Optimal Agreement Research Articles

A Prognostic Nomogram and Survival Analysis in Relapsed or Refractory Diffuse Large B-Cell Lymphoma Patients

BackgroundThe clinical course of diffuse large B-cell lymphoma (DLBCL) is variable. Although the prognostic markers and models have been developed and adapted in DLBCL, prognostic indices in relapse and refractory (r/r) DLBCL remain unclear. In order to better guide clinical practice, this study aimed to develop a prognostic nomogram in r/r DLBCL.Patients and methodsAmong 1858 DLBCL patients treated in National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital in China, between 2006 and 2016, 682 were r/r DLBCL, of which 573 patients were treated with enough cycles of first-line anthracycline-based chemotherapy. The clinical data were reviewed and evaluated. Progression free survival (PFS) was defined as the time from first treatment administration to disease progression/relapse. Relapse was defined as progression after initial complete or partial remission, and refractory disease was defined as achieving less than a partial response in the first-line setting. Cell of origin (COO) was assessed at diagnosis via Hans algorithm. The concordance index (C-index) and a calibration curve were used to determine and compare their predictive and discriminatory capacity.ResultsOverall, the median age at diagnosis was 54 (range 10-91) years old. Among all the patients, 325 (56.7%) were male and 248 (43.3%) were female. There were 255 (45.1%) patients categorized as stage I-II and 310 (54.9%) categorized as stage III-IV at diagnosis. Of all the patients, 145 (27.0%) were germinal center B-cell-like (GCB) and 393 (73.0%) were non-GCB. According to the Eastern Cooperative Oncology Group (ECOG) performance status (PS) score, the majority of patients had a good performance, with 457 (79.8%) of cases presenting with an ECOG PS score of 0-1; 89 (15.5%) presenting with ECOG PS of 2; 27(4.7%) scored ≥3. In the entire group, 563 (98.3%) patients received CHOP or CHOP-like regimen and 301 (52.5%) received rituximab treatment in the first-line setting. There were 200 patients (34.9%) had refractory disease; 203 (35.4%) had relapse in the first year from treatment commence; 91 (15.9%) had relapse in the second year; and 79 (13.8%) had relapse after the first 2 years. The median overall survival (OS) from diagnosis were 12, 17, 46 and 66 months, respectively in the above four groups, and OS in the entire group was shown in Figure 1. A total of 437 patients with complete clinical information were further divided into the primary (n=300) and validation (n=137) cohorts. Based on a multivariate analysis of the primary cohort, four independent prognostic factors including lactate dehydrogenase (LDH) level at diagnosis; response to the first-line treatment (CR with not CR); progression free survival and recurrence location (original with new with both) were identified and entered the nomogram (Figure 2). The calibration curve showed the optimal agreement between nomogram prediction and actual observation. In addition, the C-index of the nomogram for OS prediction was 0.743. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.75 in the internal validation and 0.84 in the external validation. Four risk groups depending the nomogram were further developed and OS were presented in Figure 3.ConclusionsIndependent predictors of survival in r/r DLBCL have been identified and 4 indicators were used to create a nomogram. This proposed nomogram help with assess individual risks and decide on second-line treatment in clinical practice. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Modeling the determinants of dry bulk FFA trading volume from a cross-market perspective of spot and forward

Purpose This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective. Findings A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors. Originality/value The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

Nomograms for estimating survival in patients with liver-only colorectal metastases: A retrospective study

BackgroundThe aim of this study was to develop and validate nomograms for individual risk prediction in patients with liver-only colorectal metastases (CRLM). MethodsHistologically confirmed CRLM diagnosed between 2010 and 2015 were analysed from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to obtain independent prognostic factors to build nomograms for predicting 1- and 3-year overall survival (OS) and cancer-specific survival (CSS). The predictive accuracy of the nomogram was determined by concordance index (C-index) and calibration plots. ResultsA total of 9615 patients with CRLM were included in the study. A nomogram predicting OS was constructed according to 9 independent clinicopathological factors. A nomogram predicting CSS was constructed based on the same 9 factors. The C-indexes of the nomograms were significantly better than the TNM staging system (7th edition) in both sets for predicting both OS and CSS. The calibration plots displayed an optimal agreement between the predictive results and the actual observed outcomes. ConclusionsThe proposed nomograms can help clinicians calculate the probability in patients with CRLM.

