Nomogram For Predicting Survival Research Articles

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

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. 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. 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. 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.

Chemoradiotherapy vs radiotherapy for non-surgical locally advanced laryngeal squamous cell carcinoma patients: a propensity score-matched study and practical nomogram construction.

To compare the cancer-specific survival (CSS) among patients with locally advanced laryngeal squamous cell carcinoma (LSCC) receiving chemoradiotherapy (CRT) and radiotherapy (RT) treatment, as well as to establish a prognostic nomogram for survival prediction in patients receiving CRT. Using data from the Surveillance, Epidemiology, and End Results (SEER) database, patients with laryngeal cancer were identified between 2010 and 2015, with follow-up up to 2018. Propensity score matching (PSM) was performed to minimize disproportionate distributions of the potential confounding. Cox regression models were used to evaluate the CSS of two treatment groups. A prognostic nomogram for patients receiving CRT was then developed and evaluated. Totally 1085 non-surgical patients with locally advanced LSCC were included in this study (median [IQR] age, 62 [55-69] years; 829 [76.41%] males), of which 913 receiving CRT and 172 receiving RT. After PSM, significantly improved CSS was observed in locally advanced LSCC patients receiving CRT when compared to RT (HR: 0.62 [95% CI 0.42-0.92]; P = 0.014). Then, in the group of 639 locally advanced LSCC patients receiving CRT, a prognostic nomogram based on age, tumor size, N category, and marital status were developed and validated, of which the predictive performance was superior to that of TNM staging system (7th edition). CSS shows a statistically significant improvement in locally advanced LSCC patients who receipt of CRT when compared with RT. Furthermore, a prognostic nomogram for locally advanced LSCC patients receiving CRT was established, which shows a good calibration and identification accuracy.

The prognostic value of age-adjusted Charlson comorbidity index in laparoscopic resection for hilar cholangiocarcinoma

The prognostic role of the Age-Adjusted Charlson Comorbidity Index (ACCI) in hilar cholangiocarcinoma patients undergoing laparoscopic resection is unclear. To evaluate ACCI's effect on overall survival (OS) and recurrence-free survival (RFS), we gathered data from 136 patients who underwent laparoscopic resection for hilar cholangiocarcinoma at Zhengzhou University People’s Hospital between 1 June 2018 and 1 June 2022. ACCI scores were categorized into high ACCI (ACCI > 4.0) and low ACCI (ACCI ≤ 4.0) groups. We examined ACCI's association with OS and RFS using Cox regression analyses and developed an ACCI-based nomogram for survival prediction. Our analysis revealed that higher ACCI scores (ACCI > 4.0) (HR = 2.14, 95%CI: 1.37–3.34) were identified as an independent risk factor significantly affecting both OS and RFS in postoperative patients with hilar cholangiocarcinoma (p < 0.05). TNM stage III-IV (HR = 7.42, 95%CI: 3.11–17.68), not undergoing R0 resection (HR = 1.58, 95%CI: 1.01–2.46), hemorrhage quantity > 350 mL (HR = 1.92, 95%CI: 1.24–2.97), and not receiving chemotherapy (HR = 1.89, 95%CI: 1.21–2.95) were also independent risk factors for OS. The ACCI-based nomogram accurately predicted the 1-, 2-, and 3-year OS rates, with Area Under the Curve (AUC) values of 0.818, 0.844, and 0.924, respectively. Calibration curves confirmed the nomogram’s accuracy, and decision curve analysis highlighted its superior predictive performance. These findings suggest that a higher ACCI is associated with a worse prognosis in patients undergoing laparoscopic resection for hilar cholangiocarcinoma. The ACCI-based nomogram could aid clinicians in making accurate predictions about patient survival and facilitate individualized treatment planning.

Nomogram based on pretreatment hepatic and renal function indicators for survival prediction of locally advanced esophageal squamous cell carcinoma with treatment of neoadjuvant chemoradiotherapy plus surgery.

