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Objectives: This study aims to investigate and develop imaging biomarkers for the diagnosis of cancer-associated cachexia based on the organ and tissue-specific abnormal metabolisms measured by fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT. Methods: FDG PET/CT data from 390 cancer patients were analyzed retrospectively. Patients were divided into a development cohort and a validation cohort. Cachexia was defined as weight loss > 5% in 6 months or BMI < 20 and weight loss > 2%. According to the above definitions, patients were divided into cachexia and non-cachexia groups. Results of the clinical laboratory tests for metabolic levels and organ and tissue-specific FDG uptake obtained from the cachexia and non-cachexia groups were compared statistically. Logistic regression analysis was performed to identify independent variables associated with cachexia in the development cohort for generating the regression model. The performance of the model was tested using the data from a validation cohort and evaluated by area under the receiver operating characteristic curve (AUC). Results: Based on the data from the development cohort of 286 patients and a validation cohort of 104 patients, it is found that age, white blood cell count, peak standardized uptake value (SUV) of the liver, and minimum SUV of lean body mass of visceral fat and subcutaneous fat were independently associated with cachexia. The model incorporating these variables reached an AUC of 0.777 (95% confidence interval (CI): 0.721, 0.833) in the development cohort and an AUC of 0.729 (95% CI: 0.629, 0.829) in the validation cohort. Conclusion: Organ and tissue-specific abnormal glucose metabolism as measured by PET/CT can be used as a biomarker for cancer-associated cachexia. Key Points: • Patients with cancer-associated cachexia have reduced FDG uptake in the liver and increased FDG uptake in visceral fat and subcutaneous fat. • FDG uptake of the liver, visceral fat, and subcutaneous fat can be independent risk factors for identifying cancer-associated cachexia. • Cancer-associated cachexia can be classified using the model that incorporates age, white blood cell count, FDG uptake of the liver, and visceral and subcutaneous fat can diagnose with an AUC of 0.729. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78–27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49–29.10; P = 0.01), f (OR 5.12; 95% CI 1.23–21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19–17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25–19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.Methods: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78–27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49–29.10; P = 0.01), f (OR 5.12; 95% CI 1.23–21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19–17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25–19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.Results: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78–27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49–29.10; P = 0.01), f (OR 5.12; 95% CI 1.23–21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19–17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25–19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.Conclusion: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78–27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49–29.10; P = 0.01), f (OR 5.12; 95% CI 1.23–21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19–17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25–19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.Graphical abstract: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78–27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49–29.10; P = 0.01), f (OR 5.12; 95% CI 1.23–21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19–17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25–19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients. [ABSTRACT FROM AUTHOR]