Development and Validation of a Nomogram for Assessing Survival in Patients With Metastatic Lung Cancer Referred for Radiotherapy for Bone Metastases

A survival prediction model for patients with bone metastases arising from lung cancer would be highly valuable. To develop and validate a nomogram for assessing the survival probability of patients with metastatic lung cancer receiving radiotherapy for osseous metastases. In this prognostic study, the putative prognostic indicators for constructing the nomogram were identified using multivariable Cox regression analysis with backward elimination and model selection based on the Akaike information criterion. The nomogram was subjected to internal (bootstrap) and external validation; its calibration and discriminative ability were evaluated with calibration plots and the Uno C statistic, respectively. The training and validation set cohorts were from a tertiary medical center in northern Taiwan and a tertiary institution in southern Taiwan, respectively. The training set comprised 477 patients with metastatic lung cancer who received radiotherapy for osseous metastases between January 2000 and December 2013. The validation set comprised 235 similar patients treated between January 2011 and December 2017. Data analysis was conducted May 2018 to July 2018. The nomogram end points were death within 3, 6, and 12 months. Of 477 patients in the training set, 292 patients (61.2%) were male, and the mean (SD) age was 62.86 (11.66) years. Of 235 patients in the validating set, 113 patients (48.1%) were male, and the mean (SD) age was 62.65 (11.49) years. In the training set, 186 (39%), 291 (61%), and 359 (75%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 4.21 (95% CI, 3.68-4.90) months. In the validating set, 84 (36%), 120 (51%), and 144 (61%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 5.20 (95% CI, 4.07-7.17) months. Body mass index (18.5 to <25 vs ≥25: hazard ratio [HR], 1.42; 95% CI, 1.14-1.78 and <18.5 vs ≥25: HR, 2.31; 95% CI, 1.56-3.44), histology (non-small cell vs small cell lung cancer: HR, 0.59; 95% CI, 0.41-0.86), epidermal growth factor receptor mutation (positive vs unknown: HR, 0.66; 95% CI, 0.46-0.93 and negative vs unknown: HR, 0.98; 95% CI, 0.66-1.45), smoking status (ever smoker vs never smoker: HR, 1.50; 95% CI, 1.24-1.83), age, and neutrophil to lymphocyte ratio were incorporated. The HRs of age and neutrophil to lymphocyte ratio were modeled nonlinearly with restricted cubic splines (both P < .001). The nomogram's discriminative ability was good in the training set (C statistic, ≥0.77; P < .001) and was validated using both an internal bootstrap method (C statistic, ≥0.76; P < .001) and an external validating set (C statistic, ≥0.75; P < .001). The calibration plots for the end points showed optimal agreement between the nomogram's assessment and actual observations. The nomogram (with web-based tool) can be useful for assessing the probability of survival at 3, 6, and 12 months in patients with metastatic lung cancer referred for radiotherapy to treat bone metastases, and it may guide radiation oncologists in treatment decision making and engaging patients in end-of-life discussions and/or hospice referrals at appropriate times.

P1.16-62 A Nomogram to Predict Disease-Free Survival After Curative Resection of Non-Small Cell Lung Cancer

