Epidemiological Study of Adenoid Cystic Carcinoma and Its Outcomes: Insights from the Surveillance, Epidemiology, and End Results (SEER) Database.

OBJECTIVE: Adenoid cystic carcinoma (ACC) is a rare malignant tumor that mainly arises in the head and neck area. We aimed to compare the long-term survival of patients with ACC based on their geographic regions within the United States using the Surveillance, Epidemiology, and End Results (SEER) registry data. METHODS: We queried the SEER database to evaluate the geographic distribution of ACC patients based on inpatient admissions. The states included in the study were divided into four geographical regions (Midwest, Northeast, South, and West) based on the U.S. Census Bureau-designated regions and divisions. Demographic and clinical variables were compared between the groups. Kaplan-Meier curves and Cox regression were used to assess late mortality. RESULTS: A total of 5150 patients were included (4.2% from the Midwest, 17.2% from the Northeast, 22.5% from the South, and 56.1% from the West regions). The median follow-up was 12.3 (95% CI: 11.6-13.1 years). Median overall survival was 11.0 (95% CI: 9.2-NR years), 14.3 (95% CI: 12.4-16.4 years), 11.3 (95% CI: 9.7-14.8 years), and 12.0 (95% CI: 11.3-13.0 years) for Midwest, Northeast, South, and West regions, respectively. In multivariable analysis, older age, male sex, thoracic cancer, the presence of regional and distal disease, receiving chemotherapy, not undergoing surgical resection, and being treated in the West vs. Northeast region were found to be independent predictors of poor survival. We identified a significant survival difference between the different regions, with the West exhibiting the worst survival compared to the Northeast region. CONCLUSIONS: In addition to the well-known predictors of late mortality in ACC (tumor location, stage, and treatment modalities), our study identified a lack of social support (being unmarried) and geographic location (West region) as independent predictors of late mortality in multivariable analysis. Further research is needed to explore the causal relationships.

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.

Background: Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions. Methods: We built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases. Results: The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma. Conclusion: The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.

Background: The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. Methods: A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. Results: The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. Conclusions: This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

Cardiovascular Mortality Risk among Patients with Liver Cancer: A SEER Database Study.

Crucial for understanding their overall health outcomes. This research aimed to as-sess the CVM risk of liver cancer patients. METHODS AND MATERIALS: Data sourced from the Surveillance, Epidemiology, and End Results (SEER) database encompassing liver cancer diagnoses from 2000 to 2017 were utilized. The standardized mortality rate (SMR) was computed using general population reference data, and multivariate competing risk models were employed for analysis. RESULTS: Analysis of 70,733 liver cancer patient records revealed 1,954 instances of CVM. The overall CVM SMR for liver cancer patients was 12.01 (95% CI: 11.48-12.55). Various demographic and clinical factors, including sex, race, age, year of diagnosis, pathological type, general stage, treatment modalities, and matrimonial status, emerged as liver cancer pa-tients` independent predictors of CVM. CONCLUSION: Liver cancer patients have a notably heightened susceptibility to cardiovascular mortality (CVM) in contrast to the general populace. It is imperative to promptly recognize high-risk subcategories and execute tailored cardiovascular interventions as crucial measures to bolster survival rates within this cohort of patients.

Long-term trends analysis of the incidence and mortality in patients with ovarian cancer: a large sample study based on SEER database.

