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Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Background: Thyroid carcinoma (THCA) is a common type of cancer worldwide, and its incidence has been increasing in recent years. Disulfidptosis, a recently defined form of metabolic-related regulated cell death (RCD), has been shown to play a sophisticated role in antitumor immunity. However, its mechanisms and functions are still poorly understood and the association between disulfidptosis and the prognosis of patients with papillary thyroid carcinoma remains to be elucidated. This study aims to investigate the connection between disulfidptosis and the prognosis of thyroid cancer, while also developing a prognostic index based on disulfidptosis genes. Materials and methods: We utilized 24 genes associated with disulfidptosis to create the classification and model. To gather data, we sourced gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database for patients with thyroid cancer. Additionally, we obtained single-cell transcriptome data GSE184362 from the Gene Expression Omnibus (GEO) database for further analysis. Results: In this study, we utilized 24 genes associated with disulfidptosis to identify two distinct groups with different biological processes using non-negative matrix factorization (NMF). Our findings showed that Cluster 1 is associated with chemokines, interleukins, interferons, checkpoint genes, and other important components of the immune microenvironment. Moreover, cluster 1 patients with high IPS scores may be more sensitive to immunotherapy. We also provide drug therapeutic strategies for each cluster patients based on the IC50 of each drug. The Enet model was chosen as the optimal model with the highest C-index and showed that patients with high risk had a worse prognosis and weak cell-to-cell interactions in THCA. Finally, we established a nomogram model based on multivariable cox and logistic regression analyses to predict the overall survival of THCA patients. Conclusion: This research provides new insight into the impact of disulfidptosis on THCA. Through a thorough examination of disulfidptosis, a new classification system has been developed that can effectively predict the clinical prognosis and drug sensitivity of THCA patients. [ABSTRACT FROM AUTHOR]

Objective: By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients. Method: The Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts. Results: Cox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits. Conclusions: The constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals. [ABSTRACT FROM AUTHOR]

Background: CD5-positive diffuse large B-cell lymphoma (CD5+ DLBCL) is a rare subtype of DLBCL with invasive clinical features and poor prognosis. Current clinical variables based on prognostic systems for DLBCL are inadequate to accurately stratify the prognosis of CD5+ DLBCL. Methods: A total of 195 CD5+ DLBCL patients were retrospectively recruited from nine centers in Huaihai Lymphoma Working Group. MaxStat analysis was used to identify optimal cutoff points for continuous variables; univariable and multivariable Cox analyses were used for variable selection; Kaplan–Meier curve was used to analyze the value of variables on prognosis; and C-index, Brier score, and decision curve analysis were measured for predicting model performance. Results: The derivation and validation cohorts consisted of 131 and 64 patients. Of the whole cohort, median age at diagnosis was 61 years, of whom 100 (51.28%) were males and the 5‐year overall survival rate was 42.1%. MYC, BCL-2, and the coexpression of MYC/BCL-2 could distinguish the survival of CD5+ DLBCL. Multivariable analysis showed that age, IPI, red blood cell count, neutrophil count, MYC expression, and hepatosplenomegaly were independent predictors, and the prognostic nomogram was developed. The C‐index of the nomogram was 0.809 in the derivation and 0.770 in the validation cohort. Decision curve analysis proved that compared with IPI, the specific nomogram showed a better identification in CD5+ DLBCL. Conclusion: The proposed nomogram provided a valuable tool for prognosis prediction in patients with CD5+ DLBCL. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the prognostic performance of the revised 2018 FIGO staging system for cervical cancer. Methods: This retrospective multicenter study enrolled cervical cancer patients with 2009 FIGO Stage IA1–IIA2 who underwent surgeries between January 2006 and December 2017 in four tertiary hospitals. Patients were restaged according to the 2018 FIGO staging system by reviewing their medical data. Results: Of 3238 cervical cancer patients included, 1841 (56.9%) patients were restaged: 641 (34.9%) due to tumor size, 544 (29.5%) due to lymph node metastasis, 614 (33.4%) due to the inconsistency between pre‐ and postoperative assessments, and 42 due to the cancellation of invasion width in Stage IA. After restaging, a clear tendency of decreased recurrence‐free survival (RFS) and overall survival (OS) with increasing stage was observed. Multivariate Cox analysis showed that 2018 FIGO stage, parametrial involvement, and histology were independent prognostic factors for both OS and RFS (P < 0.05). Based on these factors, we established predictive nomograms with c‐indexes of 0.735 and 0.721, showing good predictive ability for cervical cancer. Conclusion: The revised 2018 FIGO staging system can better reflect the survival of cervical cancer patients. Based on it, we established a nomogram that can predict the prognosis of cervical cancer patients more precisely. Synopsis: The 2018 FIGO staging system for cervical cancer can better reflect the survival of patients; based on it, a prognostic nomogram was established. [ABSTRACT FROM AUTHOR]