Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients.

PURPOSE: To explore the variation of the discriminative power of CT (Computed Tomography) radiomic features (RF) against image discretization/interpolation in predicting early distant relapses (EDR) after upfront surgery. MATERIALS AND METHODS: Data of 144 patients with pre-surgical high contrast CT were processed consistently with IBSI (Image Biomarker Standardization Initiative) guidelines. Image interpolation/discretization parameters were intentionally changed, including cubic voxel size (0.21-27 mm3) and binning (32-128 grey levels) in a 15 parameter's sets. After excluding RF with poor inter-observer delineation agreement (ICC < 0.80) and not negligible inter-scanner variability, the variation of 80 RF against discretization/interpolation was first quantified. Then, their ability in classifying patients with early distant relapses (EDR, < 10 months, previously assessed at the first quartile value of time-to-relapse) was investigated in terms of AUC (Area Under Curve) variation for those RF significantly associated to EDR. RESULTS: Despite RF variability against discretization/interpolation parameters was large and only 30/80 RF showed %COV < 20 (%COV = 100*STDEV/MEAN), AUC changes were relatively limited: for 30 RF significantly associated with EDR (AUC values around 0.60-0.70), the mean values of SD of AUC variability and AUC range were 0.02 and 0.05 respectively. AUC ranges were between 0.00 and 0.11, with values ≤ 0.05 in 16/30 RF. These variations were further reduced when excluding the extreme values of 32 and 128 for grey levels (Average AUC range 0.04, with values between 0.00 and 0.08). CONCLUSIONS: The discriminative power of CT RF in the prediction of EDR after upfront surgery for pancreatic cancer is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.

Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms.

PURPOSE: To explore the variation of the discriminative power of CT radiomic features (RF) against image discretization/interpolation in characterizing pancreatic neuro-endocrine (PanNEN) neoplasms. MATERIALS AND METHODS: Thirty-nine PanNEN patients with pre-surgical high contrast CT available were considered. Image interpolation and discretization parameters were intentionally changed, including pixel size (0.73-2.19 mm2), slice thickness (2-5 mm) and binning (32-128 grey levels) and their combination generated 27 parameter's set. The ability of 69 RF in discriminating post-surgically assessed tumor grade (>G1), positive nodes, metastases and vascular invasion was tested: AUC changes when changing the parameters were quantified for selected RF, significantly associated to each end-point. The analysis was repeated for the corresponding images with contrast medium and in a sub-group of 29/39 patients scanned on a single scanner. RESULTS: The median tumor volume was 1.57 cm3 (16%-84% percentiles: 0.62-34.58 cm3). RF variability against discretization/interpolation parameters was large: only 21/69 RF showed %COV < 20%. Despite this variability, AUC changes were limited for all end-points: with typical AUC values around 0.75-0.85, AUC ranges for the 27 parameter's set were on average 0.062 (1SD:0.037) for all end-points with maximum %COV equal to 5.5% (mean:2.3%). Performances significantly improved when excluding the 5 mm thickness case and fixing the binning to 64 (mean AUC range: 0.036, 1SD:0.019). Using contrast images or limiting the population to single-scanner patients had limited impact on AUC variability. CONCLUSIONS: The discriminative power of CT RF for panNEN is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.

Insulin resistance and hyperandrogenism have no substantive association with birth weight in adolescents with polycystic ovary syndrome.

OBJECTIVE: To assess whether birth weight influences the metabolic and hormonal profile of adolescents with polycystic ovary syndrome (PCOS). DESIGN: Retrospective study. SETTING: University outpatient clinic. PATIENT(S): One hundred seventy consecutive adolescents 12 to 19 years of age with PCOS, 15 of whom were small for gestational age (SGA), and 75 healthy female aged-matched adolescents as controls. INTERVENTION(S): Physical evaluations, fasting blood samples for measuring endocrine and metabolic parameters, and an oral glucose tolerance test. MAIN OUTCOMES MEASURE(S): Physical, endocrine, and metabolic features. RESULT(S): The birth weights of adolescents with PCOS as well as those with hyperinsulinemic or insulin resistance were similar to those of the control group. The PCOS SGA adolescents had basal insulin (15.93 ± 7.16 µU/mL vs. 10.97 ± 5.79 µU/mL) and homeostasis model assessment of insulin resistance values (3.2 ± 1.54 vs. 2.19 ± 1.28) that were statistically significantly higher than in the control group. The mean levels of total testosterone in the SGA adolescents with PCOS were above the upper limit of the normal range (0.80 ng/mL). CONCLUSION(S): Low birth weight may influence the appearance of hyperandrogenism and insulin resistance in a portion of adolescents with PCOS, but only 9% of the adolescents with PCOS in this study were SGA. In the majority of adolescents with PCOS, hyperinsulinemia and hyperandrogenism are related to factors other than birth weight alone.