BACKGROUND: To analyze long-term trends of the incidence and mortality of ovarian cancer in the United States. METHODS: Patients diagnosed with ovarian cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2017. Joinpoint regression analysis was used to analyze the incidence and mortality trend, and the changes were reported as average annual percentage change (AAPC) with a 95% confidence interval (CI). Kaplan-Meier survival curve and Cox regression analyses were utilized for survival analysis. RESULTS: A total of 74 682 patients were included, among whom 49 491 (66.27%) died and 44 487 (59.57%) died from ovarian cancer. The mean age was 61.95 ± 15.23 years. The incidence of ovarian cancer showed a decreased trend from 2000 to 2017 with an AAPC of -1.9 (95%CI: -2.0, -1.7). Both the overall mortality and cancer-specific mortality for ovarian cancer decreased from 2000 to 2017, with AAPCs of -5.0 (95%CI: -5.7, -4.2) and -4.6 (95%CI: -5.4, -3.8), respectively. There was a significant decrease in the incidence and mortality of patients with the distant SEER stage, histological subtypes of serous and malignant Brenner carcinoma, and grades II and III from 2000 to 2017. Older age, Black race, histological subtypes of carcinosarcoma, higher tumor grade, and radiotherapy were associated with poorer overall survival and cancer-specific survival, whereas higher income, histological subtype of endometrioid, and surgery were associated with better survival. CONCLUSION: This study provided evidence of a statistically significant decrease in the incidence and mortality of ovarian cancer from 2000 to 2017. Key message What is already known on this topic?  Ovarian cancer is one of the most common tumors in women, with high morbidity and mortality. However, trends in long-term morbidity and mortality of patients with ovarian cancer have not been reported. What this study adds  Overall incidence and mortality for ovarian cancer showed a decreased trend from 2000 to 2017, and trends in incidence and mortality varied by stage, histological subtype, and tumor grade. Factors associated with overall survival and cancer-specific survival also differ. How this study might affect research, practice, or police  This study provides evidence of long-term trends in ovarian cancer incidence and mortality from 2000 to 2017.

Construction and validation of a nomogram for predicting cervical lymph node metastasis in tall cell variant of papillary thyroid carcinoma.

OBJECTIVE: To analyze the risk factors associated with the occurrence of cervical lymph node metastasis (LNM) in patients with tall cell variant of papillary thyroid carcinoma (TCV-PTC) and to establish a nomogram. METHODS: Clinical data of 727 patients with TCV-PTC from SEER database were obtained, and they were randomly divided into the training group (n = 508) and validation group (n = 219). The clinicopathological characteristics were analyzed by logistic regression, including age, marital status, race, gender, tumor size(cm), T stage, M stage, bilaterality, capsular invasion, extrathyroidal extension (ETE), vascular invasion and multifocality. The C-index, calibration curves, and DCA were utilized to validate the model from the differentiation and calibration of the nomogram, respectively. RESULTS: Tumor size, extrathyroidal extension, and multifocality were independent risk factors for the development of LNM in patients with TCV-PTC (P < 0.05). In the training and validation groups, the C-index of internal validation of the nomogram were 0.727 (95% CI: 0.571-0.785) and 0.712 (95%CI: 0.700-0.714). The calibration curves indicated that the model was in good agreement, and the DCA indicated that the nomogram model had good clinical utility. CONCLUSION: Tumor size, extrathyroidal extension, and multifocality are independent risk factors for developing LNM in TCV-PTC. The nomogram model can predict the risk of developing LNM in TCV-PTC patients and provide clinical guidance.

Prognostic nomogram of overall survival for radiation therapy in hepatocellular carcinoma: a population study based on the SEER database and an external cohort.

