ObjectiveTo investigate the prognostic factors of primary gastric squamous cell carcinoma (SCC) and develop a nomogram for predicting the survival of gastric SCC.MethodsData of 199 cases of primary gastric SCC from 2004 to 2015 were collected in the National Cancer Institute SEER database by SEER Stat 8.3.5 software. X-tile software was used to determine the best cut-off value of the age, SPSS 25.0 software was used to analyze the prognostic factors of gastric SCC and draw a Kaplan-Meier curve, and then the Cox proportional hazard regression model analysis was performed to obtain independent prognostic factors of gastric SCC. We used R studio software to visualize the model and draw a nomogram. C-index was used to evaluate the prediction effect of the nomogram. Bootstrap analyses with 1 000 resamples were applied to complete the internal verification of the nomogram.ResultsAmong the 199 patients, survival rates for 1-, 3-, and 5-year were 40.7%, 22.4%, and 15.4%, respectively. Age (χ2=6.886, P=0.009), primary site (χ2=14.918, P=0.037), race (χ2=7.668, P=0.022), surgery (χ2=16.523, P<0.001), histologic type (χ2=9.372, P=0.009), T stage (χ2=11.639, P=0.009), and M stage (χ2=31.091, P<0.001) had a significant correlation with survival time of patients. The results of the Cox proportional hazard regression model showed that, age [HR=1.831, 95%CI was (1.289, 2.601)], primary site [HR=1.105, 95%CI was (1.019, 1.199)], M stage [HR=2.222, 95%CI was (1.552, 3.179)], and surgery [HR=0.561, 95%CI was (0.377, 0.835)] were independent prognostic factors affecting the survival of gastric SCC. Four independent prognostic factors contributed to constructing a nomogram with a C-index of 0.700.ConclusionIn this research, a reliable predictive model is constructed and drawn into a nomogram, which can be used for clinical reference.
ObjectiveTo explore the risk factors affecting the prognosis of patients with metastatic breast cancer (MBC) and construct a nomogram survival prediction model.MethodsThe patients with MBC from 2010 to 2013 were collected from surveillance, epidemiology, and end results (SEER) database, then were randomly divided into training group and validation group by R software. SPSS software was used to compare the survival and prognosis of MBC patients with different metastatic sites in the training group by log-rank method and construct the Kaplan-Meier survival curve. The Cox proportional hazards model was used to analyze the factors of 3-year overall survival, then construct a nomogram survival prediction model by the independent prognostic factors. The C-index was used to evaluate its predictive value and the calibration curve was used to verify the nomogram survival prediction model by internal and external calibration graph.ResultsA total of 3 288 patients with MBC were collected, including 2 304 cases in the training group and 984 cases in the validation group. The data of the two groups were comparable. The median follow-up time of training group and validation group was 34 months and 34 months, respectively. In the training group, the results of Cox proportional hazards model showed that the older, black race, higher histological grading, without operation, ER (–), PR (–), HER-2 (–), and metastases of bone, brain, liver and lung were the risk factors of survival prognosis (P<0.05) and constructed the nomogram survival prediction model with these independent prognostic factors. The nomogram survival prediction showed a good accuracy with C-index of 0.704 [95%CI (0.691, 0.717)] in internal validation (training group) and C-index of 0.691 [95%CI (0.671, 0.711)] in external validation (validation group) in predicting 3-year overall survival. All calibration curves showed excellent consistency.ConclusionNomogram for predicting 3-year overall survival of patients with MBC in this study has a good predictive capability, and it is conducive to development of individualized clinical treatment.
ObjectiveBased on a large sample of data, study the factors affecting the survival and prognosis of patients with rectal cancer and construct a prediction model for the survival and prognosis.MethodsThe clinical data of 26 028 patients with rectal cancer were screened from the Surveillance, Epidemiology, and End Results (SEER) clinical database of the National Cancer Institute. Univariate and multivariate Cox proportional hazard regression analysis were used to screen related risk factors. Finally, the Nomogram prediction model was summarized and its accuracy was verified.ResultsResult of multivariate Cox proportional hazard regression analysis showed that the risk factors affecting the survival probability of rectal cancer included: age, gender, marital status, TMN staging, T staging, tumor size, degree of tissue differentiation, total number of lymph nodes removed, positive lymph node ratio, radiotherapy, and chemotherapy (P<0.05). Then we further built the Nomogram prediction model. The C index of the training cohort and the validation cohort were 0.764 and 0.770, respectively. The area under the ROC curve (0.777 and 0.762) for 3 years and 5 years, and the calibration curves of internal and external validation all indicated that the model could effectively predict the survival probability of rectal cancer.ConclusionThe constructed Nomogram model can predict the survival probability of rectal cancer, and has clinical guiding significance for the prognostic intervention of rectal cancer.
