A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC.

BACKGROUND: As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown. METHODS: The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features. RESULTS: In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs. CONCLUSIONS: The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Distinguishing multiple primary lung cancers from intrapulmonary metastasis using CT-based radiomics.

PURPOSE: To develop CT-based radiomics models that can efficiently distinguish between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs). METHOD: This retrospective study included 127 patients with 254 lung tumors pathologically proved as MPLCs or IPMs between May 2009 and January 2020. Radiomics features of lung tumors were extracted from baseline CT scans. Particularly, we incorporated tumor-focused, refined radiomics by calculating relative radiomics differences from paired tumors of individual patients. We applied the L1-norm regularization and analysis of variance to select informative radiomics features for constructing radiomics model (RM) and refined radiomics model (RRM). The performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The two radiomics models were compared with the clinical-CT model (CCM, including clinical and CT semantic features). We incorporated both radiomics features to construct fusion model1 (FM1). We also, build fusion model2 (FM2) by combing both radiomics, clinical and CT semantic features. The performance of the FM1 and FM2 were further compared with that of the RRM. RESULTS: On the validation set, the RM achieved an AUC of 0.857. The RRM demonstrated improved performance (validation set AUC, 0.870) than the RM, and showed significant differences compared with the CCM (validation set AUC, 0.782). Fusion models further led prediction performance (validation set AUC, FM1:0.885; FM2:0.889). There were no significant differences among the performance of the FM1, the FM2 and the RRM. CONCLUSIONS: The CT-based radiomics models presented good performance on the discrimination between MPLCs and IPMs, demonstrating the potential for early diagnosis and treatment guidance for MPLCs and IPMs.

A multicenter retrospective study of transurethral prostate split for benign prostate hyperplasia.

Background: Transurethral split of the prostate (TUSP) is effective in treating benign prostatic hyperplasia (BPH). However, there is still a lack of research focusing on the optimal target population for TUSP. This study aimed to compare the efficacy of TUSP in patients with different prostate volumes or ages. Methods: The study was a multicenter retrospective study. The outcomes of TUSP in BPH patients with different prostate volumes or different ages were compared. A total of 439 patients were included in the study. Patients were divided into two groups according to prostate volume, with a cut-off value of 50 mL. Similarly, the cut-off value for the age groups was 70 years. Baseline patient characteristics and perioperative outcomes were recorded. Follow-up was performed at 1, 6, and 12 months after surgery. Results: The mean age of the patients was 73.4 years, and the mean prostate volume was 51.2 mL. At 12-month follow-up after TUSP treatment, the patients' International Prostate Symptom Scores (IPSS), quality of life (QoL) scores, and postvoid residual (PVR) volumes decreased significantly, while peak urinary flow rate (Qmax) increased significantly. Intraoperative hemoglobin (Hb) reduction was significantly lower in the small volume group than in the large volume group. The incidence of postoperative urinary urgency and transient incontinence was lower in the small volume group. IPSS score, PVR, and Qmax in the small volume group showed more remarkable changes at several time points compared to the preoperative period. Postoperative pain scores were higher in the small volume group than in the large volume group. There were no differences between the two groups in terms of long-term complications. The younger group showed greater variation in PVR and Qmax at some time points but less variation in QoL than the older group. Conclusions: TUSP is overall safe and effective in treating BPH. This study showed differences in the outcomes of TUSP in treating different prostate volumes or ages of BPH patients. The optimal surgical approach for BPH patients might be selected clinically based on a combination of prostate volume or patient age.

PEGylated Poly-HDACi: A Designer Polyprodrug from Optimized Drug Units.

We designed, synthesized, and characterized a tri-block copolymer. Its hydrophobic part, a chain of histone deacetylase inhibitor (HDACi) prodrug, was symmetrically flanked by two identical PEG blocks, whereas the built-in HDACi was a linear molecule, terminated with a thiol at one end, and a hydroxyl group at the other. Such a feature facilitated end-to-end linkage of prodrugs through alternatively aligned disulfides and carbonates. The disulfides served dual roles: redox sensors of smart nanomedicine, and warheads of masked HDACi drugs. This approach, carefully designed to benefit both control-release and efficacy, is conceptually novel for optimizing drug units in nanomedicine. Micelles from this designer polyprodrug released only PEG, CO2 and HDACi, and synergized with DOX against HCT116 cells, demonstrating its widespread potential in combination therapy. Our work highlights, for the first time, the unique advantage of thiol-based drug molecules in nanomedicine design.

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study.

PURPOSE: To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET). MATERIALS AND METHODS: We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point. RESULTS: FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ±â€¯15.1 % and 71.7 ±â€¯15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution. CONCLUSIONS: FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow.

Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning.

OBJECTIVES: To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT. METHODS: A cohort of 1414 SSNs, consisting of 319 transient SSNs in 168 individuals and 1095 persistent SSNs in 816 individuals, were identified on chest CT. The cohort was assigned by examination date into a development set of 996 SSNs, a tuning set of 212 SSNs, and a validation set of 206 SSNs. Our model was built by transfer learning, which was transferred from a well-performed deep learning model for pulmonary nodule classification. The performance of the model was compared with that of two experienced radiologists. Each nodule was categorized by Lung CT Screening Reporting and Data System (Lung-RADS) to further evaluate the performance and the potential clinical benefit of the model. Two methods were employed to visualize the learned features. RESULTS: Our model achieved an AUC of 0.926 on the validation set with an accuracy of 0.859, a sensitivity of 0.863, and a specificity of 0.858, and outperformed the radiologists. The model performed the best among Lung-RADS 2 nodules and maintained well performance among Lung-RADS 4 nodules. Feature visualization demonstrated the model's effectiveness in extracting features from images. CONCLUSIONS: The transfer learning model presented good performance on the discrimination between transient and persistent SSNs. A reliable diagnosis on nodule persistence can be achieved at baseline CT; thus, an early diagnosis as well as better patient care is available. KEY POINTS: • Deep learning can be used for the discrimination between transient and persistent subsolid nodules. • A transfer learning model can achieve good performance when it is transferred from a model with a similar task. • With the assistance of deep learning model, a reliable diagnosis on nodule persistence can be achieved at baseline CT, which can bring a better patient care strategy.