The parameters for survival prediction of esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant chemoradiotherapy (NCRT) combined with surgery are unclear. Here, we aimed to construct a nomogram for survival prediction of ESCC patients treated with NCRT combined with surgery based on pretreatment serological hepatic and renal function tests. A total of 174 patients diagnosed as ESCC were enrolled as a training cohort from July 2007 to June 2019, and approximately 50% of the cases (n = 88) were randomly selected as an internal validation cohort. Univariate and multivariate Cox survival analyses were performed to identify independent prognostic factors to establish a nomogram. Predictive accuracy of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration curve. ALT, ALP, TBA, TP, AST, TBIL and CREA were identified as independent prognostic factors and incorporated into the construction of the hepatic and renal function test nomogram (HRFTNomogram). The C-index of the HRFTNomogram for overall survival (OS) was 0.764 (95% CI 0.701-0.827) in the training cohort, which was higher than that of the TNM staging system (0.507 (95% CI 0.429-0.585), P < 0.001). The 5-year OS calibration curve of the training cohort demonstrated that the predictive accuracy of the HRFTNomogram was satisfactory. Moreover, patients in the high-risk group stratified by the HRFTNomogram had poorer 5-year OS than those in the low-risk group in the training cohort (27.4% vs. 80.3%, P < 0.001). Similar results were observed in the internal validation cohort. A novel HRFTNomogram might help predict the survival of locally advanced ESCC patients treated with NCRT followed by esophagectomy.

Prognostic factors of sarcomas occurring in bone and joint: A SEER based study.

To clarify the epidemiology, treatment, and prognosis of sarcomas occurring in the bones and joints. The surveillance, epidemiology, and end results (SEER) 18 registries, comprising sarcoma diagnoses made between 2008 and 2014, were queried for sarcomas arising in bones or joints. Kaplan-Meier analysis, multivariate logistic regression analysis, Cox proportional hazards model, and nomograms were used to identify prognostic factors. 2794 patients aged from 1 to 99 (55.8% male) with microscopically confirmed diagnosed as sarcomas (including osteosarcoma, chondrosarcoma, Ewing sarcoma, and soft tissue sarcomas) which primary site limited to bone and joint were identified. Eight independent factors, including age, race, sex, tumor site, histology, pathology grade, tumor size, and total number of malignant tumors (TNOMT), were associated with tumor metastasis. Nine independent prognostic factors, including age (>=60 year, hazard ratio [HR] = 4.145, 95% confidence interval [CI], P < .001), sex (female, HR = 0.814, 95%CI, P = .007), tumor site (spine, HR = 2.527, 95%CI, P < .001), histology, pathology grade (undifferentiated, HR = 5.816, 95%CI, P < .001), tumor size (>=20 cm, HR = 3.043, 95%CI, P < .001), tumor extent (distant, HR = 4.145, 95%CI, P < .001), surgery (no performed, HR = 2.436, 95%CI, P < .001), and TNOMT (1, HR = 0.679, 95%CI, P < .001, were identified and incorporated to construct a nomogram for 2- and 5-year overall survival (OS). The calibration curve for the probability of survival showed good agreement between prediction by the nomogram and actual observation. The C-index of the nomogram for survival prediction was 0.814. Patients who received chemotherapy had a significantly decreased risk of death only for Ewing sarcoma, poorly differentiated tumors, undifferentiated tumors, and distant tumor invasion (P < .05). However, radiotherapy did not show significant differences in OS. This study presents population-based estimates of prognosis for patients with bone sarcomas and demonstrates the impact of age, race, sex, tumor site, histology, pathology grade, tumor size, tumor extent, surgery, radiotherapy, chemotherapy, and the TNOMT on OS. Moreover, the nomogram resulted in a more accurate prognostic prediction. However, in our study, radiotherapy showed no survival benefit, perhaps because detailed data on treatment factors were unavailable and which may have influenced the results.

Prediction of survival in patients with infected pancreatic necrosis: a prospective cohort study.

