Bird Callesen (yeardinner33)

ng, He, Song, Zhou and Liu.Objective To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann-Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts. Results An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803-0.964) and validation (AUC, 0.876; 95% CI, 0.721-1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits. selleck kinase inhibitor Conclusion Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions. Copyright © 2020 Zhang, He, Guo, Liu, Yang, Zhang, Xie, Mu, Guo, Fu and Zhang.Objective To investigate the effect of anatomic and technical parameters on the incidental internal mammary lymph node (IMN) irradiation (IIMNI) dose among postmastectomy patients. Methods We retrospectively delineated the IMN on planning CT images from 138 patients who had undergone postmastectomy radiotherapy (PMRT). We analyzed the IIMNI dose coverage and its relationship with anatomic and technical parameters. Results The IIMNI mean dose was 32.85 ± 9.49 Gy, and 10 of 138 patients (7.25%) treated with PMRT received ≥45 Gy. In univariate analysis, the body weight, body mass index, body surface area, thoracic transverse diameter (DT), ratio of DT to the thoracic anteroposterior diameter (DAP)(RT/AP), planning target volume of IMN (PTVIMN) included in PTV (IMNin) and the ratio of IMNin to PTVIMN (RIMNin) and PTV posterior border were the parameters affecting IIMNI dose. In multivariate analysis, body weight, RT/AP, and RIMNin were correlative factors that affected IIMNI dose. Conclusions For patients who underwent PMRT without IMN irradiation (IMNI), there was a wide variety in IIMNI doses. A minority of patients had adequate IIMNI dose coverage, and the higher IIMNI doses were associated with the less body weights and more RIMNin. Copyright © 2020 Wang, Wang, Qiu, Sun, Zhang, Shao, Xu, Liu and Li.RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of the