The aim of this study was to find clinicopathologic variables associated with disease-free survival (DSF) and develop a prognostic nomogram for patients with curative resected non-small-cell lung cancer (NSCLC) and compare the value of the nomogram to the seventh AJCC TNM classification. A nomogram to predict DFS following surgical resection of NSCLC was constructed using a single-institutional cohort of 805 patients who underwent curative resection of lung adenocarcinoma in the Cancer Hospital of the Chinese Academy of Medical Sciences (Beijing, China) between January 2003 and December 2013. For nomogram construction and validation, we randomly assigned two thirds of the patients to the training cohort (n=605) and one third to the validation cohort (n=260). The median age of training cohort was 57 years (range, 25-76) and the proportion of male and female patients was 64.8% vs. 35.2%. Half of the patients (49.8%) presented with early-stage disease and 85.3% patients received adjuvant chemotherapy alone. The predictive discriminative and calibration were measured by concordance index (C-index), risk group stratification and calibration plot. The median disease-free survival for the total cohort was 23.2 months (95% CI, 21.3-26.4 months). The 1-, 3- and 5-year disease-free survival was 71.3%, 39.5% and 32.3%, respectively. The nomogram included 8 important variables based on a Cox proportional hazards regression modeling of the training cohort: histology, differentiation, nodal status, AJCC T category, primary tumor location, type of surgical resection, adjuvant radiotherapy and regimen. Each patient with a higher score had a worse prognosis. Prognostic discrimination was performed by dividing the predicted nomogram score into quartiles that were then used to plot Kaplan-Meier curves. The nomogram was able to stratify patients into 4 distinct incremental 3-year prognostic groups (quartile 1, 73.0%; quartile 2, 49.6%; quartile 3, 30.6%; and quartile 4, 9.3% [P <0.001]). Both in the training and validation cohort, the C-index of the nomogram for DFS prediction was superior to the TNM category prediction (training cohort, 0.694 vs. 0.640; P<0.01 and validation cohort, 0.662 vs. 0.602; P<0.01). The 200-sample bootstrapped calibration curves for probability of 3- and 5-year disease-free survival (PFS) showed optimal agreement between nomogram prediction and actual observation. This novel nomogram provides an individualized prediction of DFS in NSCLC patients after radical surgery and may assist the clinical treatment decision making and clinical trials designing.

Development and Validation of Nomograms for Predicting Overall and Breast Cancer–Specific Survival in Young Women with Breast Cancer: A Population-Based Study

INTRODUCTION: The objective of current study was to develop and validate comprehensive nomograms for predicting the survival of young women with breast cancer. METHODS: Women aged <40 years diagnosed with invasive breast cancer between 1990 and 2010 were selected from the Surveillance, Epidemiology, and End Results database and randomly divided into training (n = 12,465) and validation (n = 12,424) cohorts. A competing-risks model was used to estimate the probability of breast cancer–specific survival (BCSS). We identified and integrated significant prognostic factors for overall survival (OS) and BCSS to construct nomograms. The performance of the nomograms was assessed with respect to calibration, discrimination, and risk group stratification. RESULTS: The entire cohort comprised 24,889 patients. The 5- and 10-year probabilities of breast cancer–specific mortality were 11.6% and 20.5%, respectively. Eight independent prognostic factors for both OS and BCSS were identified and integrated for the construction of the nomograms. The calibration curves showed optimal agreement between the predicted and observed probabilities. The C-indexes of the nomograms in the training cohort were higher than those of the TNM staging system for predicting OS (0.724 vs 0.694; P < .001) and BCSS (0.733 vs 0.702; P < .001). Additionally, significant differences in survival were observed in patients stratified into different risk groups within respective TNM categories. CONCLUSIONS: We developed and validated novel nomograms that can accurately predict OS and BCSS in young women with breast cancer. These nomograms may help clinicians in making decisions on an individualized basis.

A nomogram prediction for the survival of patients with triple negative breast cancer.

Triple negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis. In this study, we aimed to conduct a nomogram to predict the survival of individual with TNBC by incorporating significant clinical and laboratory parameters. 404 TNBC patients from the Affiliated Union Hospital of Fujian Medical University between 2006 and 2012 were selected in the training cohort. Cox univariate and multivariate regression analyses were adopted to identify independent prognostic factors. The predictive accuracy and discriminative ability of this nomogram were evaluated by concordance index (C-index) and calibration curve. The accuracy of this nomogram was also compared with the 8th AJCC TNM staging system. An external validation cohort was further performed in an independent cohort of 200 patients between 2012 and 2014. Seven independent prognostic factors, including family history of breast cancer, tumor location, number of positive lymph nodes, histological grade, serum CEA, CA125 and CA153 were identified as independent prognostic factors. A nomogram incorporating these prognostic factors was subsequently conducted and the calibration plot on the probability for 3 or 5 years overall survival (OS) showed an optimal agreement between the nomogram prediction and actual observations. In addition, the C-index of this nomogram was higher than that of TNM staging system in both training and validation cohort (training cohort, 0.76 vs. 0.66, p<0.001 and validation cohort, 0.72 vs. 0.64, p=0.002, respectively). This proposed nomogram could provide more accurate individual prediction for the prognosis of the patients with TNBC and was able to help physicians to identify subgroups of patients at different risk and to decide who need intensive follow-up or additional treatment.

