Establishment and validation of a prognostic risk classification for patients with stage T1-3N0M0 esophageal squamous cell carcinoma.

INTRODUCTION: At present, clinical factors and hematological indicators have been proved to have great potential in predicting the prognosis of cancer patients, and no one has combined these two valuable indicators to establish a prognostic model for esophageal squamous cell carcinoma (ESCC) patients with stage T1-3N0M0 after R0 resection. To verify, we aimed to combine these potential indicators to establish a prognostic model. METHODS: Stage T1-3N0M0 ESCC patients from two cancer centers (including training cohort: N = 819, and an external validation cohort: N = 177)-who had undergone esophagectomy in 1995-2015 were included. We integrated significant risk factors for death events by multivariable logistic regression methods and applied them to the training cohort to build Esorisk. The parsimonious aggregate Esorisk score was calculated for each patient; the training set was divided into three prognostic risk classes according to the 33rd and 66th percentiles of the Esorisk score. The association of Esorisk with cancer-specific survival (CSS) was assessed using Cox regression analyses. RESULTS: The Esorisk model was: [10 + 0.023 × age + 0.517 × drinking history - 0.012 × hemoglobin-0.042 × albumin - 0.032 × lymph nodes]. Patients were grouped into three classes-Class A (5.14-7.26, low risk), Class B (7.27-7.70, middle risk), and Class C (7.71-9.29, high risk). In the training group, five-year CSS decreased across the categories (A: 63%; B: 52%; C: 30%, Log-rank P < 0.001). Similar findings were observed in the validation group. Additionally, Cox regression analysis showed that Esorisk aggregate score remained significantly associated with CSS in the training cohort and validation cohort after adjusting for other confounders. CONCLUSIONS: We combined the data of two large clinical centers, and comprehensively considered their valuable clinical factors and hematological indicators, established and verified a new prognostic risk classification that can predict CSS of stage T1-3N0M0 ESCC patients.

Reconsidering N component of cancer staging for T1-2N0-2M0 small-cell lung cancer: a retrospective study based on multicenter cohort.

BACKGROUND: The current nodal (pN) classification still has limitations in stratifying the prognosis of small cell lung cancer (SCLC) patients with pathological classifications T1-2N0-2M0. Thus. This study aimed to develop and validate a modified nodal classification based on a multicenter cohort. MATERIALS AND METHODS: We collected 1156 SCLC patients with pathological classifications T1-2N0-2M0 from the Surveillance, Epidemiology, and End Results database and a multicenter database in China. The X-tile software was conducted to determine the optimal cutoff points of the number of examined lymph nodes (ELNs) and lymph node ratio (LNR). The Kaplan-Meier method, the Log-rank test, and the Cox regression method were used in this study. We classified patients into three pathological N modification categories, new pN#1 (pN0-#ELNs > 3), new pN#2 (pN0-#ELNs ≤ 3 or pN1-2-#LNR ≤ 0.14), and new pN#3 (N1-2-#LNR > 0.14). The Akaike information criterion (AIC), Bayesian Information Criterion, and Concordance index (C-index) were used to compare the prognostic, predictive ability between the current pN classification and the new pN component. RESULTS: The new pN classification had a satisfactory effect on survival curves (Log-rank P < 0.001). After adjusting for other confounders, the new pN classification could be an independent prognostic indicator. Besides, the new pN component had a much more accurate predictive ability in the prognostic assessment for SCLC patients of pathological classifications T1-2N0-2M0 compared with the current pN classification in the SEER database (AIC: 4705.544 vs. 4731.775; C-index: 0.654 vs. 0.617, P < 0.001). Those results were validated in the MCDB from China. CONCLUSIONS: The multicenter cohort developed and validated a modified nodal classification for SCLC patients with pathological category T1-2N0-2M0 after surgery. Besides, we propose that an adequate lymph node dissection is essential; surgeons should perform and consider the situation of ELNs and LNR when they evaluate postoperative prognoses of SCLC patients.

The postoperative prognosis of skip-N2 metastasis is favorable in small-cell lung carcinoma patients with pathological N2 classification: a propensity-score-adjusted retrospective multicenter study.

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.

Nomogram predicts the prognosis of patients with thymic carcinoma: A population-based study using SEER data.

