Post-hepatectomy liver failure prediction and prevention: Development of a nomogram containing postoperative anticoagulants as a risk factor

Introduction and objectivesPosthepatectomy liver failure (PHLF) is a serious complication after hepatectomy, and its effective methods for preoperative prediction are lacking. Here, we aim to identify predictive factors and build a nomogram to evaluate patients’ risk of developing PHLF.Patients and methodsA retrospective review of a training cohort, including 199 patients who underwent hepatectomy at the Shanghai Eastern Hepatobiliary Surgery Hospital, was conducted. Independent risk variables for PHLF were identified using multivariate analysis of perioperative variables, and a nomogram was used to build a predictive model. To test the predictive power, a prospective study in which a validation cohort of 71 patients was evaluated using the nomogram. The prognostic value of this nomogram was evaluated by the C-index.ResultsIndependent risk variables for PHLF were identified from perioperative variables. In multivariate analysis of the training cohort, tumor number, Pringle maneuver, blood loss, preoperative platelet count, postoperative ascites and use of anticoagulant medications were determined to be key risk factors for the development of PHLF, and they were selected for inclusion in our nomogram. The nomogram showed a 0.911 C-index for the training cohort. In the validation cohort, the nomogram also showed good prognostic value for predicting PHLF. The validation cohort was used with similarly successful results to evaluate risk in two previously published study models with calculated C-indexes of 0.718 and 0.711.ConclusionOur study establishes for the first time a novel nomogram that can be used to identify patients at risk of developing PHLF.     

Predicting persistent organ failure on admission in patients with acute pancreatitis: development and validation of a mobile nomogram

BackgroundEarly prediction of persistent organ failure (POF) is important for triage and timely treatment of patients with acute pancreatitis (AP).MethodsAll AP patients were consecutively admitted within 48 h of symptom onset. A nomogram was developed to predict POF on admission using data from a retrospective training cohort, validated by two prospective cohorts. The clinical utility of the nomogram was defined by concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC), while the performance by post-test probability.ResultsThere were 816, 398, and 880 patients in the training, internal and external validation cohorts, respectively. Six independent predictors determined by logistic regression analysis were age, respiratory rate, albumin, lactate dehydrogenase, oxygen support, and pleural effusion and were included in the nomogram (web-based calculator: https://shina.shinyapps.io/DynNomapp/). This nomogram had reasonable predictive ability (C-indexes 0.88/0.91/0.81 for each cohort) and promising clinical utility (DCA and CIC). The nomogram had a positive likelihood ratio and post-test probability of developing POF in the training, internal and external validation cohorts of 4.26/31.7%, 7.89/39.1%, and 2.75/41%, respectively, superior or equal to other prognostic scores.ConclusionsThis nomogram can predict POF of AP patients and should be considered for clinical practice and trial allocation.     

Development and multicenter validation of a nomogram for preoperative prediction of lymph node positivity in pancreatic cancer(NeoPangram)

Background: Neoadjuvant therapy is associated with nodal downstaging and improved oncological outcomes in patients with lymph node(LN)-positive pancreatic cancer. This study aimed to develop and validate a nomogram to preoperatively predict LN-positive disease. Methods: A total of 558 patients with resected pancreatic cancer were randomly and equally divided into development and internal validation cohorts. Multivariate logistic regression analysis was used to construct the nomogram. Model performance was evaluated by discrimination, calibration, and clinical usefulness. An independent multicenter cohort consisting of 250 patients was used for external validation. Results: A four-marker signature was built consisting of carbohydrate antigen 19–9(CA19–9), CA125, CA50, and CA242. A nomogram was constructed to predict LN metastasis using three predictors identified by multivariate analysis: risk score of the four-marker signature, computed tomography-reported LN status, and clinical tumor stage. The prediction model exhibited good discrimination ability, with C-indexes of 0.806, 0.742 and 0.763 for the development, internal validation, and external validation cohorts, respectively. The model also showed good calibration and clinical usefulness. A cut-off value(0.72) for the probability of LN metastasis was determined to separate low-risk and high-risk patients. Kaplan-Meier survival analysis revealed a good agreement of the survival curves between the nomogram-predicted status and the true LN status. Conclusions: This nomogram enables the identification of pancreatic cancer patients at high risk for LN positivity who may have more advanced disease and thus could potentially benefit from neoadjuvant therapy.     

Nomogram predicting cancer-specific survival in elderly patients with stages I–III colon cancer

Abstract

Aim: This study aims to establish and validate an effective nomogram to predict cancer-specific survival (CSS) in elderly patients with stages I–III colon cancer.