Infected pancreatic necrosis (IPN) is a severe complication of acute pancreatitis, with mortality rates ranging from 15 to 35%. However, limited studies exist to predict the survival of IPN patients and nomogram has never been built. This study aimed to identify predictors of mortality, estimate conditional survival (CS), and develop a CS nomogram and logistic regression nomogram for real-time prediction of survival in IPN patients. A prospective cohort study was performed in 335 IPN patients consecutively enrolled at a large Chinese tertiary hospital from January 2011 to December 2022. The random survival forest method was first employed to identify the most significant predictors and capture clinically relevant nonlinear threshold effects. Instantaneous death risk and CS was first utilized to reveal the dynamic changes in the survival of IPN patients. A Cox model-based nomogram incorporating CS and a logistic regression-based nomogram were first developed and internally validated with a bootstrap method. The random survival forest model identified seven foremost predictors of mortality, including the number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification. Duration of organ failure and time from onset to first intervention showed distinct thresholds and nonlinear relationships with mortality. Instantaneous death risk reduced progressively within the first 30 days, and CS analysis indicated gradual improvement in real-time survival since diagnosis, with 90-day survival rates gradually increasing from 0.778 to 0.838, 0.881, 0.974, and 0.992 after surviving 15, 30, 45, 60, and 75 days, respectively. After further variables selection using step regression, five predictors (age, number of organ failures, hemorrhage, time from onset to first intervention, and bloodstream infection) were utilized to construct both the CS nomogram and logistic regression nomogram, both of which demonstrated excellent performance with 1000 bootstrap. Number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification were the most crucial predictors of mortality of IPN patients. The CS nomogram and logistic regression nomogram constructed by these predictors could help clinicians to predict real-time survival and optimize clinical decisions.

Comparison of the effectiveness of chemotherapy combined with immunotherapy and chemotherapy alone in advanced biliary tract cancer and construction of the nomogram for survival prediction based on the inflammatory index and controlling nutritional status score.

To analyze the effectiveness of combining immune checkpoint inhibitors (ICIs) with first-line therapy in patients with advanced biliary tract cancer (BTC) and explore the biomarkers affecting the prognosis of immunotherapy, to construct a nomogram for the prediction of survival. A retrospective study was conducted to include a total of 209 patients with advanced BTC treated in the first line from 2018 to 2022, divided into a combination therapy group (n = 129) and a chemotherapy-only group (n = 80) according to whether ICIs were applied in combination. Univariate and multifactorial COX regression analyses were performed on variables that may affect prognosis to identify independent influences on patient prognosis, and this was used to create nomograms, which were then prospectively validated and calibrated. The median progression-free survival (mPFS) and median overall survival (mOS) of patients in the combination therapy group were higher than those in the chemotherapy alone group [hazard ratio (HR) = 1.152, 95% confidence interval (CI): 0.7848-1.692, p = 0.0004, and HR = 1.067, 95% CI: 0.7474-1.524, p = 0.0016]. The objective response rate (ORR) of patients in the combination therapy and chemotherapy alone groups was 39.5% (51/129) vs. 27.5% (22/80), and the disease control rate (DCR) between the two groups was 89.9% (116/129) vs. 83.8% (67/80). Univariate analysis revealed the gender, presence of long-term tobacco and alcohol, degree of histological differentiation, serum albumin level, presence of liver metastases, presence of multi-visceral metastases, response, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), glycoprotein antigen 19-9 (CA19-9), systemic inflammatory index (SII), and controlling nutritional status (CONUT) scores were statistically significant with patient prognosis (all P values < 0.05). Multi-factor COX regression analysis was continued for the above variables, and the results showed that NLR, MLR, PLR, SII, and CONUT scores were independent influences on patients' OS (all p values < 0.05). A nomogram (C-index 0.77, 95% CI: 0.71-0.84) was created based on these independent influences and later validated using a validation cohort (C-index 0.75, 95% CI: 0.68-0.81). The time-dependent receiver operator characteristic curve (ROC) showed that the area under curve (AUC) of the training cohort patients at 12, 18, and 24months was 0.72 (95% CI: 0.63-0.81), 0.75 (95% CI: 0.67-0.85), and 0.77 (95% CI: 0.66-0.87) and the AUC of the validation cohort was 0.69 (95% CI: 0.58-0.79), 0.74 (95% CI: 0.65-0.87), and 0.71 (95% CI: 0.64-0.89), respectively. Finally, calibration was performed using calibration curves, and the results showed that nomograms based on inflammatory metrics and CONUT scores could be used to assess survival (12, 18, and 24months) in patients with advanced BTC treated with ICIs in the first line. Patients with advanced BTC benefit more from first-line treatment with standard chemotherapy in combination with ICIs than with chemotherapy alone. In addition, nomograms based on inflammatory metrics and CONUT scores can be used to predict survival at 12, 18, and 24months in patients with advanced BTC treated with ICIs.