Objective To explore the axillary lymph node dissection (ALND) could be safely exempted in younger breast cancer patients (≤40 years of age) who receiving breast-conserving surgery combined with radiotherapy in metastasis of 1–2 sentinel lymph node (SLN) and T1–T2 stage. Methods The data of pathological diagnosis of invasive breast cancer from 2004 to 2015 in SEER database were extracted. Patients were divided into SLN biopsy group (SLNB group) and ALND group according to axillary treatment. Propensity matching score (PSM) method was used to match and equalize the clinicopathological features between two groups at 1∶1. Multivariate Cox proportional risk model was used to analyze the relationship between axillary management and breast cancer specific survival (BCSS), and stratified analysis was performed according to clinicopathological features. Results A total of 1 236 patients with a median age of 37 years (quartile: 34, 39 years) were included in the analysis, including 418 patients (33.8%) in the SLNB group and 818 patients (66.2%) in the ALND group. The median follow-up period was 82 months (quartile: 44, 121 months), and 111 cases (9.0%) died of breast cancer, including 33 cases (7.9%) in the SLNB group and 78 cases (9.5%) in the ALND group. The cumulative 5-year BCSS of the SLNB group and the ALND group were 90.8% and 93.4%, respectively, and the log-rank test showed no significant difference (χ2=0.70, P=0.401). After PSM, there were 406 cases in both the SLNB group and the ALND group. The cumulative 5-year BCSS rate in the ALND group was 4.1% higher than that in the SLNB group (94.8% vs. 90.7%). Multivariate Cox proportional hazard analysis showed that ALND could further improve BCSS rate in younger breast cancer patients [HR=0.578, 95%CI (0.335, 0.998), P=0.049]. Stratified analyses showed that ALND improved BCSS in patients diagnosed before 2012 or with a character of lymph node macrometastases, histological grade G3/4, ER negative or PR negative. Conclusions It should be cautious to consider the elimination of ALND in the stage T1–T2 younger patients receiving breast-conserving surgery combined with radiotherapy when 1–2 SLNs positive, especially in patients with high degree of malignant tumor biological behavior or high lymph node tumor burden. Further prospective trials are needed to verify the question.
Objective To establish a prediction model for the 1-, 3-, and 5-year survival rates in patients with gastric cancer liver metastases (GCLM) by analyzing prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods Clinical and pathological data from 591 patients diagnosed with GCLM between 2010 and 2015 were obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort at a 7 to 3 ratio. Independent predictors of GCLM were analyzed using univariate and multifactorial Cox regression. Consequently, nomograms were constructed. The model's accuracy was verified by calibration curve, ROC curve, and the C-index, and the clinical utility of the model was analyzed through decision curve analysis. Results Tumor differentiation grade, surgical status, and chemotherapy were significantly associated with the prognosis of GCLM patients, and these three factors were included in constructing the prognostic model and plotting the nomogram. The C-index was 0.706 (95%CI 0.677 to 0.735) and 0.749 (95%CI 0.710 to 0.788) for the training set and the internal validation cohort, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) was over 0.7 at 1, 3, and 5 years for both the training and validation cohorts. Conclusion The prediction model of the GCLM is developed based on the 3 factors, i.e., tumor differentiation grade, surgery, and chemotherapy, and shows good prediction accuracy and thus may promote clinical decision making and individualized treatment of GCLM patients.
Objective To explore the value of surgical treatment in rectal small cell neuroendocrine carcinoma (RSCC). Method The clinical data of patients with pathologically diagnosed as RSCC from 2000 to 2019 were extracted from the Surveillance, Epidemiology and End Results (SEER) database, to explore the effect of surgical treatment on cancer-specific survival (CSS) and overall survival (OS). Results A total of 348 cases were included with the median follow-up of 8 months (IQR: 3–16 months). Of the 101 patients in the operation group, 84 died (83.2%), including 56 tumor-related deaths (55.4%). Of the 247 patients in the non-operation group, 215 died (87.0%), including 131 tumor-related deaths (53.0%). The estimated 1-year OS of the operation group and the non-operation group were 49.6% and 34.4%, respectively, and the estimated 1-year CSS of those were 62.2% and 49.2%, respectively. There were significant differences between the two groups (both P<0.05). Results of multivariate prognostic analysis by Cox proportional hazard model showed that differentiation, SEER stage, receiving operative treatment or not, receiving chemotherapy or not, and receiving radiotherapy or not were independent influencing factors for OS, and SEER stage, receiving operative treatment or not, receiving chemotherapy or not, and receiving radiotherapy or not were independent influencing factors for CSS (all P<0.05). The OS [RR=0.61, 95%CI was (0.45, 0.81), P<0.001] and CSS [RR=0.67, 95%CI was (0.47, 0.95), P=0.025] in RSCC patients were significantly improved by surgical treatment. Conclusion Surgical treatment can improve the OS and CSS in RSCC patients.