Methods: The data of elderly colon cancer patients with stages I–III were enrolled from the Surveillance, Epidemiology, and End Results database (SEER) between 2010 and 2015. The eligible patients were randomly divided into a training cohort and a validation cohort (ratio 1:1). All predictors of cancer-specific survival were determined by Cox regression. The concordance index (C-index) and calibration curves were used for validation of nomograms. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the nomogram.

Results: Cox hazard analysis in the training cohort indicated that grade, tumor stage, node stage, colectomy, and CEA were independent predictors of CSS. Nomogram was constructed based on these predictors. The C-index of nomograms for CSS was 0.728 (95%CI: 0.7133–0.7427), and were superior to that of AJCC TNM Stage (C-index: 0.625, 95%CI: 0.6093–0.6406). The calibration curves showed satisfactory consistency between actual observation and nomogram-predicted CSS probabilities. The validation cohort demonstrated similar results. The DCA showed high net benefit of nomogram in a clinical context. The population was divided into three groups based on the scores of the nomogram, and the survival analysis showed that this prognostic stratification was statistically significant (p?<?0.01).

Conclusion: The nomograms showed significant accuracy in predicting 1-, 3-, and 5-year CSS in elderly patients with stages I–III colon cancer and may be helpful inpatient counseling clinical decision guidance.     

Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram

BackgroundEarly recurrence results in poor prognosis of patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). This study aimed to explore the value of computed tomography (CT)-based radiomics nomogram in predicting early recurrence of patients with HCC after LT.MethodsA cohort of 151 patients with HCC who underwent LT between December 2013 and July 2019 were retrospectively enrolled. A total of 1218 features were extracted from enhanced CT images. The least absolute shrinkage and selection operator algorithm (LASSO) logistic regression was used for dimension reduction and radiomics signature building. The clinical model was constructed after the analysis of clinical factors, and the nomogram was constructed by introducing the radiomics signature into the clinical model. The predictive performance and clinical usefulness of the three models were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively. Calibration curves were plotted to assess the calibration of the nomogram.ResultsThere were significant differences in radiomics signature among early recurrence patients and non-early recurrence patients in the training cohort (P < 0.001) and validation cohort (P < 0.001). The nomogram showed the best predictive performance, with the largest area under the ROC curve in the training (0.882) and validation (0.917) cohorts. Hosmer-Lemeshow testing confirmed that the nomogram showed good calibration in the training (P = 0.138) and validation (P = 0.396) cohorts. DCA showed if the threshold probability is within 0.06-1, the nomogram had better clinical usefulness than the clinical model.ConclusionsOur CT-based radiomics nomogram can preoperatively predict the risk of early recurrence in patients with HCC after LT.     

Defining and predicting early recurrence after liver resection of hepatocellular carcinoma: a multi-institutional study

BackgroundA clear definition of “early recurrence” after hepatocellular carcinoma (HCC) resection is still lacking. This study aimed to determine the optimal cutoff between early and late HCC recurrence, and develop nomograms for pre- and postoperative prediction of early recurrence.MethodsPatients undergoing HCC resection were identified from a multi-institutional Chinese database. Minimum P-value approach was adopted to calculate optimal cut-off to define early recurrence. Pre- and postoperative risk factors for early recurrence were identified and further used for nomogram construction. The results were externally validated by a Western cohort.ResultsAmong 1501 patients identified, 539 (35.9%) were recurrence-free. The optimal length to distinguish between early (n = 340, 35.3%) and late recurrence (n = 622, 64.7%) was 8 months. Multivariable logistic regression analyses identified 5 preoperative and 8 postoperative factors for early recurrence, which were further incorporated into preoperative and postoperative nomograms (C-index: 0.785 and 0.834). The calibration plots for the probability of early recurrence fitted well. The nomogram performance was maintained using the validation dataset (C-index: 0.777 for preoperative prediction and 0.842 for postoperative prediction).ConclusionsAn interval of 8 months was the optimal threshold for defining early HCC recurrence. The two web-based nomograms have been published to allow accurate pre- and postoperative prediction of early recurrence. These may offer useful guidance for individual treatment or follow up for patients with resectable HCC.     

