Associations of systemic inflammatory markers with the risks of chronic heart failure: A case-control study

Objective: As a greater proportion of patients survived their initial cardiac insult, Chronic Heart Failure (CHF) is becoming a major cause of worldwide morbidity and mortality. However, the mechanism underlying the inflammation in patients with CHF has not yet been elaborated. This study aims to explore the associations between inflammation and CHF patients, and the predictive performance of inflammatory indicators in identifying patients with CHF. Methods: A matched case-control study was conducted by recruiting 385 patients who were diagnosed with CHF from January 2018 to December 2019 in The First Affiliated Hospital of Chongqing Medical University. Each CHF patient was matched against one control subject without CHF on the criteria of age, sex, Body Mass Index (BMI), smoking status, and comorbidities. The clinical data and systemic inflammatory indicators were compared between the two groups, independent risk factors of CHF were identified by multivariate regression analysis, and the predictive values of systemic inflammatory indicators for CHF were analyzed by Receiver Operating Characteristic (ROC) curve analysis. Results: After processed in the univariate and multivariate regression analysis models, three systemic inflammatory indicators (hs-CRP [high sensitivity C Reactive Protein], LMR [lymphocyte-to-monocyte ratio], and Monocyte-to-High-density-lipoprotein Ratio [MHR]) were considered as independent predictors of CHF, among which the hs-CRP exhibited the best predictive performance (AUC = 0.752, 95%CI 0.717‒0.786, p < 0.001), followed by LMR (AUC = 0.711, 95% CI 0.675‒0.747, p < 0.001) and MHR (AUC = 0.673, 95% CI 0.635‒0.710, p < 0.001). The three-indicator combination showed an improved diagnostic performance (AUC = 0.757, 95% CI 0.724‒0.791, p < 0.001). In addition, the results of subgroup comparisons demonstrated that hs-CRP and MHR were associated with the severity of CHF (p < 0.001). Conclusions: The systemic inflammatory indicators such as hs-CRP, LMR, and MHR were independently correlated with the attack of CHF and might be the complementary markers of the diagnosis of CHF

Latest Advances in Intersphincteric Resection for Low Rectal Cancer

Background. Intersphincteric resection (ISR) has been a preferable alternative to abdominoperineal resection (APR) for anal preservation in patients with low rectal cancer. Laparoscopic ISR and robotic ISR have been widely used with the proposal of 2 cm or even 1 cm rule of distal free margin and the development of minimally invasive technology. The aim of this review was to describe the newest advancements of ISR. Methods. A comprehensive literature review was performed to identify studies on ISR techniques, preoperative chemoradiotherapy (PCRT), complications, oncological outcomes, and functional outcomes and thereby to summarize relevant information and controversies involved in ISR. Results. Although PCRT is employed to avoid positive circumferential resection margin (CRM) and decrease local recurrence, it tends to engender damage of anorectal function and patients’ quality of life (QoL). Common complications after ISR include anastomotic leakage (AL), anastomotic stricture (AS), urinary retention, fistula, pelvic sepsis, and prolapse. CRM involvement is the most important predictor for local recurrence. Preoperative assessment and particularly rectal endosonography are essential for selecting suitable patients. Anal dysfunction is associated with age, PCRT, location and growth of anastomotic stoma, tumour stage, and resection of internal sphincter. Conclusions. The ISR technique seems feasible for selected patients with low rectal cancer. However, the postoperative QoL as a result of functional disorder should be fully discussed with patients before surgery

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

Abstract Objective To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). Methods A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809–0.914), 0.853 (95% CI: 0.785–0.921), and 0.837 (95% CI: 0.714–0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495–8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118–149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821–0.923), 0.865 (95% CI: 0.800–0.930), and 0.848 (95% CI: 0.728–0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. Conclusions The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. Critical relevance statement The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. Key points • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC. Graphical Abstrac