Objective To explore the relationship between the metastatic sites and prognosis in newly diagnosed stage Ⅳ breast cancer. Methods The data of newly diagnosed female patients with stage Ⅳ invasive breast cancer with complete follow-up data from SEER database from 2010 to 2015 were grouped according to different metastatic sites, and the differences of breast cancer-specific survival (BCSS) in different metastatic sites were analyzed by univariate and multivariate Cox. Kaplan-Meier method was used to draw the survival curve, and log-rank test was used to analyze the prognostic factors of BCSS in newly diagnosed stage ⅳ breast cancer. Results A total of 8 407 patients were included in the final analysis. Among them, 5 619 (66.84%) patients were confirmed with bone metastasis only, 1 483 (17.64%) patients with lung metastasis only, 1 096 (13.04%) patients with liver metastasis only, and 209 (2.49%) patients with brain metastasis only. The median follow-up time was 22 months, with 4 180 (49.72%) breast cancer-related deaths and a median BCSS of 39 months in those patients. The location of metastasis in newly diagnosed stage Ⅳ invasive breast cancer was significantly correlated with BCSS (χ2=151.07, P<0.001). Multivariate Cox model analysis showed that the BCSS was worse in patients with liver metastasis [HR=1.34, 95%CI (1.21, 1.49), P<0.001], lung metastasis [HR=1.09, 95%CI (1.04, 1.14), P<0.001] and brain metastases [HR=1.28, 95%CI (1.20, 1.36), P<0.001] than in patients with bone metastases. Further subgroup analysis showed that the BCSS of breast cancer patients with different molecular subtypes and different metastatic sites were also significantly different (P<0.05). Patients with brain and liver metastases in the HR+/HER2– subtype had worse BCSS than those with bone metastases (P<0.001). Patients with brain metastases in the HR+/HER2+ subtype had worse BCSS than those with bone metastases (P=0.001). In HR–/HER2+ subtype, the BCSS of patients with liver metastasis, lung metastasis and brain metastasis were worse than that of patients with bone metastasis (P<0.05). In HR–/HER2– subtype, the BCSS of patients with brain metastasis and liver metastasis were worse than that of patients with bone metastasis (P<0.05) . Conclusion The prognosis of newly diagnosed stage ⅳ breast cancer patients with different metastatic sites is different, and the prognosis of different molecular subtypes and different metastatic sites is also different.
ObjectiveTo investigate the impact of surgical treatment on the prognosis of patients with gastric signet-ring cell carcinoma (GSRC). MethodsThe clinicopathologic and prognosis data of patients pathologically diagnosed with GSRC from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The Cox proportional hazards regression model was used to analyze the impact of surgery on overall survival (OS) and cancer-specific survival (CSS) of patients with GSRC. ResultsA total of 3 457 patients with GSRC were included, including 2 048 cases in the operation group and 1 409 cases in the non-operation group. The propensity-score matching by a 1∶1 nearest neighbour algorithm was conducted to control for confounding baseline differences. There were 802 cases in the operation group and 802 cases in the non-operation group after matching. The OS and CSS curves drawn by Kaplan-Meier method of the operation group were better than those of the non-operation group (χ2=434.3 P<0.001; χ2=412.4, P<0.001). The multivariate Cox proportional hazards regression analysis showed that the elderly (≥ 60 years old), late AJCC tumor stage (stage Ⅰ as reference), and patients with bone metastasis of GSRC increased the risk of shortening OS and CSS (P<0.05), while patients treated with surgery and chemotherapy decreased the risk of shortening OS and CSS (P<0.05). ConclusionAccording to the analysis results of SEER database in this study, surgical treatment is beneficial to improve the prognosis for patients with GSRC.
