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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]

Objectives: Despite the increasingly modern surgical techniques in the oncology field, the factors that influence postoperative prognosis in patients with hypopharyngeal and laryngeal carcinoma (HLC) remain unclear. The study aimed to evaluate the factors influencing the prognosis of HLC patients with pathological diagnosis of squamous cell carcinoma, and the findings are intended to direct follow-up management strategies. Methods: A retrospective cohort study was performed. The study population included 407 postoperative patients with HLC from 2011 to 2015. Univariate and multivariate analyses were used to examine the prognostic factors identified. Results: Based on univariate analysis results, smoking and alcohol history, tumor differentiation, preoperative radiotherapy, primary tumor sites, flap reconstruction, lymph node invasion (LNI), and preoperative albumin levels (PAL) significantly affects the prognosis of HLC patients (P <.05). Meanwhile, multivariate analysis revealed that smoking pack-year (OR = 1.002, 95% CI = 1.001 ∼ 1.003), primary tumor sites (OR = 6.241, 95% CI = 1.715 ∼ 18.433), LNI (OR = 2.869, 95% CI = 1.095 ∼ 8.743), and PAL (OR =.020, 95% CI =.004 ∼ 0.104) were associated with complications. Tumor differentiation (OR = 0.650, 95% CI =.383 ∼ 0.855), primary tumor sites (OR = 12.392, 95% CI = 3.290 ∼ 26.679), LNI (OR = 16.323, 95% CI = 2.726 ∼ 47.729), preoperative radiotherapy (OR = 9.300, 95% CI = 3.182 ∼ 27.181), and PAL (OR =.321, 95% CI =.141 ∼.732) were associated with overall survival rates. Conclusion: Smoking and alcohol history, tumor differentiation, LNI, primary tumor sites, flap reconstruction, PAL, and preoperative radiotherapy are crucial factors that influence the postoperative prognosis of patients with HLC. In addition, a monogram of five factors was established to predict the survival rates of HLC patients. [ABSTRACT FROM AUTHOR]

Background: Gastric cardia adenocarcinoma (GCA) is a highly fatal form of cancer in humans. The aim of this study was to extract clinicopathological data of postoperative patients with GCA from the Surveillance, Epidemiology, and End Results database, analyze prognostic risk factors, and build a nomogram. Methods: In this study, the clinical information of 1448 patients with GCA who underwent radical surgery and were diagnosed between 2010 and 2015 was extracted from the SEER database. The patients were then randomly divided into training (n = 1013) and internal validation (n = 435) cohorts at a 7:3 ratio. The study also included an external validation cohort (n = 218) from a Chinese hospital. The study used the Cox and LASSO models to pinpoint the independent risk factors linked to GCA. The prognostic model was constructed according to the results of the multivariate regression analysis. To assess the predictive accuracy of the nomogram, four methods were used: C‐index, calibration curve, time‐dependent ROC curve, and DCA curve. Kaplan–Meier survival curves were also generated to illustrate the differences in cancer‐specific survival (CSS) between the groups. Results: The results of the multivariate Cox regression analysis showed that age, grade, race, marital status, T stage, and log odds of positive lymph nodes (LODDS) were independently associated with cancer‐specific survival in the training cohort. Both the C‐index and AUC values depicted in the nomogram were greater than 0.71. The calibration curve revealed that the nomogram's CSS prediction was consistent with the actual outcomes. The decision curve analysis suggested moderately positive net benefits. Based on the nomogram risk score, significant differences in survival between the high‐ and low‐risk groups were observed. Conclusions: Race, age, marital status, differentiation grade, T stage, and LODDS are independent predictors of CSS in patients with GCA after radical surgery. Our predictive nomogram constructed based on these variables demonstrated good predictive ability. The nomogram for postoperative patients with GCA we constructed has good predictive power and can help clinicians accurately assess patient prognosis and identify high‐risk patients to develop more personalized treatment plans. [ABSTRACT FROM AUTHOR]

Background: In the absence of practical and reliable predictors for whether the endocervical curettage (ECC) procedure should be performed, decisions regarding patient selection are usually based on the colposcopists' clinical judgment instead of evidence. We aimed to develop and validate a practical prediction model that uses available information to reliably estimate the need to perform ECC in patients suspected of having cervical lesions.Methods: In this retrospective study, 2088 patients who underwent colposcopy, colposcopically directed biopsy (CDB) and ECC procedures between September 2019 and September 2020 at the Second Hospital of Shanxi Medical University were included. The data were analyzed with univariate and multivariable logistic regression. Least absolute shrinkage and selection operator (LASSO) was used to select predictors for ECC positivity. The ECC prediction model was presented as a nomogram and evaluated in terms of discrimination and calibration. Furthermore, this model was validated internally with cross-validation and bootstrapping.Results: Significant trends were found for ECC positivity with increasing age (P = 0.001), menopause (P = 0.003), Human papillomavirus (HPV) status (P < 0.001), severity of ThinPrep Cytological Test (TCT) (P < 0.001), original squamous epithelium ectopia (P = 0.037) and colposcopy impression (P < 0.001) by multivariable logistic regression analysis. The ECC prediction model was developed based on the following predictors: age, menopause, symptom of contact bleeding, severity of TCT, HPV status, cervix visibility, original squamous epithelium ectopia, acetowhite changes and colposcopic impression. This model had satisfactory calibration and good discrimination, with an area under the receiver operator characteristic curve (AUC) of 0.869 (95% confidence interval 0.849 to 0.889).Conclusions: A readily applicable clinical prediction model was constructed to reliably estimate the probability of ECC positivity in patients suspicious of having cervical lesions, which may help clinicians make decisions regarding the ECC procedure and possibly prevent adverse effects. [ABSTRACT FROM AUTHOR]