Establishment and external validation of a prognostic model for predicting disease-free survival and risk stratification in breast cancer patients treated with neoadjuvant chemotherapy.

BackgroundThe eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system for survival prediction and risk stratification in breast cancer (BC) patients after neoadjuvant chemotherapy (NCT) is of limited efficacy. This study aimed to establish a novel prognostic nomogram for predicting disease-free survival (DFS) in BC patients after NCT.Patients and methodsA total of 567 BC patients treated with NCT, from two independent centers, were included in this study. Cox proportional-hazards regression (CPHR) analysis was conducted to identify the independent prognostic factors for DFS, in order to develop a model. Subsequently, the discrimination and calibration ability of the prognostic model were assessed in terms of its concordance index (C-index), risk group stratification, and calibration curve. The performance of the nomogram was compared with that of the eighth edition of the AJCC TNM staging system via C-index.ResultsBased on the CPHR model, eight prognostic predictors were screened and entered into the nomogram. The prognostic model showed better performance (p<0.01) in terms of DFS prediction (C-index: 0.738; 95% CI: 0.698–0.779) than the eighth edition of the AJCC TNM staging system (C-index: 0.644; 95% CI: 0.604–0.684). Stratification into three risk groups highlighted significant differences between the survival curves in the training cohort and those in the validation cohort. The calibration curves for likelihood of 3- and 5-year DFS indicated optimal agreement between nomogram predictions and actual observations.ConclusionWe constructed and externally validated a novel nomogram scoring system for individualized DFS estimation in BC patients treated with NCT. This user-friendly predictive tool may help oncologists to make optimal clinical decisions.

High-resolution manometry is superior to endoscopy and radiology in assessing and grading sliding hiatal hernia: A comparison with surgical invivo evaluation.

Hiatal hernia is diagnosed by barium-swallow esophagogram or esophagogastroduodenoscopy, with possible suboptimal results. High-resolution manometry clearly identifies crural diaphragm and lower esophageal sphincter. To assess the diagnostic accuracy of high-resolution manometry in detecting hiatal hernia compared to esophagogram and esophagogastroduodenoscopy, using as reference the surgical invivo measurement. Patients were studied with esophagogram, esophagogastroduodenoscopy, high-resolution manometry and invivo evaluation of the esophago-gastric junction. Esophago-gastric junction was classified as type I (no separation between crural diaphragm and lower esophageal sphincter); type II (≥1, ≤ 2 cm separation); type III (>2 cm). During invivo measurement, distance between the esophago-gastric junction and crural diaphragm proximal border was recorded. Surgery identified 53 hiatal hernias in 100 patients. Forty-seven percent were classified as type I esophago-gastric junction, 35% type II and 18% type III. Referenced to invivo evaluation, high-resolution manometry showed superior diagnostic sensitivity and specificity (94.3% and 91.5%, respectively) to esophagogram and esophagogastroduodenoscopy, with 92.6% predictive value of a positive test and 93.5% predictive value of a negative test. The kappa value for high-resolution manometry and invivo evaluation was 0.85. High-resolution manometry showed optimal sensitivity and specificity in detecting types I, II and III esophago-gastric junction. High-resolution manometry enables an accurate diagnosis of hiatal hernia and a better classification than endoscopy and radiology, reaching optimal agreement with invivo assessment.

A nomogram for predicting lymph node metastasis in surgically resected T1 esophageal squamous cell carcinoma.

Endoscopic therapies for T1 esophageal carcinoma have been increasingly used around the world. However, the procedures are limited by without lymph nodes harvested. The risk of lymph node metastasis (LNM) should been established. Our objective was to construct a nomogram model for predict risks of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC). We reviewed the records of 221 patients with pT1 ESCC who underwent surgical resection and radical lymphadenectomy. Clinicopathological variables were analyzed univariate and multivariate logistic regression analysis. A nomogram for predicting risk of LNM was constructed and validated using bootstrap resampling. Of the 221 patients, 53 patients had been examined as LNM. Following multivariate analysis, poor differentiation (P=0.0006), lymphovascular invasion (P<0.0001) and SM3 (tumor invades the lower third of the submucosal layer) (P=0.0192) cancer were significantly independent risk factors for LNM and were entered into the nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.8667. The calibration curves for the probability of LNM showed optimal agreement between the probability as predicted by the nomogram and the actual probability. We established a nomogram that can provide individual predicting for LNM in T1 ESCC, and this model has the potential clinical utility in making therapeutic procedures.