ObjectiveTo develop and validate a nomogram for predicting overall survival among patients with breast apocrine carcinoma (BAC). MethodsThe patients diagnosed with BAC from 2010 to 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly divided into a training set and a validation set by a 7∶3 ratio. Additionally, external validation of the nomogram was conducted on BAC patients admitted to the Dongfeng Hospital Affiliated to Hubei Medical College from January 1, 2010 to December 31, 2018. The risk factors affecting the overall survival of BAC patients were determined by univariate and multivariate Cox regression analyses, which were used to develop the nomogram prediction model. The discriminative abilities of the nomogram for the 3- and 5-year overall survival rates were evaluated by the C-index and area under receiver operating characteristic curve (AUC), and the fit of actual data and nomogram-predicted data for calibrators should be evaluated. ResultsA total of 649 BAC patients who met the included criterion for this study were enrolled from the SEER database (including 454 in the training set and 195 in the internal validation set), and 21 BAC patients from the Dongfeng Hospital (external validation set) were included. The multivariate Cox regression analysis showed that the age, T stage, M stage, S stage, surgical method, and chemotherapy were the risk factors affecting the overall survival of BAC patients. The C-index values of the nomogram prediction model based on these risk factors was 0.76, 0.77, and 0.88 in the training set, internal validation set, and external validation set, respectively. The calibration curves of the actual 3- and 5-year overall survival rates and nomogram-predicted 3- and 5- year overall survival rates were close to the ideal curve. The AUCs (95%CI) of the nomogram prediction model for evaluating the 3-year and 5-year overall survival rates of BAC patients were 0.84 (0.78, 0.89) and 0.76 (0.71, 0.83) in the training set, 0.81 (0.73, 0.91) and 0.84 (0.77, 0.91) in the internal validation set, and 0.80 (0.70, 0.91) and 0.84 (0.76, 0.91) in the external validation set, respectively. ConclusionNomogram based on the SEER database to predict the overall survival of BAC patients has a good predictive effect for BAC patients.
ObjectiveTo analyze the risk factors for early mortality in patients with stage Ⅳ colorectal cancer, and further construct and validate Nomogram prediction model for early mortality in stage Ⅳ colorectal cancer. MethodsA retrospective analysis was conducted on the clinical and pathological data of stage Ⅳ colorectal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 2018 to 2020. The study data was randomly divided into a training cohort and a validation cohort at a ratio of 8∶2. Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for early mortality in stage Ⅳ colorectal cancer patients, and Nomogram prediction model was further constructed. Receiver operating characteristic curve (ROC), calibration curve, and clinical decision curve analysis (DCA) were plotted. ResultsAge (50–70 group, OR=1.984, P=0.007; >70 group, OR=1.997, P=0.008), unmarried (OR=1.342, P=0.025), primary tumor differentiation of G3+G4 (OR=1.817, P<0.001), T4 stage (OR=1.434, P=0.009), N2 stage (OR=1.621, P<0.001), M1c stage (OR=1.439, P=0.036), no chemotherapy (OR=21.820, P<0.001), bone metastasis (OR=2.000, P=0.042), brain metastasis (OR=6.715, P=0.001) and liver metastasis (OR=1.886, P<0.001) were risk factors for all-cause early death in stage Ⅳ colorectal cancer patients. Age（50–70 group, OR=2.025, P=0.008; >70 group, OR=1.925, P=0.017), primary tumor differentiation grade of G3+G4 (OR=1.818, P<0.001), T4 stage (OR=1.424, P=0.013), N2 stage (OR=1.637, P<0.001), M1c stage (OR=1.541, P=0.016), no chemotherapy (OR=21.832, P<0.001), brain metastasis (OR=6.089, P=0.001), liver metastasis (OR=2.100, P<0.001) were factors for cancer-specific early death of stages Ⅳ colorectal cancer patients. Based on these variables, we constructed two Nomogram prediction models for all-cause early death and cancer-specific early death in stage Ⅳ colorectal cancer patients. The area under curve (AUC) value of the all-cause early death prediction model in the training queue was 0.874 [95% CI (0.855, 0.893)], and the AUC value of the cancer specific early death prediction model was 0.874 [95%CI (0.855, 0.894)]; the AUC value of the all-cause early death prediction model in the validation queue was 0.868 [95%CI (0.829, 0.907)], and the AUC value of the cancer specific early death prediction model was 0.867 [95%CI (0.827, 0.907)], indicating that the model had good predictive ability. The calibration curve showed that the predictive models had good consistency with the actual results for predicting early mortality in stage Ⅳ colorectal cancer, and the DCA curve showed that the models could provide patients with higher clinical benefits. ConclusionThe predictive models established in this study have good predictive performance for early mortality in stage Ⅳ colorectal cancer patients, which is helpful for clinical physicians to identify high-risk patients in the early stage and develop personalized treatment plans in clinical practice.