Nomogram for predicting survival of patients with gastric cancer and multiple primary malignancies: a real-world retrospective analysis using the Surveillance, Epidemiology and End Results database.

Gastric cancer combined with multiple primary malignancies (GCM) is increasingly common. This study investigated GCM clinical features and survival time. Patients with GCM and GC only (GCO) were selected from the Surveillance, Epidemiology and End Results (SEER) database. Survival was compared between GCM and GCO groups using propensity score matching. Then, the GCM group was divided into a training cohort and a validation cohort. These cohorts were used to establish a nomogram for survival prediction in patients with GCM. Survival time was significantly longer in the GCM group than in the GCO group. All-subsets regression was used to identify four variables for nomogram establishment: age, gastric cancer sequence, N stage, and surgery. The concordance index and time-dependent receiver operating characteristic curve indicated that the nomogram had favorable discriminative ability. Calibration plots of predicted and actual probabilities showed good consistency in both the training and validation cohorts. Decision curve analysis and risk stratification showed that the nomogram was clinically useful; it had favorable discriminative ability to recognize patients with different levels of risk. Compared with GCO, GCM is a relatively indolent malignancy. The nomogram developed in this study can help clinicians to assess GCM prognosis.

CT-based radiomics nomogram for prediction of survival after transarterial chemoembolization with drug-eluting beads in patients with hepatocellular carcinoma and portal vein tumor thrombus.

To develop and validate a CT-based radiomics model for the prediction of the overall survival (OS) of patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT) treated with drug-eluting beads transarterial chemoembolization (DEB-TACE). Patients were retrospectively enrolled from two institutions for the constitution of training (n = 69) and validation (n = 31) cohorts with a median follow-up of 15months. A total of 396 radiomics features were extracted from each baseline CT image. Features selected by variable importance and minimal depth were used for random survival forest model construction. The performance of the model was assessed using the concordance index (C-index), calibration curves, integrated discrimination index (IDI), net reclassification index (NRI), and decision curve analysis. Type of PVTT and tumor number were proved to be significant clinical indicators for OS. Arterial phase images were used to extract radiomics features. Three radiomics features were selected for model construction. The C-index for the radiomics model was 0.759 in the training cohort and 0.730 in the validation cohort. To improve the predictive performance, clinical indicators were integrated into the radiomics model to form a combined model with a C-index of 0.814 in the training cohort and 0.792 in the validation cohort. The IDI was significant in both cohorts for the combined model versus the radiomics model in predicting 12-month OS. Type of PVTT and tumor number affected the OS of HCC patients with PVTT treated with DEB-TACE. Moreover, the combined clinical-radiomics model had a satisfactory performance. A CT-based radiomics nomogram, which consisted of 3 radiomics features and 2 clinical indicators, was recommended to predict 12-month overall survival of patients with hepatocellular carcinoma and portal vein tumor thrombus initially treated with drug-eluting beads transarterial chemoembolization. • Type of portal vein tumor thrombus and tumor number were significant predictors of the OS. • Integrated discrimination index and net reclassification index provided a quantitative evaluation of the incremental impact added by new indicators for the radiomics model. • A nomogram based on a radiomics signature and clinical indicators showed satisfactory performance in predicting OS after DEB-TACE.

Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells.

In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy. A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated. The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models. PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.

A nomogram for the prediction of the survival of patients with advanced non-small cell lung cancer and interstitial lung disease.

Lung cancer is frequently accompanied by interstitial lung disease (ILD), and the overall survival (OS) of patients with these comorbidities is poor. Thus, we developed a nomogram for the prediction of the OS of patients with advanced non-small cell lung cancer (NSCLC) and ILD. Patients with wild-type gene advanced NSCLC with and without ILD who underwent chemotherapy between 2014 and 2019 were enrolled in the present study. The 0.5- and 1-year progression-free survival (PFS) and overall survival (OS) times of patients with and without ILD were determined using the Kaplan-Meier method. Cox regression was used to assess the prognostic value of clinical factors for patients with ILD. Based on the multivariate regression results, a nomogram for survival prediction was developed. The nomogram was validated using calibration curve. Data from 155 patients with lung cancer and ILD and 118 matched patients with lung cancer alone who were receiving first-line chemotherapy were analyzed. The first-line chemotherapy regimens were paclitaxel + carboplatin, pemetrexed + carboplatin, gemcitabine + carboplatin, and other. The median PFS and OS were significantly shorter in patients with than in those without ILD (3.0 vs. 7.0 months [p < 0.001] and 7.0 vs. 15.0 months (p < 0.001), respectively). Multivariate analysis showed that the lymphocyte count (hazard ratio [HR] 2.38; 95% confidence interval [CI], 1.44-3.94; p = 0.01), partial pressure of oxygen (PaO2 ; HR, 1.37; 95% CI, 1.03-1.82; p = 0.03), and chemotherapy regimen were independently associated with prognosis. The nomogram showed good discriminatory ability [C-index = 0.69 (95% CI, 0.49-0.82)]. Calibration curves showed that predicted and actual prognoses were consistent. This nomogram can aid the prediction of the OS of patients with advanced NSCLC and ILD.

Identification of HOXB9 to predict prognosis of endometrial cancer based on comprehensive bioinformatics analysis

BackgroundThe HOXB9 gene, which plays a key role in embryonic development, is also involved in the regulation of various human cancers. However, the potential relationship between HOXB9 and endometrial cancer (EC) has not yet been comprehensively analyzed and fully understood.MethodsWe used multiple bioinformatics tools to explore the role of HOXB9 in EC.ResultsThe expression of HOXB9 was significantly upregulated in pan-cancer, including EC (P < 0.05). Quantitative real time polymerase chain reaction (qRT-PCR) experiment confirmed the high expression of HOXB9 in EC from clinical samples (P < 0.001). Double validated by Enrichr and Metascape, HOXB9 showed a strong correlation with HOX family, suggesting that HOX family may also involve in the development of EC (P < 0.05). Enrichment analysis revealed HOXB9 is mainly associated with cellular process, developmental process, P53 signaling pathway, etc. At the single-cell level, the clusters of cells ranked were glandular and luminal cells c-24, glandular and luminal cells c-9, endothelial cells c-15, compared with the other cells. At the genetic level, promoter methylation levels of HOXB9 were significantly higher in tumors than in normal tissues. Furthermore, variations of HOXB9 were closely associated with overall survival (OS) and recurrence free survival (RFS) in EC patients (P < 0.05). The agreement between univariate and multivariate Cox regression indicated that the results were more reliable. Stages III and IV, G2 and G3, tumor invasion ≥ 50%, mixed or serous histological type, age > 60 years, and high expression of HOXB9 were risk factors strongly associated with OS in EC patients (P < 0.05). Therefore, six factors were incorporated to construct a nomogram for survival prediction. Finally, we used the Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and time-dependent ROC to assess predictive power of HOXB9. KM curve showed EC patients overexpressing HOXB9 had a worse OS. AUC of diagnostic ROC was 0.880. AUCs of time-dependent ROC were 0.602, 0.591, and 0.706 for 1-year, 5-year, and 10-year survival probabilities (P < 0.001).ConclusionsOur study provids new insights into the diagnosis and prognosis of HOXB9 in EC and constructs a model that can accurately predict the prognosis of EC.

Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy

BackgroundThe prognosis for hepatocellular carcinoma (HCC) is complex due to its high level of heterogeneity, even after radical resection. This study was designed to develop and validate a prognostic nomogram for predicting the postoperative prognosis for HCC patients following partial hepatectomy.Patients and methodsWe extracted data on HCC patients and randomly divided them into two groups (primary and validation cohorts), using the Surveillance, Epidemiology and End Results (SEER) database. We developed the prediction model based on the data of the primary cohort and prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram was constructed for predicting the 1-, 3-, and 5-year survival probability of HCC patients after surgery based on the results of the multivariate Cox regression analysis. The performance of the nomogram was evaluated in terms of its discrimination and calibration. To validated the model, discrimination and calibration were also evaluated in the validation cohort. Decision curve analysis (DCA) was performed to assess the clinical utility of the nomogram.ResultsA total of 890 patients who underwent partial hepatectomy for HCC were included in the study. The primary cohort enrolled 628 patients with a median follow-up time of 39 months, the 1-, 3-, and 5-year survival rate were 95.4%, 52.7% and 25.8% during follow-up. Multivariate Cox regression analysis showed that differentiation, tumor size, AFP and fibrosis were independently association with the prognosis of HCC patients after partial hepatectomy. The nomogram showed a moderate discrimination ith a C-index of 0.705 (95% CI 0.669 to 0.742), and good calibration. Similar discrimination with a C-index of 0.681 (95% CI 0.625 to 0.737), and calibration were also observed in the validation cohort. Decision curve analysis showed that the nomogram could be useful to predicting the prognosis in HCC patients following partial hepatectomy.ConclusionsThe proposed nomogram is highly predictive and has moderate calibration and discrimination, potentially contributing to the process of managing HCC patients after partial hepatectomy in an individualized way.

Identification of therapeutic targets and prognostic biomarkers in cholangiocarcinoma via WGCNA.

Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor for which limited treatment methods and prognostic signatures are available. This study aims to identify potential therapeutic targets and prognostic biomarkers for CCA. Based on differentially expressed genes (DEGs) identified from The Cancer Genome Atlas (TCGA) data, our study identified key gene modules correlated with CCA patient survival by weighted gene coexpression network analysis (WGCNA). Cox regression analysis identified survival-related genes in the key gene modules. The biological properties of the survival-related genes were evaluated by CCK-8 and transwell assays. Then, these genes were used to construct a prognostic signature that was internally and externally validated. Additionally, by combining clinical characteristics with the gene-based prognostic signature, a nomogram for survival prediction was built. WGCNA divided the 1531 DEGs into four gene modules, and the yellow gene module was significantly associated with overall survival (OS) and histologic neoplasm grade. Our study identified the lncRNA AGAP2-AS1 and a novel gene, GOLGA7B, that are closely related to survival. GOLGA7B downregulation promoted the invasion, migration and proliferation of CCA cells, but AGAP2-AS1 had the opposite effect. AGAP2-AS1 and GOLGA7B were integrated into a gene-based prognostic signature, and both internal and external validation studies confirmed that this two-gene prognostic signature and nomogram could accurately predict CCA patient prognosis. AGAP2-AS1 and GOLGA7B are potential therapeutic targets and prognostic biomarkers for CCA.

Prognostic value of a nomogram based on peripheral blood immune parameters in unresectable hepatocellular carcinoma after intensity-modulated radiotherapy.

For patients with unresectable hepatocellular carcinoma (uHCC), intensity-modulated radiotherapy (IMRT) has become one of the options for clinical local treatment. Immune parameters, including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic immune inflammatory (SII), predict survival in various cancers. This study aimed to determine whether peripheral immune parameters can predict survival in patients with uHCC undergoing IMRT and establish a clinically useful prognostic nomogram for survival prediction. The clinical data of 309 HCC patients were retrospectively analyzed and randomly divided into training (n = 216) and validation (n = 93) cohorts. PLR, NLR and SII were collected before and after IMRT. Univariate and multivariate Cox analyses were performed to identify independent prognostic factors affecting survival, which were used to generate a nomogram. The median survival was 16.3months, and significant increases in PLR, NLR, and SII were observed after IMRT (P < 0.001). High levels of immune parameters were associated with poor prognosis (P < 0.001); enlarged spleen, Barcelona clinic liver cancer stage (B and C), post-SII, and delta-NLR were independent risk factors for survival and were included in the nomogram, which accurately predicted 3- and 5-year survival. The nomogram was well verified in the validation cohort. High levels of immune parameters are associated with poor prognosis in uHCC patients receiving IMRT. Our nomogram accurately predicts the survival of patients with uHCC receiving IMRT.

The prognostic impact of preoperative body mass index changes for patients with esophageal squamous cell carcinoma who underwent esophagectomy: A large-scale long-term follow-up cohort study.

This study aims to investigate the relationship between preoperative body mass index changes (ΔBMI) and prognosis in patients with esophageal squamous cell carcinoma who underwent esophagectomy. We identified 1,883 patients with esophageal squamous cell carcinoma who underwent curative resection in our department between January 2005 and December 2013. Patients were grouped into a stable body mass index (ΔBMI = 0) group and a decreased body mass index (ΔBMI < 0) group. Risk factors for ΔBMI were assessed using logistic regression analysis. The impact of ΔBMI on survival was investigated using Kaplan-Meier curves and Cox regression. A nomogram for survival prediction was constructed and validated. The results showed that T stage (OR: 1.30, 95% CI: 1.16-1.45, P < 0.001) and N stage (OR: 1.24, 95% CI: 1.11-1.38, P < 0.001) were independent risk factors for ΔBMI. The ΔBMI < 0 group had worse overall survival than the stable body mass index group (HR: 1.25, 95% CI: 1.08-1.44, P = 0.002). When stratified by stage, ΔBMI had the greatest prognostic impact in stage I tumors (HR: 1.82, 95%: 1.05-3.15, P = 0.033). In addition, multiple comparisons showed that decreasing ΔBMI correlated with worse prognosis. The ΔBMI-based nomogram presented good predictive ability with a C-index of 0.705. This study demonstrates that ΔBMI < 0 had an adverse impact on the long-term survival of patients with esophageal squamous cell carcinoma undergoing esophagectomy. These results may support further investigation of preoperative nutrition support.

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma

ObjectiveThe incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients.MethodsOverall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan–Meier (K–M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment.ResultsAccording to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723–0.797) in the development cohort and 0.796 (95% CI 0.737–0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K–M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively.ConclusionFor NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

Establishment and validation of an individualized nomogram for survival prediction of primary mediastinal germ cell tumors based on the SEER database.

BackgroundPrimary mediastinal germ cell tumors (PMGCT) represent a rare but sometimes highly aggressive type of mediastinal tumors. The current prognostic models for PMGCT are insufficient. This study was undertaken to establish and validate an individualized nomogram for predicting the overall survival (OS) of patients with PMGCT.MethodsWe conducted a retrospective analysis of patients with PMGCT diagnosed between 2000 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database in the United States. Clinical variables included surgery subtype, gender, treatment regimens, age, histology, tumor size, stage, chemotherapy, radiation, race, and survival-related information. The main outcome measure was survival duration. The Kaplan-Meier method along with the log-rank test were utilized to estimate the OS. Independent prognostic factors were identified by performing the univariate and multivariate Cox proportional hazards regression analyses, from which an individualized nomogram was constructed to predict 3-, 5-, and 10-year OS of patients with PMGCT. The concordance index (C-index) and calibration curve were used to verify the discrimination and accuracy of the nomogram.ResultsA total of 845 patients with PMGCT were recruited from the SEER database and further randomly assigned to a training set (n=635) and a validation set (n=210) at a ratio of 7:3. The 3-, 5-, and 10-year OS for overall PMGCT was 70.0%, 67.1%, and 63.9%, respectively. Cox regression analysis indicated that age, tumor size, stage, chemotherapy, radiation, histology, and surgery type were as independent factors for OS in patients with PMGCT (P<0.05). An individualized nomogram for OS was constructed utilizing these variables, with the C-index of 0.714 [95% confidence interval (CI): 0.695 to 0.743] and 0.756 (95% CI: 0.735 to 0.787) in the training and validation groups. Moreover, good levels of agreement were observed according to the calibration curve between the predicted and actual 3-, 5-, and 10-year survival rates both in the training and validated cohorts, showing that the model could accurately predict patient prognosis.ConclusionsThis study documented the first attempt at establishing and validating a novel nomogram for predicting the 3-, 5-, and 10-year OS probabilities of PMGCT. The prognostic nomogram was demonstrated to have good performance for predicting individualized OS of patients with PMGCT.

Necroptosis-Related Prognostic Signature and Nomogram Model for Predicting the Overall Survival of Patients with Lung Cancer.

Background Necroptosis is a type of programmed cell death mode and it serves an important role in the tumorigenesis and tumor metastasis. The purpose of this study is to develop a prognostic model based on necroptosis-related genes and nomogram for predicting the overall survival of patients with lung cancer. Method Differentially expressed necroptosis-related genes (NRDs) between lung cancer and normal samples were identified. Univariate and LASSO regression analyses were performed to establish a risk score (RS) model, followed by validation within TCGA and GSE37745. The correlation between RS model and tumor microenvironment, mutation status, or drug susceptibility was analyzed. By combining clinical factors, nomogram was developed to predict 1-, 3-, and 5-year survival probability of an individual. The biological function involved by different risk groups was conducted by GSEA. Results A RS model containing six NRDs (FLNC, PLK1, ID1, MYO1C, SERTAD1, and LEF1) was constructed, and patients were divieded into low-risk (LR) and high-risk (HR) groups. Patients in HR group were associated with shorter survival time than those in the LR group; this model had better prognostic performance. Nomogram based on necroptosis score, T stage, and stage had been confirmed to predict survival of patients. The number of resting NK cells and M0 macrophages was higher in HR group. In addition, higher tumor mutational burden and drug sensitivity were observed in the HR group. Patients in HR group were involved in p53 signaling pathway and cell cycle. Conclusion This study constructed a robust six-NRDs signature and established a prognostic nomogram for survival prediction of lung cancer.

A nomogram for survival prediction in 275,812 U.S. patients with breast cancer: a population-based cohort study based on the SEER database.

BackgroundNomograms can assess the risk of clinicopathological features by quantifying the biological and clinical variables of cancer patients. However, the nomogram based on significant factors that influence the survival of breast cancer in a large population has been rarely explored. This study was to investigate the predictive effectiveness of a nomogram for the survival of patients with breast cancer.MethodsDemographic and clinical data of 275,812 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All patients aged ≥20 years in this retrospective cohort study were classified as two groups in a random manner, namely the training set (n=193,069) and validation set (n=82,743). The outcomes of our study were the 3- and 5-year survival of breast cancer. The potential predictors of cancer mortality were screened by univariate and multivariable Cox regression analyses. The nomogram was conducted based on the predictors. Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curve was utilized to evaluate the performance of the nomogram.ResultsThe age at diagnosis, race, marital status, tumor size, first malignant primary indicator, American Joint Committee on Cancer (AJCC) T stage, M stage, tumor grade, and number of malignant tumors were independent predictors for the death of patients with breast cancer. The C-indexes of the training set and the validation set were 0.782 and 0.778, respectively. The area under the curve (AUC) values of the nomogram for predicting the 3- and 5-year survival of breast cancer were 0.770 and 0.756, respectively. Furthermore, the C-index values of our nomogram were 0.816, 0.775, 0.773, 0.734, and 0.750 for predicting survival in Asian, White, Hispanic, American Indian, and Black populations, respectively.ConclusionsThe nomogram may have predictive performance for predicting the 3- and 5-year survival of breast cancer patients, and future studies need to validate our findings.