Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement.

To recognize patients at high risk of refractory disease, the identification of novel prognostic parameters improving stratification of newly diagnosed Hodgkin Lymphoma (HL) is still needed. This study investigates the potential value of metabolic and texture features, extracted from baseline 18F-FDG Positron Emission Tomography/Computed Tomography (PET) and Contrast-Enhanced Computed Tomography scan (CECT), together with clinical data, in predicting first-line therapy refractoriness (R) of classical HL (cHL) with mediastinal bulk involvement. We reviewed 69 cHL patients who underwent staging PET and CECT. Lesion segmentation and texture parameter extraction were performed using the freeware software LIFEx 6.3. The prognostic significance of clinical and imaging features was evaluated in relation to the development of refractory disease. Receiver operating characteristic curve, Cox proportional hazard regression and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate their prognostic value. Among clinical characteristics, only stage according to the German Hodgkin Group (GHSG) classification system significantly differed between R and not-R. Among CECT variables, only parameters derived from second order matrices (gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) demonstrated significant prognostic power. Among PET variables, SUVmean, several variables derived from first (histograms, shape), and second order analyses (GLCM, GLRLM, NGLDM) exhibited significant predictive power. Such variables obtained accuracies greater than 70% at receiver operating characteristic analysis and their PFS curves resulted statistically significant in predicting refractoriness. At multivariate analysis, only HISTO_EntropyPET extracted from PET (HISTO_EntropyPET ) and GHSG stage resulted as significant independent predictors. Their combination identified 4 patient groups with significantly different PFS curves, with worst prognosis in patients with higher HISTO_EntropyPET values, regardless of the stage. Imaging radiomics may provide a reference for prognostic evaluation of patients with mediastinal bulky cHL. The best prognostic value in the prediction of R versus not-R disease was reached by combining HISTO_EntropyPET with GHSG stage.

Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.

BACKGROUND: Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. METHODS: This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into 'No CAC' (WLGS-0/1 at baseline prior treatment and at first follow-up: N = 158, 51F/107M), 'Dev CAC' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N = 90, 34F/56M), and 'CAC' (WLGS-3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as 'No CAC', 'Dev CAC', and 'CAC' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. RESULTS: The three CAC categories displayed multi-organ differences in . In all target organs, was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In 'CAC' patients, a strong negative Spearman correlation (ρ = -0.8) was identified between and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'. CONCLUSIONS: WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.

Added prognostic value of molecular imaging parameters over proliferation index in typical lung carcinoid: an [18F]FDG PET/CT and SSTR imaging study.

OBJECTIVE: This study was performed to evaluate the prognostic meaning of volumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and somatostatin receptor (SSTR) imaging in patients with typical lung carcinoid (TC), and their relationship with proliferative index (Ki67). METHODS: We retrospectively reviewed 67 patients (38-94 years old, mean: 69.7) with diagnosis of TC who underwent [18F]FDG PET/CT and/or SSTR scintigraphy/SPECT with [111In]DTPA-Octreotide plus contrast-enhanced CT (CECT) at staging evaluation. All patients had Ki67 measured and a follow-up (FU) of at least 1 year. SSTR density (SSTRd) was calculated as the percentage difference of tumor/non-tumor ratio at 4 and 24 h post-injection. At PET/CT, metabolic activity was measured using SUVmax and SUVratio; volumetric parameters included MTV and TLG of the primary tumor, measured using the threshold SUV41%. ROC analysis, discriminant analysis and Kaplan-Meier curves (KM) were performed. RESULTS: 11 patients died during FU. Disease stage (localized versus advanced), SUVratio, SUVmax, Ki67, MTV and TLG were significantly higher in non-survivors than in survivors. ROC curves resulted statistically significant for Ki67, SUVratio, SUVmax, MTV and TLG. On multivariate analysis, stage of disease and TLG were significant independent predictors of overall survival (OS). In KM curves, the combination of disease stage and TLG identified four groups with significantly different outcomes (p < 0.005). Metabolic activity (SUVmax and SUVratio) was confirmed as significant independent prognostic factor for OS also in patients with advanced disease, with the best AUC using SUVmax. In patients with advanced and localized disease, SSTRd proved to be the best imaging prognostic factor for progression and for disease-free survival (DFS), respectively. In localized disease, SSTRd 31.5% identified two subgroups of patients with significant different DFS distribution and in advanced disease, a high cutoff value (58.5%) was a significant predictor of adverse prognosis. CONCLUSION: Volumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and SSTR imaging combined with Ki67 may provide a reference for prognosis evaluation of patients with TC, to better stratify risk groups with the goal of developing individualized therapeutic strategies.