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Purpose: It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (Feature SD ) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored., Methods: The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. Feature SD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression., Results: In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none Feature SD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one Feature SD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous Feature SD ; furthermore, that of each factor was obviously lower than Feature SD ., Conclusion: Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In predicting pediatric kidney depth, we are especially interested in that the errors of most estimates are within a narrow range. Therefore, this study was intended to use the proportion of estimates within a range of -5 to 5 mm (P5 mm) to evaluate the formulas and tried to regress a kidney depth formula for children. The enrolled children aged from 1 to 19 years were randomly sampled into group A and group B (75% and 25% of all recruits, respectively). Using data of the group A, the test formula was regressed by nonlinear regression and subsequently Passing & Bablok regression, and validated in group B. The Raynaud, Gordon, Tonnesen, Taylor, and the test formulas were evaluated in the 2 groups. Accuracy was evaluated by bias, absolute bias, and P5 mm; and precision was evaluated by correlation coefficient. In addition, root-mean square error was used as a mixed index for both accuracy and precision. Body weight, height, and age did not have significant differences between the 2 groups. In the nonlinear regression, coefficients of the formula (kidney depth = a × weight/height + b × age) from group A were in narrower 95% confidence intervals. After the Passing & Bablok regression, biases of left and right kidney estimates were significantly decreased. In the evaluation of formulas, the test formula was obviously better than other formulas mentioned above, and P5 mm for left and right kidneys was about 60%. Among children younger than 10 years, P5 mm was even more than 70% for left and right kidney depths. To predict pediatric kidney depth, accuracy and precision of a step-by-step regressed formula were better than the 4 "standard" formulas.

To explore whether weight-age (W-A) could be applied in clinical practice, this study was designed to verify the normalization ability of W-A by the data from another medical center, and to access the influence of the normalization on glomerular filtration rate (GFR) values in renal patients.Both plasma clearance (pGFR) and camera-based (gGFR), which were separately scaled to W-A and body surface area (BSA), were measured for patients with diffuse renal diseases. The patients (n = 298) were stratified according to the Chinese body mass index (BMI) criteria and were staged according to the Kidney Disease Outcome Quality Initiatives guideline based on gGFR and pGFR separately.The indices of intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and ratio of residual standard deviation to pooled standard deviation (RSD/PSD) suggested that, for all patients and each BMI stratum, W-A was obviously better than BSA in scaling GFR. Both under pGFR or gGFR renal stages, only small amount of the patients encountered stage migrations from BSA to W-A scaled stages. The differences between any 2 of the unscaled, BSA scaled, and W-A scaled gGFR (or pGFR) were not obviously changed. Additionally, in some strata, W-A normalization is better than BSA normalization in decreasing the median bias between pGFR and gGFR.W-A is better than BSA in scaling GFR without obvious modifying GFR values and can be applied in routine clinical practice., Competing Interests: The authors have no conflicts of interest to disclose.

Objective: Using a best variable to scale glomerular filtration rate (GFR) is important for clinical practice. The variables, estimated by equations regressed from a healthy population, are usually used in scaling GFR of renal patients. However, because the predicted variables may deviate in renal patients, it is necessary to verify whether these variables can be used to reduce the variability of GFR of renal patients. This study was designed to use repeated measures analyses to identify the best variable for scaling GFR of renal patients., Methods: Patients with non-obstructive renal diseases were enrolled in this study. The absolute GFRs of (99m)Tc-DTPA renography (gGFR) and plasma clearance (pGFR) were measured. The indices relating to between-subjects variability, such as Passing and Bablok regression, intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) were used to identify the best variable from body surface area (BSA), extracellular fluid volume (ECV), lean body mass (LBM), total body water (TBW), body mass index (BMI), and metabolic rate (MR)., Results: For the scaled indices related to between-subjects variability, ICC and CCC identified the same ranking sequence (BMI < LBMB(B; Boer) < LBMJ(J; James) < TBW < ECVB(B; Bird) < ECVS(S; Silva) < BSA < MR). In the Passing and Bablok regression, the ratio of residual standard deviation to pooled standard deviation (RSD/PSD) produced the same ranking sequence as that identified by ICC and CCC., Conclusion: The estimated metabolic rate can explain most between-subjects variability of GFR, and seems to be the best variable for scaling GFR of renal patients.