Purpose: Radiotherapy (RT) plays an important role in the treatment of hepatocellular carcinoma (HCC). To screen patients who benefit most from RT, a nomogram for survival prediction of RT based on a large sample of patients with HCC was created and validated. Methods: A total of 2,252 cases collected from the Surveillance, Epidemiology, and End Results (SEER) database were separated into a training or an internal validation cohort in a 7:3 ratio (n = 1,565:650). An external validation cohort of cases from our institute was obtained (n = 403). LASSO regression and Cox analyses were adopted to develop a nomogram for survival prediction. The decision curve analysis (DCA), calibration curve, and time-dependent receiver operating characteristic curves (TROCs) demonstrated the reliability of the predictive model. Results: For patients with HCC who received RT, the analyses revealed that the independent survival prediction factors were T stage {T2 vs. T1, hazard ratio (HR) =1.452 [95% CI, 1.195-1.765], p < 0.001; T3 vs. T1, HR = 1.469 [95% CI, 1.168-1.846], p < 0.001; T4 vs. T1, HR = 1.291 [95% CI, 0.951-1.754], p = 0.101}, N stage (HR = 1.555 [95% CI, 1.338-1.805], p < 0.001), M stage (HR = 3.007 [95% CI, 2.645-3.418], p < 0.001), max tumor size (>2 and ≤5 vs. ≤2 cm, HR = 1.273 [95% CI, 0.992-1.633], p = 0.057; >5 and ≤10 vs. ≤2 cm, HR = 1.625 [95% CI, 1.246-2.118], p < 0.001; >10 vs. ≤2 cm, HR = 1.784 [95% CI, 1.335-2.385], p < 0.001), major vascular invasion (MVI) (HR = 1.454 [95% CI, 1.028-2.057], p = 0.034), alpha fetoprotein (AFP) (HR = 1.573 [95% CI, 1.315-1.882], p < 0.001), and chemotherapy (HR = 0.511 [95% CI, 0.454-0.576], p < 0.001). A nomogram constructed with these prognostic factors demonstrated outstanding predictive accuracy. The area under the curve (AUC) in the training cohort for predicting overall survival (OS) at 6, 12, 18, and 24 months was 0.824 (95% CI, 0.803-0.846), 0.824 (95% CI, 0.802-0.845), 0.816 (95% CI, 0.792-0.840), and 0.820 (95% CI, 0.794-0.846), respectively. The AUCs were similar in the other two cohorts. The DCA and calibration curve demonstrated the reliability of the predictive model. Conclusion: For patients who have been treated with RT, a nomogram constructed with T stage, N stage, M stage, tumor size, MVI, AFP, and chemotherapy has good survival prediction ability.

Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis.

Background: This study aimed to develop and validate nomograms to predict overall survival (OS) for pelvic Ewing's sarcoma (EWS) and chordoma, identify prognostic factors, and compare outcomes between the two conditions. Methods: We identified patients diagnosed with pelvic EWS or chordoma from the SEER database (2001-2019). Independent risk factors were identified using univariate and multivariate Cox regression analyses, and these factors were used to construct nomograms predicting 3-, 5-, and 10-year OS. Validation methods included AUC, calibration plots, C-index, and decision curve analysis (DCA). Kaplan-Meier curves and log-rank tests compared survival differences between low- and high-risk groups. Results: The study included 1175 patients (EWS: 611, chordoma: 564). Both groups were randomly divided into training (70 %) and validation (30 %) cohorts. OS was significantly higher for chordoma. Multivariate analysis showed year of diagnosis, income, stage, and surgery were significant for EWS survival, while age, time to treatment, stage, and surgery were significant for chordoma survival. Validation showed the nomograms had strong predictive performance and clinical utility. Conclusions: The nomograms reliably predict overall survival (OS) in pelvic EWS and chordoma, helping to identify high-risk patients early and guide preventive measures. The study also found that survival rates are significantly higher for chordoma, highlighting different prognostic profiles between EWS and chordoma.

Radiotherapy can improve overall survival in patients with lymph-node positive, high-grade neuroendocrine cervical cancer: construction of two prognostic nomograms to predict treatment outcome.

