Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

PURPOSE: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

A Prospective Randomized Study to Compare Postoperative Drainage After Mastectomy Using Electrosurgical Bipolar Systems and Conventional Electro-Cautery.

PURPOSE: Advanced energy devices, including electrosurgical bipolar systems or ultrasonic shears, are widely used in various surgeries. An electrosurgical bipolar device allows surgeons to grasp and dissect tissues, as well as simultaneously ligate and cut vessels and lymphatics during surgery. This study aimed to evaluate the effects of advanced bipolar energy devices on the reduction in seroma formation during mastectomy, axillary staging, and/or reconstruction. METHODS: This prospective randomized clinical trial with a 1:1 ratio compared the use of an electrosurgical bipolar device, LigaSureTM (LGS), against conventional cut-and-ligate techniques in mastectomy with axillary procedures for patients with breast cancer. A total of 82 patients with breast cancer who underwent definitive surgery were enrolled in this study. The primary endpoint was the total drainage volume after surgery. RESULTS: The clinicopathological characteristics of the two groups were not significantly different. The total postoperative drainage volume was significantly lower in the LGS group than in the control group (756.26 mL vs. 1,167.74 mL, p = 0.009). The actual postoperative drainage volume and duration also decreased significantly in the LGS group compared with those in the control group (all p < 0.05). The rate of postoperative complications was lower in the LGS group than in the control group (9.8% vs. 27.5%, p = 0.05). CONCLUSION: Electrosurgical bipolar devices showed better performance in terms of decreasing postoperative drainage during mastectomy and axillary staging and/or reconstruction.

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform.

Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.