Background: This study aimed to establish a novel nomogram prognostic model to predict death probability for non-small cell lung cancer (NSCLC) patients who received surgery..Methods: We collected data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict mortality of NSCLC patients who received surgery.Results: A total of 44,880 NSCLC patients who received surgery from 2004 to 2014 were included in this study. Gender, ethnicity, tumor anatomic sites, histologic subtype, tumor differentiation, clinical stage, tumor size, tumor extent, lymph node stage, examined lymph node, positive lymph node, type of surgery showed significant associations with lung cancer related death rate (P < 0.001). Patients who received chemotherapy and radiotherapy had significant higher lung cancer related death rate but were associated with significant lower non-cancer related mortality (P<0.001). A nomogram model was established based on multivariate models of training data set. In the validation cohort, the unadjusted C-index was 0.73 (95% CI, 0.72-0.74), 0.71 (95% CI, 0.66-0.75) and 0.69 (95% CI, 0.68-0.70) for lung cancer related death, other cancer related death and non-cancer related death.Conclusions: A prognostic nomogram model was constructed to give information about the risk of death for NSCLC patients who received surgery. [ABSTRACT FROM AUTHOR]

Background: Ribosomal RNA N6-methyltransferase METTL5 was reported to catalyze m6A in 18S rRNA. We aimed to investigate the expression and prognostic features of METTL5 in gastric cancer (GC). Methods: In this study, 168 GC patients and their corresponding adjacent tissues were collected. Immunohistochemical staining was used to detect the expression of METTL5 protein. Univariate and multivariate Cox analysis were used to dertermine the prognostic role of METTL5 protein in GC, and a nomogram was constructed to evaluate GC patients' prognosis based on METTL5 expression. Data from TCGA and GEO database were also used to validate the prognostic value of METTL5 in GC patients on mRNA level. We further performed GSEA enrichment analysis to explore the possible function and related pathways related to METTL5. Results: METTL5 protein in gastric cancer tissues (GCTs) was significantly decreased compared with adjacent normal tissues (ANTs) and adjacent intestinal metaplasia tissues (AIMTs) (P < 0.001, respectively). Meanwhile, METTL5 expression was negatively correlated with clinicopathologic stage. According to multivariate Cox proportional hazards model analysis, METTL5 protein expression was a good independent predictor of GC prognosis (p < 0.05). Patients with high METTL5 expression had better prognosis. The nomogram constructed based on METTL5 expression could predict the prognosis of GC patients well. GSEA analysis showed that genes of METTL5 low expression group were enriched in some oncogenic signaling pathways such as ERBB, MAPK, JAK-STAT, Wnt, and mTOR, as well as some immune pathways, including Fc-gamma R mediated phagocytosis, Fc-epsilon Ri, chemokine, T cell receptor and B cell receptor signaling pathway. While the high expression group of METTL5 was mainly related to oxidative phosphorylation, nucleotide excision repair and mismatch repair. Conclusions: METTL5 protein was decreased in GCTs compared with AIMTs and ANTs, and it may be a potential prognostic biomarker in GC. [ABSTRACT FROM AUTHOR]

Background: Malignant pleural mesothelioma (MPM) is a rare but aggressive tumor that originates from the pleura and has a poor prognosis. Eligible patients can benefit from surgery, but their survival is affected by many factors. Therefore, we created a graphic model that could predict the prognosis of surgically treated patients. Methods: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results database from 2004 to 2014 to identify the key factors affecting the prognosis of surgically treated MPM patients. On this basis we built a nomogram to predict survival. We then evaluated the performance of the nomogram in a validation cohort. Results: In a training cohort of 828 cases, independent prognostic factors, including age, gender, histological type, differentiation, N stage, chemotherapy, type of surgery, and lymph node dissection, were identified. We then developed a nomogram to evaluate individual patient survival. In Kaplan–Meier analysis, a higher score in the nomogram was associated with a worse prognosis. We also used a validation cohort consisting of 312 patients to evaluate the performance of the nomogram, which was well calibrated and had good discrimination ability, with concordance indices of 0.715 and 0.656 for the training and validation cohorts, respectively. Conclusion: This study has improved our understanding of resected MPM and shown that key factors, including age and histological type, are associated with overall survival. The nomogram is a reliable tool that can help clinicians turn individualized prediction into reality and maximize patient benefit by identifying the most beneficial treatment approach. [ABSTRACT FROM AUTHOR]