Validation of an Integrative and Dynamic Prognostic Score for Response to Therapy in Solid Organ Transplants

Introduction Drug development in transplantation has diminished due to the lack of suitable endpoints and the long time span between transplantation and graft failure. We sought to validate the performance of an integrative scoring system for predicting long-term kidney allograft loss in the setting of therapeutic interventions after transplantation. Materials and methods We used an integrative risk-scoring system that predicts the long-term allograft failure, previously derived and validated in an international cohort comprising 5,125 kidney transplant recipients (http//http://www.paristransplantgroup.org). The risk score is based on readily-accessible clinical, biochemical, immunological and histological parameters measured after transplant. We included patients from the Paris Transplant Group reference cohort who underwent therapeutic interventions following standardized protocols for antibody-mediated rejection (ABMR), T-cell mediated rejection (TCMR) and CNI toxicity and tested the performance of the risk score for predicting response to therapy in the 3 clinical scenarios. ABMR patients received standard of care treatment, plasma exchange, and anti-CD-20 rituximab. Patients with TCMR received 3 IV methylprednisolone pulses together with oral steroid tapering. The last group consisted of patients diagnosed with CNI toxicity by biopsy and converted to Belatacept (CTLA4-Ig). All patients underwent risk score measurement at treatment and 3 months after treatment. The outcome measure was the performance of the score to predict long-term allograft failure as compared with the actual events observed (kidney allograft loss). Results 484 patients were included: 224 receiving SOC ABMR treatment; 143 receiving SOC treatment for TCMR and 117 receiving Belatacept for CNI toxicity. The mean time follow-up post transplantation was 4.048 ± 3.07 years. The mean time between transplant and diagnosis was 385 ± 808 days. The prognostic risk score was significantly modified by the therapeutic interventions (mean risk score of 3.01 ± 0.79 at the time of treatment vs 2.71±0.82 after treatment, p<0.001; Figure 1A). 2 prototypes of patients were identified by the risk score: i) Group 1 showing decreasing predicted probability of graft loss after treatment (blue lines, responders, 68%); ii) Group 2 showing stable or increased predicted probability of graft loss after treatment (red lines, non-responders, 32%). The risk score prediction capability of individual patient long-term allograft loss was highly accurate (C-index 0·84; 95% bootstrap percentile CIs=0·80-0·89). The calibration plot showed an optimal agreement between the risk score prediction model after therapeutic intervention and the actual observation of kidney allograft loss at 3, 5 and 7 years after treatment (Figure 1B).Conclusion This integrative risk scoring system for long-term allograft loss showed high performance in the setting of therapeutic interventions after kidney transplant, correlated well with the true clinical outcome and captured the net effect of treatment on the clinical outcome. Our result suggests that this scoring system could be used as a valid surrogate endpoint for next-generation multicenter trials and in the approval of drugs in solid organ transplantation.

An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy

BackgroundEmerging inflammatory response biomarkers are developed to predict the survival of patients with cancer, the aim of our study is to establish an inflammation-related nomogram based on the classical predictive biomarkers to predict the survivals of patients with non-small cell lung cancer (NSCLC).MethodsNine hundred and fifty-two NSCLC patients with lung cancer surgery performed were enrolled into this study. The cutoffs of inflammatory response biomarkers were determined by Receiver operating curve (ROC). Univariate and multivariate analysis were conducted to select independent prognostic factors to develop the nomogram.ResultsThe median follow-up time was 40.0 months (range, 1 to 92 months). The neutrophil to lymphocyte ratio (cut-off: 3.10, HR:1.648, P = 0.045) was selected to establish the nomogram which could predict the 5-year OS probability. The C-index of nomogram was 0.72 and the 5-year OS calibration curve displayed an optimal agreement between the actual observed outcomes and the predictive results.ConclusionsNeutrophil to lymphocyte ratio was shown to be a valuable biomarker for predicting survival of patients with NSCLC. The addition of neutrophil to lymphocyte ratio could improve the accuracy and predictability of the nomogram in order to provide reference for clinicians to assess patient outcomes.