BACKGROUND: Thymic carcinoma (TC) is a rare malignant tumor that can have a poor prognosis, and accurate prognostication prediction remains difficult. We aimed to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) based on a large cohort of patients. METHODS: The Surveillance Epidemiology and End Results (SEER) database was searched to identify TC patients (1975-2016). Univariate and multivariable Cox regression analyses were used to identify predictors of OS and CSS, which were used to construct nomograms. The nomograms were evaluated using the concordance index (C-index), calibration curve, receiver operating characteristic curve, and decision curve analysis (DCA). Subgroup analysis was performed to identify high-risk patients. RESULTS: The analysis identified six predictors of OS (Masaoka stage, surgical method, lymph node metastasis, liver metastasis, bone metastasis, and radiotherapy) and five predictors of CSS (Masaoka stage, surgical method, lymph node metastasis, tumor size, and brain metastasis), which were used to create nomograms for predicting three-year and five-year OS and CSS. The nomograms had reasonable C-index values (OS: 0.687 [training] and 0.674 [validation], CSS: 0.712 [training] and 0.739 [validation]). The DCA curve revealed that the nomograms were better for predicting OS and CSS, relative to the Masaoka staging system. CONCLUSION: We developed nomograms using eight clinicopathological factors that predicted OS and CSS among TC patients. The nomograms performed better than the traditional Masaoka staging system and could identify high-risk patients. Based on the nomograms' performance, we believe they will be useful prognostication tools for TC patients.

Prognostic value of preoperative C-reactive protein to albumin ratio in patients with thymic epithelial tumors: a retrospective study.

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.

The Prognostic Value of Preoperative Serum D-dimer Levels After Surgical Resection of Thymic Epithelial Tumors.

INTRODUCTION: Thymic epithelial tumors are the most common mediastinal tumors. Despite the high survival rate after surgery, some patients still require postoperative adjuvant therapy and closer follow-up. Hematological indicators such as biochemical routines and coagulation indicators have been reported to be independently associated with the prognosis of various malignancies. Therefore, we included hematological indicators in the analysis. METHODS: The data of 105 patients with thymic epithelial tumors were retrospectively collected from Sun Yat-sen University Cancer Center, and the patients with missing preoperative hematological indicators were excluded. X-tile software was used to obtain the best cutoff value of each preoperative hematological indicator, and COX regression analysis and Kaplan-Meier survival curves were used to demonstrate statistically significant results. RESULTS: COX univariate regression analysis of all patients showed that Masaoka stage, T stage, WHO histologic types, D-dimer, albumin-fibrinogen ratio (AFR), Fibrinogen (Fbg) were associated with postoperative overall survival (P < .05). T stage, WHO histologic types, D-dimer, and AFR were associated with postoperative recurrence-free survival (P < .05). Finally, multivariate regression analysis showed that T stage, D-dimer levels were independently associated with postoperative overall survival (OS) and recurrence-free survival (RFS) in patients with thymic epithelial tumors. CONCLUSIONS: For thymic epithelial tumors, higher preoperative D-dimer levels predict poorer survival and shorter recurrence-free survival. This may help guide postoperative adjuvant therapy and follow-up patterns in patients with thymic epithelial tumors.

Prognostic Value of Preoperative Serum Carcinoembryonic Antigen for Overall Survival and Recurrence-Free Survival in Resectable Thymic Epithelial Tumors.

Introduction: Tumor markers have been shown to be closely related to the long-term survival of patients with cancer and the recurrence of various malignant tumors. However, their role in thymic epithelial tumors (TETs) remains to be elucidated. We aimed to investigate whether the preoperative tumor biomarkers carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) could serve as independent predictors of postoperative prognosis in patients with TETs. Materials and Methods: We retrospectively included a total of 111 patients with TETs who underwent thymectomy at our hospital. Cox regression analysis was used to evaluate the statistical significance of CEA and NSE as independent predictors of overall survival (OS) and recurrence-free survival (RFS). Kaplan-Meier curves were used to present the results of our survival analyses. Results: Cox regression analysis showed that T stage, World Health Organization (WHO) histologic type, tumor size, and CEA levels served as independent prognostic factors for OS (P < .05). Whereas for RFS, multivariate analysis showed that only T stage, WHO histologic type, and drinking history were independently associated with it (P < .05). Conclusion: Our study found that preoperative serum CEA levels and tumor size may be strong predictors of postoperative OS in patients with TETs.

Prognostic Value of Preoperative Nutritional Assessment and Neutrophil-to-Lymphocyte Ratio in Patients With Thymic Epithelial Tumors.

Introduction: Systemic 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. Materials: We 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. Results: Univariate 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). Conclusions: This 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.

Nomogram predict relapse-free survival of patients with thymic epithelial tumors after surgery.

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.