Background Hematological indicators and clinical characteristics play an important role in the evaluation of the progression and prognosis of thymic epithelial tumors. Therefore, we aimed to combine these potential indicators to establish a prognostic nomogram to determine the relapse-free survival (RFS) of patients with thymic epithelial tumors undergoing thymectomy. Methods This retrospective study was conducted on 156 patients who underwent thymectomy between May 2004 and August 2015. Cox regression analysis were performed to determine the potential indicators related to prognosis and combine these indicators to create a nomogram for visual prediction. The prognostic predictive ability of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, and risk stratification. Decision curve analysis was used to evaluate the net benefits of the model. Results Preoperative albumin levels, neutrophil-to-lymphocyte ratio (NLR), T stage, and WHO histologic types were included in the nomogram. In the training cohort, the nomogram showed well prognostic ability (C index: 0.902). Calibration curves for the relapse-free survival (RFS) were in good agreement with the standard lines in training and validation cohorts. Conclusions Combining clinical and hematologic factors, the nomogram performed well in predicting the prognosis and the relapse-free survival of this patient population. And it has potential to identify high-risk patients at an early stage. This is a relatively novel approach for the prediction of RFS in this patient population.
Background: The study on skip-N2 metastasis in small-cell lung cancer (SCLC) is lacking. Therefore, this study aimed to explore the prognostic significance of skip-N2 metastasis based on a multicenter cohort. Methods: We collected 176 SCLC patients with pathological categories T1-4N1-2M0 from four hospitals in China. Survival curves were drawn through the Kaplan–Meier method and compared by the log-rank test. The Cox regression method was used to calculate the hazard ratio (HR) and 95% confidence interval of the characteristics for cancer-specific survival (CSS). Two propensity-score methods were used to reduce the bias, including the inverse probability of treatment weighting (IPTW) and propensity-score matching (PSM). Results: This multicenter database included 64 pN1 patients, 63 non-skip-N2 cases, and 49 skip-N2 cases. Skip-N2 and the non-skip-N2 patients had gap CSS rates (skip-N2 no versus yes: 41.0% versus 62.0% for 1-year CSS, 32.0% versus 46.0% for 2-year CSS, and 20.0% versus 32.0% for 3-year CSS). After PSM, there were 32 pairs of patients to compare survival differences between N2 and skip-N2 diseases, and 34 pairs of patients to compare prognostic gaps between N1 and skip-N2 diseases, respectively. The results of IPTW and PSM both suggested that skip-N2 cases had better survival outcomes than the non-skip-N2 cases (IPTW-adjusted HR = 0.578; PSM-adjusted HR = 0.510; all log-rank p < 0.05). Besides, the above two analytic methods showed no difference in prognoses between pN1 and skip-N2 diseases (all log-rank p > 0.05). Conclusions: Skip-N2 patients were confirmed to have a better prognosis than non-skip-N2 patients. Besides, there was no survival difference between pN1 and skip-N2 cases. Therefore, we propose that the next tumor-node-metastasis staging system needs to consider the situation of skip metastasis with lymph nodes in SCLC.
IntroductionSystemic nutrition and immune inflammation are the key factors in cancer development and metastasis. This study aimed to compare and assess four nutritional status and immune indicators: prognostic nutritional index (PNI), nutritional risk index (NRI), neutrophil-to-lymphocyte ratio (NLR), and the systemic immune-inflammatory index (SII) as prognostic indicators for patients with thymic epithelial tumors.MaterialsWe retrospectively reviewed 154 patients who underwent thymic epithelial tumor resection at our hospital between 2004 and 2015. The optimal cutoff value for each nutritional and immune index was obtained using the X-tile software. Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis.ResultsUnivariate analysis showed that PNI, NRI, NLR, SII, albumin (ALB), the albumin/globulin ratio (A/G), WHO stage, T stage, and drinking history were associated with the overall survival (OS) of patients (P < 0.05). The NRI, NLR, A/G, ALB, T stage, and WHO stage were significant independent prognostic factors of OS in multivariate analysis (P < 0.05). Finally, we constructed a coNRI-NLR model to predict OS and recurrence-free survival (RFS).ConclusionsThis study suggests that the preoperative NRI, NLR, and coNRI-NLR model may be important prognostic factors for patients with thymic epithelial tumors who undergo surgical resection.
Background The C-reactive protein to albumin ratio (CAR) is associated with poor prognosis in various cancers. However, its value in thymic epithelial tumors remains to be elucidated, we aimed to evaluate the prognostic significance of preoperative CAR in patients with surgically resected thymic epithelial tumors (TETs). Methods We retrospectively collected data from 125 patients with TETs who underwent thymoma resection at our center. The best cutoff values for the continuous variable, CAR, were obtained using X-tile software. Univariate and multivariate Cox regression analyses were used to evaluate CAR as an independent predictor of overall survival (OS) and recurrence-free survival (RFS). Kaplan–Meier analysis and log-rank tests were used to present risk stratification of patients based on CAR and the Glasgow-prognostic-score (GPS). The prognostic effect of CAR was assessed using a receiver operating characteristic curve. Results Patients were categorized into high (≥ 0.17) and low (< 0.17) CAR groups according to the optimal cutoff value of 0.17. Univariate and multivariate analyses showed that CAR was an independent predictor of prognosis. World health organization stage, CAR level, GPS score, and drinking history were important independent prognostic factors for OS (p < 0.05). T stage, CAR level, and drinking history were important independent prognostic factors for RFS (p < 0.05). The area under the curve value of CAR to predict prognosis was 0.734 for OS and 0.680 for RFS. Conclusions Elevated preoperative CAR was independently associated with poor OS and RFS after thymectomy. Therefore, CAR may be a valuable biomarker for the postoperative prognosis of TETs.
Background: Occult breast cancer is a rare breast tumor, whose prognostic nomogram model has not been established. Thus, we aim to develop and validate a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) of patients with occult breast cancer. Methods: Between 2004 and 2015, 704 eligible occult breast cancer patients were screened from the Surveillance, Epidemiology, and End Results (SEER) database using specific inclusion and exclusion criteria and then included in the surveillance. They were randomly divided into a training cohort (n = 494) and a validation cohort (N = 210). Univariate and multivariate Cox analyses were performed to explore independent prognostic factors and establish two survival-related nomograms. Area under the curve (AUC), consistency index (C index), internal and external validation calibration curve, decision curve analysis (DCA), Kaplan-Meier analysis, and subgroup analysis were used to evaluate the nomogram. Results: A total of seven variables were considered to be independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS): age, chemotherapy, radiotherapy, Progesterone receptor (PR) status, N stage, number of lymph node examinations, and number of positive lymph nodes. In the training cohort, the OS nomogram-predicted AUC for three, five, and ten years were 0.792, 0.775, and 0.783, respectively, while those of the CSS nomogram were 0.807, 0.817, and 0.812, respectively. The calibration chart showed excellent agreement between the actual and the nomogram-predicted survival rates in both the training and validation cohorts. The C-index values of the OS nomogram in the training and validation cohorts were 0.762 and 0.782, respectively, while those of the CSS nomogram were 0.786 and 0.816, respectively. DCA and subgroup analysis proved the usefulness of nomograms. Conclusion: The developed nomogram provided a comprehensive visual model of the risk of each prognostic factor. It can be conveniently used as a personalized prediction tool for the prognosis of occult breast cancer patients.
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