Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm.

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators.

Construction of a novel radiomics nomogram for the prediction of aggressive intrasegmental recurrence of HCC after radiofrequency ablation.

Objectives: To construct a precise prediction model of preoperative magnetic resonance imaging (MRI)-based nomogram for aggressive intrasegmental recurrence (AIR) of hepatocellular carcinoma (HCC) patients treated with radiofrequency ablation (RFA).

Methods: Among 891 patients with HCC treated by RFA, 22 patients with AIR and 36 patients without AIR (non-AIR) were finally enrolled in our study, and each patient was followed up for more than 6 months to determine the occurrence of AIR. The laboratory indicators and MRI features were compared and assessed.

Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis.

Objectives: To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis.

Methods: A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters.