Overall Survival C-index Research Articles

Novel Prediction Models for Patients With Oral Squamous Cell Carcinoma at Different Anatomical Sites

The individualized prediction of oral cavity squamous cell carcinoma (OC-SCC) is essential and should be as comprehensive as possible. The aim of this study was to identify new risk factors and develop nomograms comparing all anatomic sites of the oral cavity. We performed a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database. All patients with OC-SCC diagnosed from 2004 to 2015 were selected and divided into the training cohort and the validation cohort. Age, gender, race, marital status, primary site, tumor grade, American Joint Committee on Cancer (AJCC) stage, TNM stage, surgical treatment, radiotherapy and chemotherapy were identified as predictor variables. The overall survival (OS) and disease specific survival (DSS) were identified as outcome variables. Kaplan-Meier method with log-rank test, univariate and multivariate cox regression analysis were performed. Independent prognostic factors were used to develop 3- and 5-year nomograms. Hazard ratio (HR) and corresponding 95% confidence interval (CI) showed the influence of each factor on OS or DSS. Concordance indexes (C-indexes) and calibration curves verified the nomograms internally and externally. A total of 12,346 patients were included. Marital status and chemotherapy were independent prognostic factors (P < .05). Tumors occurring on the cheek mucosa had the highest risk in OS (HR, 2.0, 95% CI, 1.7-2.3) and DSS (HR, 4.7, 95% CI, 3.6-6.0), while tumors occurring on the lip had the lowest risk in OS (HR, 1.0) and DSS (HR,1.0). The C-indexes for OS in the training and validation sets were 0.767 and 0.770, respectively, and for DSS were 0.800 and 0.799, respectively. Marital status and chemotherapy independently affect OC-SCC patients' survival. The prognosis is least favorable for tumors occurring on the cheek mucosa and most favorable for tumors occurring on the lip.

Early Tumor Size Reduction of at least 10% at the First Follow-Up Computed Tomography Can Predict Survival in the Setting of Advanced Melanoma and Immunotherapy

Early tumor size reduction (TSR) has been explored as a prognostic factor for survival in patients with advanced melanoma in clinical trials. The purpose of this analysis is to validate, in a routine clinical milieu, the predictive capacity of TSR by 10% for overall survival (OS) and progression-free survival (PFS) and to compare its predictive performance with the RECIST 1.1 criteria. This retrospective study was approved by the local ethics committee. A total of 152 patients with both CT before immunotherapy initiation and at first response evaluation after immunotherapy initiation were included. Prior to statistical analysis, treatment response was trichotomized as follows: Complete response and/or partial response, stable disease and progressive disease. Furthermore, response was dichotomized regarding TSR (TSR ≥ 10% and TSR < 10%). Kaplan-Meier survival estimates, Cox regression and Harrel's concordance index (C-index) were computed for prediction of overall survival and progression-free survival. Tumor size reduction by at least 10% significantly differentiated between patients with increased survival from the ones with decreased survival (median OS: TSR ≥ 10%: 2137 days vs. TSR < 10%: 263 days) (p < 0.001) (median PFS: TSR ≥ 10%: 590 days vs. TSR < 10%: 11 days) (p < 0.001). RECIST 1.1. criteria had a slightly higher C-index for overall survival reflecting a slight superior predictive capacity (RECIST: 0.69 vs TSR: 0.64) but a similar predictive capacity regarding progression-free survival (both: 0. 63). Early tumor size reduction serves as a simple-to-use metric which can be implemented on the first follow-up CT. Tumor size reduction by at least 10% can be considered an additional biomarker predictive of overall survival and progression-free survival in routine clinical care and not only in the context of clinical trials in patients with advanced melanoma undergoing immunotherapy. Nevertheless, RECIST-based criteria should remain the main tool of treatment response assessment until results of prospective studies validating the TSR method are available.

Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data

BackgroundThe clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.MethodsA total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.ResultsA nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.ConclusionThe established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG.

Nomograms to Predict Overall and Cancer-Specific Survival in Gastric Signet-Ring Cell Carcinoma

The objective of our study was to develop and validate nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with signet-ring cell carcinoma (SRCC) of the stomach. Data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 1781 patients were randomly allocated to a training set (n=1335) and a validation set (n=446). Univariate and multivariate analyses were used to determine the prognostic effect of variables. Nomograms were developed to estimate OS and CSS and assessed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC), and decision curve analyses (DCA). DCA was utilized to compare the nomograms and the Tumor-Node-Metastasis (TNM) staging system. Age, race, tumor size, T, N, M stage, and use of surgery and/or radiotherapy were included in the nomograms. C-indexes for OS and CSS were 0.74 and 0.75 in the training set, respectively. C-indexes for OS and CSS were 0.76 and 0.76 in the validation set. Calibration plots and receiver operating characteristic (ROC) curves showed good predictive accuracy. According to the decision curve analyses (DCA), the new model was more useful than the TNM staging system. We developed nomograms to predict OS and CSS in patients with SRCC of the stomach. Nomograms may be a valuable clinical supplement of the conventional TNM staging system.

A Nomogram Model to Predict Prognosis of Patients With Genitourinary Sarcoma

ObjectivesThe aim of this study is to evaluate the significant factors influencing the overall survival (OS) and recurrence free survival (RFS) and make an attempt to develop a nomogram for predicting the prognosis of patients with genitourinary sarcoma (GS).MethodsData on adult GS from 1985 to 2010 were collected. The impact of clinical factors on OS and RFS were estimated by Kaplan–Meier (KM) analysis, and differences between groups were analyzed by the log-rank test. To establish a nomogram, all patients were randomly divided into a training set (n = 125) and a testing set (n = 63). Cox proportion hazard model was utilized to assess the prognostic effect of variables. Then, a nomogram was established to estimate 1-, 3-, and 5-year OS based on Cox regression model. Subsequently, the nomogram was validated by a training set and a validation set.ResultsA total of 188 patients were enrolled into our study. Male patients with bladder sarcoma had better OS rather than RFS when stratified by gender (P = 0.022). According to histological subtypes, patients with leiomyosarcoma (LMS) undergoing chemotherapy were associated with favorable OS (P = 0.024) and RFS (P = 0.001). Furthermore, LMS in kidney sarcoma were associated with lower recurrence rate in comparison to rhabdomyosarcoma (RMS) (P = 0.043). Margin status after surgical excision markedly influenced the OS and RFS of GS patients and negative margins presented optimal prognosis. Chemotherapy was associated with improved OS for patients without surgery (P = 0.029) and patients with positive margins (P = 0.026). Based on the multivariate analysis of the training cohort, age, gender, surgery status, histological subtype, and chemotherapy were included in our nomogram for prediction of OS. The nomogram had sufficient power with concordance index (C-index) of OS: 0.770, 95%CI: 0.760–0.772 and area under curve (AUC) of OS: 0.759, 95%CI: 0.658–0.859 in the training set and with C-index of OS: 0.741, 95%CI: 0.740–0.765, and AUC of OS: 0.744, 95%CI: 0.576–0.913 in the validation set.ConclusionsAdults GS is a group of extremely rare tumors with poor prognosis. Of all histological types, LMS is sensitive to chemotherapy. We highlighted the cardinal role of surgical resection and the importance of achieving negative margins. We identified the efficacy of chemotherapy for patients with positive margins and those without surgery as well. A nomogram is validated as an effective tool predicting short-term outcomes.

Construction and verification of prognostic nomogram for early-onset esophageal cancer.

ABSTRACTThis study aimed to build up nomogram models to evaluate overall survival (OS) and cancer-specific survival (CSS) in early-onset esophageal cancer (EOEC). Patients diagnosed with esophageal cancer (EC) from 2004 to 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Clinicopathological characteristics of younger versus older patients were compared, and survival analysis was performed in both groups. Independent related factors influencing the prognosis of EOEC were identified by univariate and multivariate Cox analysis, which were incorporated to construct a nomogram. The predictive capability of the nomogram was estimated by the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). A total of 534 younger and 17,243 older patients were available from the SEER database. Younger patients were randomly segmented into a training set (n = 266) and a validation set (n = 268). In terms of the training set, the C-index of the OS nomogram was 0.740 (95% CI: 0.707-0.773), and that of the CSS nomogram was 0.752 (95% CI: 0.719-0.785). In view of the validation set, the C-index of OS and CSS were 0.706 (95% CI: 0.671-0.741) and 0.723 (95% CI: 0.690-0.756), respectively. Calibration curves demonstrated the consistent degree of fit between actual and predicted values in nomogram models. From the perspective of DCA, the nomogram models were more beneficial than the tumor-node-metastasis (TNM) and the SEER stage for EOEC. In brief, the nomogram models can be considered as an individualized quantitative tool to predict the prognosis of EOEC patients to assist clinicians in making treatment decisions.

A Prognostic Prediction Model of Transarterial Radioembolization in Hepatocellular Carcinoma: SNAP-HCC.

Prognosis prediction in patient with hepatocellular carcinoma (HCC) after transarterial radioembolization (TARE) remains difficult. The aim of this study was to develop a prognostic model to aid in the decision to use TARE. A total of 174 patients in Korea who underwent TARE for HCC as the initial treatment were included. We developed a prediction model for overall survival (OS) based on independent risk factors for OS and validated the model by bootstrap method. The median maximal size of the tumors was 8.2cm, the median number of tumors was 2, and the median albumin level was 4.0g/dL. Portal vein tumor thrombosis was found in 46.0% (Vp1-3 [39.7%] and Vp4 [6.3%]). Four independent risk factors associated with OS (maximal tumor size, tumor number, albumin, and portal vein tumor thrombosis) were used to develop the SNAP-HCC score. Bootstrap validation of the scoring index determined that the Harrell's c-index for OS was 0.756 (95% confidence interval: 0.729-0.783). Patients grouped based on their SNAP-HCC (scores 0-5) were well discriminated, with significant differences between the groups (all P < 0.05). Patients with SNAP-HCC < 3 showed significantly longer OS than patients with SNAP-HCC ≥ 3 (P < 0.001). The respective survival probabilities at years 1 and 3 were 0.81 and 0.73 in the low-risk (SNAP-HCC < 3) and 0.32 and 0.14 in the high-risk (SNAP-HCC ≥ 3) patients. The SNAP-HCC scoring system predicted the outcome of HCC patients undergoing TARE as an initial treatment. This model could be helpful for initial planning the treatment of HCC patients.

The Epidemiology, Staging and Outcomes of Sarcomatoid Hepatocellular Carcinoma: A SEER Population Analysis.

Hepatocellular carcinoma (HCC) subtypes differ in terms of histopathology and prognosis. Sarcomatoid HCC is rare and literature concerning the survival of patients with sarcomatoid HCC is scarce. Data of patients with sarcomatoid HCC, diagnosed from 1989 to 2016, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We evaluated the baseline and tumor related data, overall survival (OS), disease-specific survival and the performance (Harrell's concordance index - OS c-index) of the eighth edition of the American Joint Committee on Cancer TNM staging system (AJCC8). In addition, univariate and multivariate forward stepwise cox regression analyses were performed to identify factors associated with increased risk of death. The SEER cohort consisted of 71 patients, mostly males (n=49, 69.0%), of White race (n=51, 71.8%) and the most common stage at presentation was stage IVB (n=30, 42.3%). The overall predictive ability of AJCC8 was mediocre, with an OS c-index=0.577 (SE=0.048). Surgery (hazard ratio=0.25, p<0.001) was significantly associated with reduced risk of death. Advanced TNM stage was not associated with increased risk of death. Sarcomatoid HCC, a rare subtype of HCC, is associated with poor outcomes in terms of overall and disease-specific survival across all disease stages. Surgery seems to be of utmost importance. The eighth edition of the AJCC8 for HCC underperforms in predicting the survival of patients with sarcomatoid subtype.

Development and validation of a SEER-based prognostic nomogram for cervical cancer patients below the age of 45 years.

In this study, we established a nomogram for the prognostic prediction of patients with early-onset cervical cancer (EOCC) for both overall survival (OS) and cancer-specific survival (CSS). The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 10,079 patients diagnosed with EOCC between 2004 and 2015; these cases were then randomly divided into training and validation sets. The independent prognostic factors were identified in a retrospective study of 7,055 patients from the training set. A prognostic nomogram was developed using R software according to the results of multivariable Cox regression analysis. Furthermore, the model was externally validated using the data from the remaining 3,024 patients diagnosed at different times and enrolled in the SEER database. For the training set, the C-indexes for OS and CSS prediction were determined to be 0.831 (95 % confidence interval [CI]: 0.815–0.847) and 0.855 (95 % CI: 0.839–0.871), respectively. Receiver operating characteristic (ROC) analysis has revealed that the nomograms were a superior predictor compared with TNM stage and SEER stage. The areas under the curve (AUC) of the nomogram for OS and CSS prediction in the ROC analysis were 0.855 (95 % CI: 0.847–0.864) and 0.782 (95 % CI: 0.760–0.804), respectively. In addition, calibration curves indicated a perfect agreement between the nomogram-predicted and the actual 1-, 3-, and 5-year OS and CSS rates in the validation cohort. Thus, in this study, we established and validated a prognostic nomogram that provides an accurate prediction for 3-, 5-, and 10-year OS and CSS of EOCC patients. This will be useful for clinicians in guiding counseling and clinical trial design for cervical cancer patients.

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis.

Purpose Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis. Methods The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively. Results 3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility. Conclusion Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.

Prognostic Value of Preoperative Fibrinogen for Predicting Clinical Outcome in Patients with Nonmetastatic Colorectal Cancer.

BackgroundThe prognostic role of preoperative fibrinogen in colorectal cancer (CRC) patients remains controversial. Therefore, we assessed the predictive value of preoperative fibrinogen and developed a tool for predicting the survival of CRC patients.MethodsThis retrospective study evaluated 1869 patients who underwent curative resection for CRC. Univariate and multivariate survival analyses were conducted to identify the factors correlated with overall survival (OS) and cancer-specific survival (CSS). Nomograms were developed as a graphical representation of the Cox proportional hazards regression models. The performance of the nomograms was assessed by Harrell’s concordance index (c-index) and calibration plots.ResultsThe preoperative fibrinogen levels were correlated with age, tumor differentiation, tumor location, pT category, and TNM stage. In the multivariate analysis, elevated fibrinogen level was independently correlated with worse OS and CSS (OS: hazard ratio [HR] = 0.777, 95% confidence interval [95% CI] = 0.630–0.958, P = 0.018; CSS: HR = 0.757, 95% CI = 0.605–0.947, P = 0.015). The nomograms could predict outcomes with a c-index for OS and CSS of 0.79 and 0.81, respectively. The nomograms also had a good calibration.ConclusionPreoperative fibrinogen level was an independent marker of poor prognosis in patients with nonmetastatic CRC, and there was a threshold level for the use of fibrinogen as a prognostic factor. Furthermore, nomograms may help predict the individual risk of OS and CSS in patients treated for CRC.

Postoperative Nomogram for Predicting Cancer-Specific and Overall Survival among Patients with Medullary Thyroid Cancer.

Background Medullary thyroid carcinoma (MTC) accounts for 1%–2% of thyroid cancer in the United States based on the latest Surveillance, Epidemiology, and End Results (SEER) data, and this study aimed to construct a comprehensive predictive nomogram based on various clinical variables in MTC patients who underwent total thyroidectomy and neck lymph nodes dissection. Methods Data regarding 1,237 MTC patients who underwent total thyroidectomy and neck lymph nodes dissection from 2004 to 2015 were obtained from the SEER database. Univariate and multivariate Cox regression analyses were used to screen for meaningful independent predictors. These independent factors were used to construct a nomogram model, a survival prognostication tool for 3- and 5-year overall survival, and cancer-specific survival among these MTC patients. Result A total of 1,237 patients enrolled from the SEER database were randomly divided into the training group (n = 867) and the test group (n = 370). Univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors (P < 0.05). Tumor size, age, metastasis status, and LNR were selected as independent predictors of overall survival (OS) and cancer-specific survival (CSS). Finally, two nomograms were developed, and the predicted C-index of overall survival (OS) and cancer-specific survival (CSS) rate in the training group was 0.828 and 0.904, respectively. The predicted C-index of overall survival (OS) and cancer-specific survival (CSS) rate in the test group was 0.813 and 0.828. Conclusion Nomograms constructed by using various clinical variables can make more comprehensive and accurate predictions for MTC patients who underwent total thyroidectomy and neck lymph nodes. These predictive nomograms help identify postoperative high-risk MTC patients and facilitate patient counseling on clinical prognosis and follow-up.

18F-FDG PET/CT Habitat Radiomics Predicts Outcome of Patients with Cervical Cancer Treated with Chemoradiotherapy.

To determine if quantitative features extracted from pretherapy fluorine 18 fluorodeoxyglucose (18F-FDG) PET/CT estimate prognosis in patients with locally advanced cervical cancer treated with chemoradiotherapy. In this retrospective study, PET/CT images and outcomes were curated from 154 patients with locally advanced cervical cancer, who underwent chemoradiotherapy from two institutions between March 2008 and June 2016, separated into independent training (n = 78; mean age, 51 years ± 13 [standard deviation]) and testing (n = 76; mean age, 50 years ± 10) cohorts. Radiomic features were extracted from PET, CT, and habitat (subregions with different metabolic characteristics) images that were derived by fusing PET and CT images. Parsimonious sets of these features were identified by the least absolute shrinkage and selection operator analysis and used to generate predictive radiomics signatures for progression-free survival (PFS) and overall survival (OS) estimation. Prognostic validation of the radiomic signatures as independent prognostic markers was performed using multivariable Cox regression, which was expressed as nomograms, together with other clinical risk factors. The radiomics nomograms constructed with T stage, lymph node status, and radiomics signatures resulted in significantly better performance for the estimation of PFS (Harrell concordance index [C-index], 0.85 for training and 0.82 for test) and OS (C-index, 0.86 for training and 0.82 for test) compared with International Federation of Gynecology and Obstetrics staging system (C-index for PFS, 0.70 for training [P = .001] and 0.70 for test [P = .002]; C-index for OS, 0.73 for training [P < .001] and 0.70 for test [P < .001]), respectively. Prognostic models were generated and validated from quantitative analysis of 18F-FDG PET/CT habitat images and clinical data, and may have the potential to identify the patients who need more aggressive treatment in clinical practice, pending further validation with larger prospective cohorts.Supplemental material is available for this article.© RSNA, 2020.

Establishment and validation of prognostic nomograms to predict overall survival and cancer-specific survival for patients with osteosarcoma.

This study aimed to develop and validate nomograms predicting the survival of osteosarcoma patients from the SEER database and our hospital. Data of 1,066 osteosarcoma patients from the SEER database were randomly divided into a development cohort (n=800) and validation cohort one (n=266). Another cohort of 126 patients from our hospital was utilized as validation cohort two. Univariate and multivariate Cox analyses were performed to identify the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Nomograms predicting the 3- and 5-year OS and CSS probability were constructed and validated. The predictive performances of the established nomograms were evaluated by the concordance index (C-index) and the calibration plot. Variables of age, surgical stage, surgery, grade, tumor site, and tumor size were identified as independent prognosticators for OS and CSS in Cox analyses. The C-indexes for OS and CSS in the development cohort were 0.818 and 0.829. Comparatively, the C-indexes for OS and CSS were 0.843 and 0.834, 0.736 and 0.782 for validation cohort one and two, respectively. Calibration plots showed excellent consistency between nomogram prediction and actual survival. Nomograms based on the SEER database are of high accuracy and can serve as a reliable tool for individualized consultation and survival prediction in osteosarcoma patients.

Prognostic Nomograms Predicting Survival in Patients With Locally Advanced Cervical Squamous Cell Carcinoma: The First Nomogram Compared With Revised FIGO 2018 Staging System.

ObjectivesTo develop nomograms to assess prognostic factors for 5-year overall survival (OS) and 5-year progression-free survival (PFS) in locally advanced cervical squamous cell carcinoma (LACSC).MethodsOverall, 618 patients with LACSC were included in this retrospective analysis. Nomograms for 5-year OS and PFS were developed based on Cox proportional hazards regression models. Concordance index (C-index) and calibration curves were used to define the predictive and discriminatory capacity of the nomogram. A comparison between the nomogram and the International Federation of Gynecology and Obstetrics (FIGO) staging system was conducted using time-dependent receiver operating characteristic (tROC) and area under the curve (tAUC).ResultsMultivariate analysis identified several prognostic factors for OS including squamous cell carcinoma antigen (SCC-Ag), body mass index (BMI), tumor size, pelvic wall involvement, and para-aortic lymph node metastasis (PALNM). Prognostic factors for PFS included BMI, hemoglobin (HGB), tumor size, pelvic wall involvement, pelvic lymph node metastasis (PLNM) and PALNM. Following bootstrap correction, the C-index of OS and PFS was 0.713 and 0.686, respectively. These nomograms showed superior performance compared with the FIGO 2009 and 2018 staging schema.ConclusionsNomograms were developed to identify prognostic factors for 5-year OS and PFS in patients with LACSC. These nomograms showed good prognostication and are more comprehensive in predicting survival outcomes than existing staging criteria.

Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

BackgroundOvarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC.MethodsData of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups.ResultsA total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups.ConclusionsWe have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years.

This study aimed to establish a nomogram for the prognostic prediction of patients with early-onset lung cancer (EOLC) in both overall survival (OS) and cancer-specific survival (CSS). EOLC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further divided into training and validation sets randomly. The prognostic nomobgram for predicting 3-, 5- and 10-years OS and CSS was established based on the relative clinical variables determined by the multivariate Cox analysis results. Furthermore, the predictive performance of nomogram was assessed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve. A total of 1,822 EOLC patients were selected and randomized into a training cohort (1,275, 70%) and a validation cohort (547, 30%). The nomograms were established based on the statistical results of Cox analysis. In training set, the C-indexes for OS and CSS prediction were 0.797 (95% confidence interval [CI]: 0.773-0.818) and 0.794 (95% CI: 0.771-0.816). Significant agreement in the calibration curves was noticed in the nomogram models. The results of ROC and DCA indicated nomograms possessed better predict performance compared with TNM-stage and SEER-stage. And the area under the curve (AUC) of the nomogram for OS and CSS prediction in ROC analysis were 0.766 (95% CI: 0.745-0.787) and 0.782 (95% CI: 0.760-0.804) respectively. The prognostic nomogram provided an accurate prediction of 3-, 5-, and 10-year OS and CSS of EOLC patients which contributed clinicians to optimize individualized treatment plans.

Predictive Risk Factors and Online Nomograms for Synchronous Colon Cancer With Liver Metastasis.

ObjectivesTo develop and validate predictive nomograms of cancer specific survival (CSS) and overall survival (OS) for synchronous colon cancer with liver metastasis (SCLM) patients.MethodsPatients with pathologically diagnosed colon cancer with liver metastasis were retrieved from the SEER database between 2010 and 2015. Only SCLM patients were included. Univariate and multivariate cox regression analyses were conducted to identify the potential predictors of patients’ survival outcomes. The selected variables were integrated to create predictive nomograms via R tools. Furthermore, the concordance index Harrell’s C statistic (C-index) was calculated to describe the discrimination of nomograms. Calibration (1000 bootstrap resamples) curves were plotted to compare the predictions of nomograms with the observed outcomes. Decision curve analysis (DCA) and clinical impact curves were performed to evaluate the clinical effects of nomograms.ResultsA total of 22,378 SCLM patients were included. The median time of OS and CSS was 13 and 17 months, respectively. The 1-, 2-, and 3-year rate of OS was 50.6, 28.1, and 14.8%, respectively. While the 1-, 2-, and 3-year rate of CSS was 58.7, 36.8, and 22.5%, respectively. SCLM patients with increased age, left primary tumor location, AJCC IVb stage, and no chemotherapy were associated with an obviously reduced OS and CSS. Variables including age, histological grade, T/N/M stage, tumor size, bone/lung metastasis, CEA, surgery of primary site, and chemotherapy were closely related to the prognoses of SCLM patients. Nomograms of OS and CSS were built and displayed online for convenient utilization. The C-index of OS and CSS monograms were 0.74 and 0.73, respectively, indicating relatively good discrimination of the nomograms. The calibration curves suggested a good agreement between the actual observation and the nomogram prediction. DCAs and clinical impact curves reflected favorable potential clinical effects of predictive nomograms.ConclusionChemotherapy, surgery of primary site, and age were important independent risk factors for the CSS and OS of SCLM patients. We built and validated two reliable nomograms of OS and CSS to predict the prognoses of SCLM patients, which can be accessed online at (https://predictive-tool.shinyapps.io/CSS-DynNomapp/; https://predictive-tool.shinyapps.io/OS-DynNomapp/).

Nomograms for predicting overall and cancer-specific survival in patients with papillary renal cell carcinoma: a population-based study using SEER database.

BackgroundTo establish and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with papillary renal cell carcinoma (pRCC).MethodsPatients diagnosed with pRCC between 2010 and 2014 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively included in this study and divided into training and validation groups randomly. Uni- and multivariate Cox regression analyses were used to identify significant variables related to OS and CSS in the training group. Based on results of multivariate Cox regression analysis, nomograms for 3- and 5-year CSS and OS were established, respectively. Additionally, Kaplan-Meier (KM) survival curves were produced to learn the actual effects of different variables. Finally, the nomograms were evaluated both in the training group and the validation group using the area under the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves.ResultsA total of 4,859 eligible patients were enrolled, with 3,403 categorized into the training group and 1,456 into the validation group. Seven factors [age, T stage, N stage, M stage, use of surgery/lymph node removal (LNR) and insurance status] were significantly related to OS and seven factors (age, T stage, N stage, M stage and use of surgery/chemotherapy/LNR) were significantly associated with CSS. These factors were eventually included in the predictive nomograms. The C-indexes for OS in the training and validation groups were 0.764 and 0.723 respectively, and 0.859 and 0.824 for CSS. The 3- and 5-year AUCs for OS were 0.779 and 0.752 in the training cohort, and 0.749 and 0.722 in the validation cohort. Similarly, 3- and 5-year AUCs for OS were 0.871 and 0.844 in the training cohort, and 0.853 and 0.822 in the validation group. Finally, the calibration curves suggested that the predictive nomograms had a good consistency between the observed and the predicted survival.ConclusionsIt was the first time to develop nomograms to predict the survival outcomes of pRCC patients. The prognostic nomograms were reliable with high accuracy, which might have guiding significance for clinical practice.

Nomograms to predict overall and cancer-specific survival in patients with upper tract urothelial carcinoma: a large population-based study.

BackgroundTo develop and validate survival nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in upper tract urothelial carcinoma (UTUC) patients.MethodPatients diagnosed with UTUC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively enrolled. Clinical characteristics and survival outcomes were respectively collected from the included patients. Then, eligible patients were divided into the training cohort and the validation cohort. Additionally, survival nomograms were developed based on the results of multivariate Cox analysis in the training cohort. Furthermore, Kaplan-Meier (KM) survival curves were generated to assess the actual effect of each variable. Lastly, the nomograms were validated using the concordance index (C-index), the area under the receiver operating characteristic (ROC) curve and calibration curves.ResultsTotally, 3,556 patients were included, with 2,492 in the training cohort and 1,064 in the validation cohort. No significant differences were detected in comparisons in clinical characteristics between two cohorts. Based on the results of uni- and multivariate Cox regression analysis, seven factors (age, TNM stage, use of surgery/radiation and marital status) for OS and six factors (age, TNM stage and use of surgery/radiation) for CSS were selected to develop the survival nomograms. The C-index for OS and CSS was 0.763 and 0.793 in the training cohort, and 0.759 and 0.784 in the validation cohort. Additionally, the 3- and 5-year AUCs for OS were 0.808 and 0.780 in the training cohort, and 0.785 and 0.778 in the validation group. As for CSS, it was 0.833 and 0.803 in the training cohort, and 0.815 and 0.810 in the validation cohort. Lastly, the calibration curves indicated a good consistency between the actual survival and the predictive survival.ConclusionsIt was the first time to conduct survival models for UTUC patients with predictive performance. It might be valuable of clinical application and further exploration with more studies in the future.