Long-term survival after partial hepatectomy for sub-stage patients with intermediate stage hepatocellular carcinoma

BackgroundThe current study aimed to examine the long-term survival after partial hepatectomy for patients with BCLC intermediate stage hepatocellular carcinoma (HCC) stratified by the Bolondi's sub-staging model. Materials and MethodsThis cohort consisted of 360 patients with BCLC intermediate stage HCC who underwent partial hepatectomy between January 2008 and February 2010. Patients were stratified into 3 subgroups (B1-B3) based on the Bolondi's sub-staging model. The last follow-up was conducted at February 2014. ResultsOf these patients, 166, 171 and 23 patients had B1, B2, and B3 sub-stage HCC, respectively. The postoperative 5-year Overall survival (OS) rate for patients with these three sub-stages was 49.5%, 33.7% and 12.9%, respectively (P < 0.001). Compared with the reported survival outcomes from previous studies which used transarterial chemoembolization (TACE) as first-line treatment, hepatectomy had a better median survival than TACE in B1 and B2 patients. On multivariable analysis, presence of esophageal and gastric varices, higher NDR score, presence of microvascular invasion, differentiation grade III-IV, and patterns of AFP decreases after surgery were the independent risk factors of OS in the sub-stages B1 and B2 patients. A nomogram which integrated all these independent risk factors was developed, with a C-index of 0.71 for OS prediction. The calibration curve showed an optimal agreement between prediction by the nomogram and actual observation. ConclusionsThe patients with intermediate stage HCC clarified as sub-stages B1 and B2 according to Bolondi's model had an optimal long-term survival following partial hepatectomy than TACE. Their postoperative prognosis could be accurately predicted by our proposed nomogram.

Development and Validation of Web-Based Nomograms to Precisely Predict Conditional Risk of Site-Specific Recurrence for Patients With Completely Resected Non-small Cell Lung Cancer: A Multiinstitutional Study

There is currently no consensus regarding the optimal postoperative follow-up strategy for patients with completely resected non-small cell lung cancer (NSCLC). We aimed to develop web-based nomograms to precisely predict site-specific postoperative recurrence in patients with NSCLC and to guide individual surveillance strategies including when to follow up and what diagnostic tests to perform. We investigated the pattern of recurrence in a series of 2,017 patients with NSCLC (squamous cell carcinoma and nonlepidic invasive adenocarcinoma) who underwent complete surgical resection at Fudan University Shanghai Cancer Center (development cohort), and developed web-based clinicopathologic prediction models for conditional risk of site-specific recurrence based on Cox regression. The variables used in the analysis included sex, age, smoking history, tumor size, tumor histology, lymphovascular invasion, visceral pleural invasion, and pathologic TNM stage. A separate cohort of 3,308 patients with NSCLC from Shanghai Chest Hospital was used for external validation. In the development cohort and the external validation cohort for the established nomograms to predict overall recurrence, thorax recurrence, abdomen recurrence, neck recurrence, brain recurrence, and bone recurrence, the C-statistics of Harrell etal were 0.743 and 0.748, 0.728 and 0.703, 0.760 and 0.749, 0.779 and 0.757, 0.787 and 0.784, and 0.777 and 0.739, respectively. The calibration plots showed optimal agreement between nomogram-predicted 3-year recurrence-free survival and actual 3-year recurrence-free survival. These user-friendly nomograms can precisely predict site-specific recurrence in patients with completely resected NSCLC, based on clinicopathologic features. They may help physicians to make individual postoperative follow-up plans.

A nomogram integrating hepatic reserve and tumor characteristics for hepatocellular carcinoma following curative liver resection

