur J Nucl Med Mol Imaging, 2025, 52(11): 4139-4150. DOI: 10.1007/s00259-025-07296-x.
[21]
ZHU W W, LIU Q Y, YAN Z M, et al. Sex-specific body composition profile determined by pelvic computed tomography associated with mortality in older patients with hip fracture[J/OL]. J Am Med Dir Assoc, 2025, 26(4): 105502 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/39961357/. DOI: 10.1016/j.jamda.2025.105502.
[22]
CASTRO-BARQUERO S, CASAS R, RIMM E B, et al. Loss of visceral fat is associated with a reduction in inflammatory status in patients with metabolic syndrome[J/OL]. Mol Nutr Food Res, 2023, 67(4): e2200264 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/36416291/. DOI: 10.1002/mnfr.202200264.
[23]
WANG Y Y, LIN S Y, CHUANG Y H, et al. Adipose proinflammatory cytokine expression through sympathetic system is associated with hyperglycemia and insulin resistance in a rat ischemic stroke model[J/OL]. Am J Physiol Endocrinol Metab, 2011, 300(1): E155-E163 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/20978230/. DOI: 10.1152/ajpendo.00301.2010.
[24]
KIM H K, KIM C H. Quality matters as much as quantity of skeletal muscle: clinical implications of myosteatosis in cardiometabolic health[J]. Endocrinol Metab (Seoul), 2021, 36(6): 1161-1174. DOI: 10.3803/EnM.2021.1348.
[25]
SHIRAISHI A, YOSHIMURA Y, WAKABAYASHI H, et al. Prevalence of stroke-related sarcopenia and its association with poor oral status in post-acute stroke patients: Implications for oral sarcopenia[J]. Clin Nutr, 2018, 37(1): 204-207. DOI: 10.1016/j.clnu.2016.12.002.
[26]
STYCZEN H, MAUS V, WEISS D, et al. Impact of imaging biomarkers from body composition analysis on outcome of endovascularly treated acute ischemic stroke patients[J]. J Neurointerv Surg, 2025, 17(12): 1308-1313. DOI: 10.1136/jnis-2024-022275.
[27]
ZOPFS D, PINTO DOS SANTOS D, KOTTLORS J, et al. Two-dimensional CT measurements enable assessment of body composition on head and neck CT[J]. Eur Radiol, 2022, 32(9): 6427-6434. DOI: 10.1007/s00330-022-08773-9.
[28]
VAN DEN BROECK J, SEALY M J, BRUSSAARD C, et al. The correlation of muscle quantity and quality between all vertebra levels and level L3, measured with CT: An exploratory study[J/OL]. Front Nutr, 2023, 10: 1148809 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/36908909/. DOI: 10.3389/fnut.2023.1148809.
[29]
DOUKBI E, SOGHOMONIAN A, SENGENÈS C, et al. Browning epicardial adipose tissue: friend or foe [J/OL]. Cells, 2022, 11(6): 991 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/35326442/. DOI: 10.3390/cells11060991.
[30]
NDREPEPA G. Epicardial adipose tissue: an anatomic component of obesity & metabolic syndrome in close proximity to myocardium & coronary arteries[J]. Indian J Med Res, 2020, 151(6): 509-512. DOI: 10.4103/ijmr.ijmr_2692_19.
[31]
CHEN Q, CHEN X Z, WANG J F, et al. Redistribution of adipose tissue is associated with left atrial remodeling and dysfunction in patients with atrial fibrillation[J/OL]. Front Cardiovasc Med, 2022, 9: 969513 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/36035916/. DOI: 10.3389/fcvm.2022.969513.
[32]
GAETA M, BANDERA F, TASSINARI F, et al. Is epicardial fat depot associated with atrial fibrillation A systematic review and meta-analysis[J]. EP Eur, 2017, 19(5): 747-752. DOI: 10.1093/europace/euw398.
[33]
WOLF P A, ABBOTT R D, KANNEL W B. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study[J]. Stroke, 1991, 22(8): 983-988. DOI: 10.1161/01.str.22.8.983.
[34]
CASEY J R, GRINSTEIN S, ORLOWSKI J. Sensors and regulators of intracellular pH[J]. Nat Rev Mol Cell Biol, 2010, 11(1): 50-61. DOI: 10.1038/nrm2820.
[35]
DING Y, CHEN S N, SUN Q, et al. Correlation of circadian rhythms and improvement of DepressiveSymptoms in acute ischemic stroke patients[J]. Curr Neurovascular Res, 2024, 21(1): 15-24. DOI: 10.2174/0115672026288134231228091756.
[36]
PARK J E, JUNG S C, KIM H S, et al. Amide proton transfer-weighted MRI can detect tissue acidosis and monitor recovery in a transient middle cerebral artery occlusion model compared with a permanent occlusion model in rats[J]. Eur Radiol, 2019, 29(8): 4096-4104. DOI: 10.1007/s00330-018-5964-3.
[37]
SONG G D, LI C M, LUO X J, et al. Evolution of cerebral ischemia assessed by amide proton transfer-weighted MRI[J/OL]. Front Neurol, 2017, 8: 67 [2026-01-01]. https://pubmed.ncbi.nlm.nih.gov/28303115/. DOI: 10.3389/fneur.2017.00067.
[38]
BIANCO F J. Nomograms and medicine[J]. Eur Urol, 2006, 50(5): 884-886. DOI: 10.1016/j.eururo.2006.07.043.
[39]
TURC G, APOIL M, NAGGARA O, et al. Magnetic resonance imaging-DRAGON score: 3-month outcome prediction after intravenous thrombolysis for anterior circulation stroke[J]. Stroke, 2013, 44(5): 1323-1328. DOI: 10.1161/strokeaha.111.000127.
[40]
FLINT A C, CULLEN S P, FAIGELES B S, et al. Predicting long-term outcome after endovascular stroke treatment: the totaled health risks in vascular events score[J]. AJNR Am J Neuroradiol, 2010, 31(7): 1192-1196. DOI: 10.3174/ajnr.A2050.

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