Background: To explore the beneficial subgroups after radiotherapy in high-grade neuroendocrine cervical cancer (HGNECC) and construct two survival prognosis models to quantify the efficacy of radiotherapy assessment. Methods: In this retrospective study, we included 592 eligible samples from the Surveillance, Epidemiology, and End Results (SEER) database and 56 patients with lymph-node positive HGNECC from Chongqing Medical University. Cox regression analysis was used to identify independent survival prognosis risk factors for HGNECC patients. Propensity score matching (PSM) was employed as it balances the baseline differences among grouping methods. Kaplan-Meier (K-M) curves were used to analyze survival differences among different groups. Two survival prediction nomograms were constructed separately (using the "rms" package in R software) based on whether radiotherapy was administered. The stability and accuracy of these models were assessed using receiver operating characteristic (ROC) curves and calibration curves in both the training and validation datasets. P<0.05 was considered to indicate statistically significant differences. Results: Age, Federation of Gynecology and Obstetrics (FIGO)-stage, and treatment methods (surgery vs. chemotherapy) were independent risk factors that affected survival prognosis (P<0.05). Radiotherapy showed adverse effects on survival in patients with early tumor staging, lymph-node negative status, and absence of distant metastasis (all P<0.05). The lymph-node positive group had a beneficial response to radiotherapy (P<0.05), and patients with metastasis in the radiotherapy group showed a survival protection trend (P=0.069). Conclusion: In HGNECC, patients with lymph-node positive status can benefit from radiotherapy in terms of survival outcomes. We constructed two survival prediction models based on whether radiotherapy was administered, thereby offering a more scientifically guided approach to clinical treatment planning by quantifying the radiotherapy efficacy.

Nomograms to predict lung metastasis in malignant primary osseous spinal neoplasms and cancer-specific survival in lung metastasis subgroup.

Purpose: To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis and predicting cancer-specific survival (CSS) in the lung metastasis subgroup. Methods: A total of 1,298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing sarcoma, and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3- and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms. Results: Histologic type, grade, lymph node involvement, tumor size, tumor extension, and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827, respectively. Age, histologic type, surgery at primary site, and grade were identified as the prognostic factors for the CSS. The AUC for the 3- and 5-year CSS were 0.790 and 0.740, respectively. Calibration curves revealed good agreements, and the Hosmer and Lemeshow test identified the models to be well fitted. DCA curves demonstrated that nomograms were clinically useful. Conclusion: The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis and make a personalized CSS evaluation for the lung metastasis subgroup.

Demographics, Histopathology, and Treatment Outcomes of Squamous Cell Carcinoma of the Prostate.

BACKGROUND: Squamous cell carcinoma of the prostate (SCCP) is a neoplasm that comprises fewer than 1% of all primary prostate cancer diagnoses. Given its rarity, there is a paucity of data regarding the treatment of this disease. The limited literature points to the potential of local therapy in conjunction with chemotherapy to improve patient mortality. METHODS: Using the National Cancer Initiative's Surveillance, Epidemiology, and End Results (SEER) database, a retrospective review of patients diagnosed with primary SCCP between 2000 and 2018 was performed. Patient demographics, tumor characteristics, and patient outcomes based on treatment modality were analyzed. Univariate and survival analyses were conducted with p < 0.05 indicating statistical significance. RESULTS: A total of 66 patients were identified. Five-year overall survival (5y OS) was 24%; mean and median survival were 2.2 years (1.8, 2.7) and 1.2 years (0.3, 2.1), respectively. Patients with Grade I or Grade II disease had an increased 5y OS of 55% (27%, 83%). In comparison, 5y OS was 13% (-2%, 29%) for patients with Grade III and Grade IV disease (p = 0.017). Analysis of 5y OS based on disease histology revealed patients with papillary SCC had a 5y OS of 50% [9.2%, 91%], compared to 21% [9%, 34%] for patients with SCC, not otherwise specified and 0% for those with lymphoepithelial carcinoma (p = 0.048). Analysis of 5y OS stratified by treatment modality revealed no statistically significant change with any treatment (surgery, radiotherapy, and chemotherapy). No difference in 5y OS was seen between those treated with radical prostatectomy versus external beam radiation therapy. CONCLUSIONS: The literature on SCCP remains sparse; the rarity of this disease limits analysis. While the investigation undertaken in this paper does not find any change in 5y OS regardless of treatment modality, the variation in 5y OS based on histologic classification of SCCP points to a potential route for the future treatment of this disease.

Survival prediction in peritoneal mesothelioma: a nomogram based on SEER data and a Chinese cohort.

