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Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma

ABSTRACT: Background As we all know, patients with epithelial ovarian carcinoma have poor prognosis and high recurrence rate. It is critical and challenging to screen out the patients with high risk of recurrence. At present, there are some models predicting the overall survival of epithelial ovarian carcinoma, however, there is no widely accepted tool or applicable model predicting the recurrence risk of epithelial ovarian carcinoma patients. The objective of this study was to establish and verify a nomogram to predict the recurrence risk of EOC. Methods We reviewed the clinicopathological and prognostic data of 193 patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy between January 2003 and December 2013 in Peking University First Hospital. The nomogram was established with the risk factors selected by LASSO regression. The medical data of 187 EOC patients with 5-year standard follow-up in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were used for external validation of the nomogram. AUC curve and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration. Results The nomogram for 3-year recurrence risk was established with FIGO stage, histological grade, histological type, lymph node metastasis status and serum CA125 level at diagnosis. The total score can be obtained by adding the grading values of these factors together. The C statistics was 0.828 [95% CI, 0.764–0.884] and the Chi-square value is 3.6 (P?=?0.731?>?0.05) with the training group. When the threshold value was set at 198, the sensitivity, specificity, positive predictive value, negative predictive value and concordance index were 88.8, 67.0, 71.8, 86.3% and 0.558 respectively. In the external validation, the C statistics was 0.803 [95%CI, 0.738–0.867] and the Chi-square value is 11.04 (P?=?0.135?>?0.05). With the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and concordance index of the nomogram were 75.7, 77.0, 83.2, 67.9%, and 0.52 respectively. Conclusions We established and validated a nomogram to predict 3-year recurrence risk of patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence.

SUBMITTER: Hu J

PROVIDER: S-EPMC7526363 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Intratumoral heterogeneity has an enormous effect on patient treatment and outcome. The purpose of the current study was to establish and validate a nomogram with intratumoral heterogeneity derived from 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for prognosis of 5-Year progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC). METHODS:A total of 171 NPC patients who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Data was randomly divided into training cohort (n?=?101) and validation cohort (n?=?70). The clinicopathologic parameters and the following PET parameters were analyzed: maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI, SUVmax/SUVmean) for primary tumor and maximal neck lymph node. Cox analyses were performed on PFS in the training cohort. A prognostic nomogram based on this model was developed and validated. RESULTS:For the primary tumor, MTV-2.5, TLG-2.5, MTV-70%, and TLG-70% were significantly correlated with PFS. For the maximal neck lymph node, short diameter and HI were significantly correlated with PFS. Among the clinicopathologic parameters, M stage was a significant prognostic factor for recurrence. In multivariate analysis, M stage (P?=?0.006), TLG-T-70% (P?=?0.002), and HI-N (P?=?0.018) were independent predictors. Based on this prognostic model, a nomogram was generated. The C-index of this model was 0.74 (95% CI: 0.63-0.85). For the cross validation, the C-index for the model was 0.73 (95% CI: 0.62-0.83) with the validation cohort. Patients with a risk score of ?111 had poorer survival outcomes than those with a risk score of 0-76 and 77-110. CONCLUSIONS:Intratumoral heterogeneity derived from 18F-FDG PET/CT could predict long-term outcome in patients with primary NPC. A combination of PET parameters and the TNM stage enables better stratification of patients into subgroups with different PFS rates.

Project description:Background and aim: Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of nonsmall cell lung cancer with a poor prognosis. This study aimed to analyze the clinicopathological characteristics and survival outcomes among patients with PSC, lung squamous cell cancer (SCC), and lung adenocarcinoma (LAC), and to construct a competing risk nomogram for patients with PSC. Method: Data of 3 groups of patients diagnosed with PSC, SCC, or LAC from the surveillance, epidemiology, and end results (SEER) database between 1988 and 2015 were retrospectively reviewed. A 1:1 propensity score matching (PSM) analysis was used to balance the baseline data of patients. Independent risk factors associated with survival outcomes were screened by the least absolute shrinkage and selection operator and further determined by univariate and multivariate Cox proportional risk regression analyses. The overall survival (OS) of patients was evaluated by Kaplan-Meier analysis and compared with a log-rank test. The cumulative incidence function was used to estimate the 5-year probabilities of the cancer-specific mortality of PSC. A nomogram was constructed to illustrate the competing risk model to predict the 3- and 5-year OS, and corresponding concordance indexes (C-indexes) and calibration curves were used to assess and validate the competing risk nomogram. Results: A total of 2285 patients with PSC were included in this study. Compared with SCC and LAC patients, the Kaplan-Meier analysis showed that patients with PSC had a worse prognosis, with a median survival of 5 months (95% confidence interval [CI]: 5-6 months) and a 5-year OS rate of 15.3% (95% CI: 13.9%-16.9%). Similar outcomes were demonstrated after 1:1 PSM. Moreover, the competing risk model showed that age, T stage, M stage, tumor size, lymph node ratio (LNR), surgery, and chemotherapy were associated with PSC-specific mortality. The 5-year C-index of the nomogram was 0.718. Calibration curves illustrated that the nomogram was well-validated and had great accuracy. Conclusions: Patients with PSC had a worse survival outcome compared with SCC or LAC patients. Age, T stage, M stage, tumor size, LNR, surgery, and chemotherapy were associated with PSC-specific mortality. The competing risk nomogram displayed excellent discrimination in predicting PSC-specific mortality.

Project description:AbstractVarious researches demonstrated that transcription factors (TFs) played a crucial role in the progression and prognosis of cancer. However, few studies indicated that TFs were independent biomarkers for the prognosis of thyroid papillary carcinoma (TPC). Our aim was to establish and validate a novel TF signature for the prediction of TPC patients' recurrence-free survival (RFS) from The Cancer Genome Atlas (TCGA) database to improve the prediction of survival in TPC patients.The genes expression data and corresponding clinical information for TPC were obtained from TCGA database. In total, 722 TFs and 545 TPC patients with eligible clinical information were determined to build a novel TF signature. All TFs were included in a univariate Cox regression model. Then, the least absolute shrinkage and selection operator Cox regression model was employed to identify candidate TFs relevant to TPC patients' RFS. Finally, multivariate Cox regression was conducted via the candidate TFs for the selection of the TF signatures in the RFS assessment of TPC patients.We identified 6 TFs that were related to TPC patients' RFS. Receiver operating characteristic analysis was performed in training, validation, and whole datasets, we verified the high capacity of the 6-TF panel for predicting TPC patients' RFS (AUC at 1, 3, and 5 years were 0.880, 0.934, and 0.868, respectively, in training dataset; 0.760, 0.737, and 0.726, respectively, in validation dataset; and 0.777, 0.776, and 0.761, respectively, in entire dataset). The result of Kaplan-Meier analysis suggested that the TPC patients with low scores had longer RFS than the TPC patients with high score (P = .003). A similar outcome was displayed in the validation dataset (P = .001) and the entire dataset (P = 2e-05). In addition, a nomogram was conducted through risk score, cancer status, C-index, receiver operating characteristic, and the calibration plots analysis implied good value and clinical utility of the nomogram.We constructed and validated a novel 6-TF signature-based nomogram for predicting the RFS of TPC patients.

Project description:Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non?recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein?protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with 'positive regulation of steroid hormone biosynthetic processes'. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA?4; fibroblast growth factor 9; aromatase; 3??hydroxysteroid dehydrogenase/?5?4?isomerase type 2; corticosteroid 11??dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left?right determination factor 1; homeobox protein ARX; estrogen receptor ?; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist?related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non?recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

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Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma

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