External validation of the Besançon nomogram in Asian patients with advanced pancreatic cancer receiving second-line chemotherapy: A multi-institute experience in Taiwan

BackgroundDetermining survival outcome in advanced pancreatic ductal adenocarcinoma (aPDAC) patients receiving second-line (L2) chemotherapy is important for clinical decision-making. The Besançon group from France recently proposed a prognostic nomogram to predict overall survival (OS) for aPDAC patients receiving L2 chemotherapy. The present study aimed to externally validate the performance of the Besançon nomogram in predicting OS in an Asian cohort.MethodsWe retrospectively enrolled 349 patients who received L2 chemotherapy for aPDAC between 2010 and 2016 at four institutes in Taiwan. The performance of the Besançon model in this cohort was evaluated with C-index and calibration plots.ResultsThe median OS time in our patient cohort was 4.5 months (95% confidence interval [CI], 3.0–5.0). Using the Besançon nomogram-predicted risk groups, the median OS times in the low, intermediate, and high-risk groups were 6.7 (95% CI, 5.3–8.2), 3.2 (95% CI, 2.4–3.9), and 1.7 months (95% CI, 0.6–2.7), respectively. The C-index of the predicted six- and 12-month survival probabilities for the Besançon nomogram were 0.766 (95% CI, 0.715–0.816) and 0.698 (95% CI, 0.641–0.754), respectively. The calibration plot showed that the observed six-month survival probability was close to the diagonal line, while that for 12-month survival deviated below the diagonal line compared to the survival probability predicted by the Besançon nomogram.ConclusionsAlthough the Besançon nomogram tended to over-estimate the 12-month survival probability, our study demonstrated that the nomogram is a reliable and readily applicable model to estimate survival outcomes of aPDAC patients receiving L2 chemotherapy.     

Development and validation of a novel nomogram and risk score for biliary atresia in patients with cholestasis

BackgroundTimely discriminating biliary atresia (BA) from other causes of cholestasis is important but challenging.AimsTo develop a useful diagnostic nomogram and a simplified scoring system to diagnosing BA.Study designAll medical records of the patients who were consecutively admitted to our institution with cholestasis from March 2016 to December 2020 were retrospectively searched. The patients were allocated to the derivation cohort (n = 343) and the validation cohort (n = 246). Multivariable logistic regression models were used to construct the nomogram. The nomogram was validated in both cohorts. The simplified risk score was derived from the nomogram.ResultsThe nomogram was constructed based on presence of clay stool, gallbladder length, gallbladder emptying index, shear wave elastography value, and gamma-glutamyl transferase level. This model showed good calibration and discrimination ability, with the C-index of 0.968 (95% CI: 0.951–0.984). The discriminating ability is most prominent in the 61–90 days group, with AUC of 0.982 (95% CI: 0.955–1.000). The simplified risk score identified most patients with very high or low risk of BA, and was capable of exempting 64.3% non-BA patients from intraoperative cholangiogram procedure.ConclusionsThis novel diagnostic nomogram had good discrimination and calibration abilities. The simplified scoring system showed significant clinical utility.     

A potential biomarker based on clinical-radiomics nomogram for predicting survival and adjuvant chemotherapy benefit in resected node-negative,early-stage lung adenocarcinoma

BackgroundWe aimed to construct a clinical-radiomics nomogram to predict disease-free survival (DFS) and the added survival benefit of adjuvant chemotherapy (ACT) for node-negative, early-stage (I–II) lung adenocarcinoma (ADC).MethodsIn this retrospective study including 310 patients from two independent cohorts, the CT-derived radiomics features were selected by least absolute shrinkage and selection operator Cox regression to generate a radiomics signature associated with DFS. The radiomics signature was incorporated to construct a clinical-radiomics nomogram along with the independent clinical risk predictors. The model performance was evaluated with reference to discrimination quantified by Harrell concordance index (C-index), integrated discrimination improvement (IDI) and net reclassification index (NRI), calibration and clinical utility. The risk score (RS) for clinical-radiomics nomogram was calculated. The association between ACT and survival benefit was assessed in high and low RS subgroup.ResultsThe clinical-radiomics nomogram achieved the highest C-index of 0.822 [95% confidence interval (CI): 0.769, 0.876] in training cohort and 0.802 (95% CI: 0.716, 0.888) in validation cohort. The incorporation of radiomics signature into clinical-radiomics nomogram showed an incremental benefit over clinical nomogram according to the improved NRI and IDI. The calibration curves and decision curve analysis further verified the clinical utility of clinical-radiomics nomogram. Further, patients with high RS based on clinical-radiomics nomogram were more prone to benefit from ACT.ConclusionsThe clinical-radiomics nomogram approach can feasibly conduct risk prediction and have potential to identify the beneficiaries of ACT among patients with node-negative, early-stage ADC, which might serve as a helpful tool in informing therapeutic decision-making.     