Objective: This study aimed to develop nomogram predicting overall survival (OS) of patients with peritoneal mesothelioma (PeM) using data from Surveillance, Epidemiology, and End Results (SEER) database and a Chinese institution. Methods: 1,177 PeM patients from the SEER database were randomized into training and internal validation cohorts at a 7:3 ratio. An external validation cohort consisting of 109 patients was enrolled from a Chinese institution. Nomogram was constructed based on variables identified through multivariate Cox regression analysis and evaluated by consistency indices (C-index), calibration plots, and receiver operating characteristic (ROC) curves. Patients were stratified into different risk categories, and Kaplan-Meier survival analysis was used to assess OS differences among these groups. Results: The nomogram, incorporating age, gender, histological type, T stage, M stage, and surgical status, demonstrated strong predictive capability with C-index values of 0.669 for the training cohort, 0.668 for the internal validation cohort, and 0.646 for the external validation cohort. The nomogram effectively stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly poorer OS (P < 0.05). Multivariate analysis confirmed gender, age, surgical intervention, and M stage as independent prognostic factors (P < 0.05). Specifically, male gender, older age, and unspecified M stage were linked to worse outcomes, while surgical intervention was associated with improved survival. Conclusion: The nomogram provide a reliable tool for predicting the survival in PeM patients, facilitating more informed treatment decisions. Key independent prognostic factors include gender, age, surgical intervention, and M stage.

Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients.

BACKGROUND: HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients. METHODS: 240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA). RESULTS: The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model. CONCLUSION: In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different machine learning algorithms were applied to develop treatment options for predicting patients with prostate cancer, differentiating between surgical and non-surgical treatments. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value. The Shapley Additive Explanations (SHAP) method was employed to investigate the key factors influencing the prediction process. Survival analysis methods were used to compare the survival rates of different treatment options. The CatBoost model yielded the best results (AUC = 0.939, sensitivity = 0.877, accuracy = 0.877). SHAP interpreters revealed that the T stage, cancer stage, age, cores positive percentage, prostate-specific antigen, and Gleason score were the most critical factors in predicting treatment options. The study found that surgery significantly improved survival rates, with patients undergoing surgery experiencing a 20.36% increase in 10-year survival rates compared with those receiving non-surgical treatments. Among surgical options, radical prostatectomy had the highest 10-year survival rate at 89.2%. This study successfully developed a predictive model to guide treatment decisions for prostate cancer. Moreover, the model enhanced the transparency of the decision-making process, providing clinicians with a reference for formulating personalized treatment plans.

A novel nomogram based on the number of positive lymph nodes can predict the overall survival of patients with pancreatic head cancer after radical surgery.

BACKGROUND: This study aimed to construct a novel nomogram based on the number of positive lymph nodes to predict the overall survival of patients with pancreatic head cancer after radical surgery. MATERIALS AND METHODS: 2271 and 973 patients in the SEER Database were included in the development set and validation set, respectively. The primary clinical endpoint was OS (overall survival). Univariate and multivariate Cox regression analyses were used to screen independent risk factors of OS, and then independent risk factors were used to construct a novel nomogram. The C-index, calibration curves, and decision analysis curves were used to evaluate the predictive power of the nomogram in the development and validation sets. RESULTS: After multivariate Cox regression analysis, the independent risk factors for OS included age, tumor extent, chemotherapy, tumor size, LN (lymph nodes) examined, and LN positive. A nomogram was constructed by using independent risk factors for OS. The C-index of the nomogram for OS was 0.652 [(95% confidence interval (CI): 0.639-0.666)] and 0.661 (95%CI: 0.641-0.680) in the development and validation sets, respectively. The calibration curves and decision analysis curves proved that the nomogram had good predictive ability. CONCLUSIONS: The nomogram based on the number of positive LN can effectively predict the overall survival of patients with pancreatic head cancer after surgery.

Development and validation of a nomogram to predict cancer-specific survival of patients with large hepatocellular carcinoma accepting surgical resection: a real-world analysis based on the SEER database.

Background: Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR. Methods: The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups. Results: In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram. Conclusions: The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.