BackgroundBecause of the mutual influence of liver dysfunction and malignancy, overall survival (OS) is a composite clinical endpoint in hepatocellular carcinoma (HCC). We developed a nomogram integrating albumin–bilirubin (ALBI) grade, a new index of hepatic reserve, and tumor characteristics of HCC for predicting OS following curative liver resection. MethodsThe nomogram was built to estimate the probabilities of 1, 3, and 5-y OS based on training cohort of 709 HCC, which was validated in an international independent dataset. The prognostic value of the nomogram was determined by concordance index (C-index), time-dependent receiver operating characteristics (tdROC), and decision curves, comparing with ALBI grade alone, the Cancer of the Liver Italian Program (CLIP), the Barcelona Clinic Liver Cancer (BCLC), and Okuda staging systems. ResultsIndependent factors derived from multivariable Cox analysis of the training cohort to predict OS were tumor grade, microvascular invasion, tumor size and ALBI grade which were assembled into nomogram. The calibration curves for probability of OS showed optimal agreement between nomogram-prediction and actual observation, which was tested in validation cohort. The C-index, tdROC and decision curves showed the nomogram was superior to CLIP, ALBI grade, BCLC and Okuda. The patients could also be stratified into low, intermediate risk, and high risk of the mortality by the normogram in both development and validation cohorts. ConclusionsThe nomogram integrating hepatic reserve and tumor characteristics provided a highly accurate estimation of OS in patients with HCC after curative liver resection, contributing to assess patient prognosis.

Abstract 646: A Simple Multi-Risk-Factor Decision-Making Strategy for Improved Coronary Plaque Burden Increase Prediction: a Patient-Specific 3D FSI Study Using IVUS Follow-up

Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find simple methods to combine multiple risk factors for better plaque development predictions. A sample size of 374 intravascular ultrasound (IVUS) slices with matched follow-up was obtained from 9 patients (Mean age 59, 7 m) with informed consent obtained. 3D fluid-structure interaction models were constructed to obtain plaque stress/strain conditions. Four morphological and biomechanical factors (plaque burden (PB), cap thickness (CT), lipid percent (LP) and average plaque wall stress (PWS)) were chosen to predict plaque burden increase defined as PBI = (PB at follow-up) - (PB at baseline). For a given slice Si, the ground truth Y PBI is define as Y PBI (Si)=1 if PBI(Si)&gt;0; Y PBI =0 if PBI(Si)≤0. For a single predictor W, a threshold value Wc was used to assign the binary prediction outcome: Y W (Si)=1 if W&gt;Wc; Y W (Si)=0 if W≤Wc. Wc was chosen to get optimal agreement between Y W and Y PBI . To use multiple predictors (say, W1, W2, W3) to PBI, a new predictor Combo(W1,W2,W3) was created with its values defined as Combo(W1,W2,W3)=Y W1 +Y W2 +Y W3 , where Y W1 , Y W2 and Y W3 were evaluated the same way as before. Combo was then treated as a single predictor and a threshold value was determined to achieve best agreement with Y PBI . Table 1 summarizes the optimal thresholds and agreement rates for all 15 strategies. Agreement rate using PB alone was 57.5%. PWS was the best single predictor for PBI with agreement rate 62.6%. Combining CT and PWS achieved 66.5% agreement rate, 9% better over PB, which was also obtained by combining 4 risk factors. The method presented here could be used to combine predictors from different sources (stenosis, cap, lipid, inflammation, macrophage, hemorrhage, stress, strain, flow shear stress, FFR, smoking, diabetes, cholesterol, alcohol, hypertension, pro-rupture genes, etc.) to improve prediction accuracy and help decision-making in clinical practice.

Abstract 345: Combining Morphological and Mechanical Risk Factors May Improve Carotid Plaque Progression Prediction: an Magnetic Resonance Image-Based Follow-Up Study

It has been hypothesized that combining morphological and mechanical risk factors may improve plaque progression prediction. In this paper, plaque burden (PB), cap thickness (CT), lipid percent (LP), plaque wall stress (PWS), plaque wall strain (PWSn) and their combinations were used as predictors to identify the best predictor(s) for plaque progression measured by plaque area increase (PAI) from baseline (T1) and follow-up (T2). In vivo magnetic resonance image (MRI) carotid plaque data were acquired from 8 patients (5 m, mean age 71) with follow-up (18 months) with informed consent obtained. We built 3D thin-layer models for the 41 matched slices to obtain plaque stress/strain data using patient-specific material properties determined from Cine MRI and patient arm blood pressure. The mean values of the 5 predictors from all lumen points of each slice were obtained for analysis. For each predictor Y, a threshold value Yc was determined so that optimal agreement rate was obtained (agreement cases are either {(Y&gt;Yc) and (PAI &gt;0)} or {(Y≤Yc) and (PAI ≤0)}). For PWS and PWSn, we used (either {(Y&lt;Yc) and (PAI &gt;0)} or {(Y≥Yc) and (PAI ≤0)}). Two combinations of predictors COM1=a1*PB+a2*CT+a3*LP, COM2=b1*PB+b2*CT+b3*LP+b4*PWS+b5*PWSn were considered, where COM1 combines PB with morphological features, and COM2 combines all 5 predictors. The coefficients in these formulas were determined by using iterative linearly fitting method and initial guess of threshold values from single predictor results. The optimal threshold values for the 5 single predictors and 2 combination predictors are given in Table 1. PWSn was the best single predictor and COM2 was the best combination predictor. The threshold value-based method is simple and easy for clinical implementation. Our preliminary results indicate that combining PB, CT, LP, PWS, and PWSn could improve the agreement rate by nearly 10%, compared to using PB alone. Large-scale patient studies are needed to confirm our findings.

Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis

BackgroundDevelopment demand of precise medicine in resectable esophageal squamous cell carcinoma (ESCC) require to recognize patients at high risk treated by surgery alone. Thus, our aim was to construct a clinical nomogram and recursive partitioning analysis (RPA) to predict long-term survival in ESCC treated by surgery alone. MethodsBased on the patients with ESCC who treated by three-incisional esophagectomy and two-field lymphadenectomy alone, we identified and integrated significant prognostic factors for survival to build a nomogram. The nomogram was calibrated for overall survival (OS) and the predictive accuracy and discriminative ability was measured by concordance index (c-index) and Akaike information criterion (AIC). Based on the nomogram, the RPA was performed for risk stratification. ResultsA total of 747 patients were included for analysis. Five independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation. The AIC value of the nomogram was lower than that of the 7th edition staging system, whereas the c-index of the nomogram was higher than that of the 7th edition staging system. The risk groups stratified by RPA allowed significant distinction between survival curves within respective TNM categories. ConclusionThe RPA based on a clinical nomogram appears to be suitable for risk stratification in OS for resected ESCC. This practical system may help clinicians in decision making and design of clinical studies.

Comparison of the everolimus concentrations measured in whole blood with everolimus QMS or sirolimus CMIA

Few years ago, it was proposed that everolimus blood levels could be determined with the commercially available sirolimus chemiluminescence magnetic microparticle immunoassay (CMIA). More recently, a highly specific microsphere system (QMS) has been approved by FDA for therapeutic drug monitoring in humans. Aim of the present study was to compare the results of everolimus assay performed with everolimus QMS and with sirolimus CMIA. The two methods were compared with Passing–Bablok regression and Bland–Altman plot analysis. The Passing–Bablok regression analysis showed that although the results obtained with the two techniques were significantly correlated, CMIA-measured differed from QMS-measured everolimus concentrations by both a systematic and a proportional error. Specifically, at blood levels lower than 5 ng/mL CMIA were lower than QMS-measured everolimus concentrations. On the opposite, at everolimus blood concentrations higher than 10 ng/mL CMIA-estimated values became progressively higher than QMS-measured everolimus concentrations. The analysis of the Bland Altman plot showed a less than optimal agreement of the two tests (5.59% of the data point outside the ±1.96 SD interval). Moreover, the relationship between the difference between EveroQMS and EveroCMIA and their average was clearly concentration dependent with positive and negative values at concentration values lower and higher than 5 ng/mL respectively.In conclusion, our finding showed that the values of everolimus concentrations measured with sirolimus CMIA differ from those detected with the FDA-approved everolimus QMS further suggesting that sirolimus CMIA should not be used anymore for everolimus therapeutic drug monitoring.

Catastrophe and Cooperation

We study international environmental agreements in the presence of a potential climate catastrophe between sovereign countries that are heterogeneous in their exposure to climate change. We do so by analyzing a stochastic game with an absorbing state. The equilibrium structure of this game is very different from the infinitely repeated games that are usually studied in the literature on environmental agreements. In particular, there is no “folk theorem” that guarantees that the social optimum can be sustained in a Nash equilibrium as long as players are sufficiently patient. However, often, it is feasible to implement an abatement scheme with the same level of aggregate abatement as in the social optimum, but the distribution of abatement among countries is sub-optimal. Moreover, the discount rate has a non-monotonic effect on the optimal environmental agreement.