Nomogram for predicting the overall survival of the patients with oesophageal signet ring cell carcinoma

BackgroundThe purpose of this study was to explore the prognostic factors of oesophageal signet ring cell (SRC) carcinoma and to construct a nomogram for predicting the outcome of SRC carcinoma of oesophagus.MethodsA total of 968 cases of oesophageal SRC carcinoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. Cases were divided into training cohort and validation cohort. Univariate and multivariable Cox analyses was performed to select the predictors of overall survival (OS for the nomogram. The performance of nomogram was validated with Harrell’s concordance index (C-index), calibration curves and decision curve analysis (DCA).ResultsThe 1- and 5-year OS in the training cohort were 0.446 and 0.146, respectively, and the 1- and 5-year OS in the validation cohort were 0.459 and 0.138. The independent prognostic factors for establishing the nomogram were marital status, invasion of the surrounding tissue, lymph node metastasis, distant metastasis, surgery and chemotherapy. The Harrell’s c-index value of the training cohort and validation cohort were 0.723 and 0.708. In the calibration curves, the predicted survival probability and the actual survival probability have a considerable consistency. DCA indicated the favourable potential clinical utility of the nomogram.ConclusionsA nomogram to predict the OS of patients with oesophageal SRC carcinoma was established. The validation of the nomogram fully demonstrates its great performance.     

Development of preoperative prognostic models including radiological features for survival of singular nodular HCC patients

BackgroundEarly singular nodular hepatocellular carcinoma (HCC) is an ideal surgical indication in clinical practice. However, almost half of the patients have tumor recurrence, and there is no reliable prognostic prediction tool. Besides, it is unclear whether preoperative neoadjuvant therapy is necessary for patients with early singular nodular HCC and which patient needs it. It is critical to identify the patients with high risk of recurrence and to treat these patients preoperatively with neoadjuvant therapy and thus, to improve the outcomes of these patients. The present study aimed to develop two prognostic models to preoperatively predict the recurrence-free survival (RFS) and overall survival (OS) in patients with singular nodular HCC by integrating the clinical data and radiological features.MethodsWe retrospective recruited 211 patients with singular nodular HCC from December 2009 to January 2019 at Eastern Hepatobiliary Surgery Hospital (EHBH). They all met the surgical indications and underwent radical resection. We randomly divided the patients into the training cohort (n =132) and the validation cohort (n = 79). We established and validated multivariate Cox proportional hazard models by the preoperative clinicopathologic factors and radiological features for association with RFS and OS. By analyzing the receiver operating characteristic (ROC) curve, the discrimination accuracy of the models was compared with that of the traditional predictive models.ResultsOur RFS model was based on HBV-DNA score, cirrhosis, tumor diameter and tumor capsule in imaging. RFS nomogram had fine calibration and discrimination capabilities, with a C-index of 0.74 (95% CI: 0.68-0.80). The OS nomogram, based on cirrhosis, tumor diameter and tumor capsule in imaging, had fine calibration and discrimination capabilities, with a C-index of 0.81 (95% CI: 0.74-0.87). The area under the receiver operating characteristic curve (AUC) of our model was larger than that of traditional liver cancer staging system, Korea model and Nomograms in Hepatectomy Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma, indicating better discrimination capability. According to the models, we fitted the linear prediction equations. These results were validated in the validation cohort.ConclusionsCompared with previous radiography model, the new-developed predictive model was concise and applicable to predict the postoperative survival of patients with singular nodular HCC. Our models may preoperatively identify patients with high risk of recurrence. These patients may benefit from neoadjuvant therapy which may improve the patients’ outcomes.     

Development and validation of a nomogram for the prediction of brain metastases in small cell lung cancer

Introduction

The aim was to develop and validate a nomogram for the prediction of brain metastases (BM) in small cell lung cancer (SCLC), to explore the risk factors and assist clinical decision-making.

Methods

We reviewed the clinical data of SCLC patients between 2015 and 2021. Patients between 2015 and 2019 were included to develop, whereas patients between 2020 and 2021 were used for external validation. Clinical indices were analysed by using the least absolute shrinkage and selection operator (LASSO) logistic regression analyses. The final nomogram was constructed and validated by bootstrap resampling.

Results

A total of 631 SCLC patients between 2015 and 2019 were included to construct model. Gender, T stage, N stage, Eastern Cooperative Oncology Group (ECOG), haemoglobin (HGB), the absolute value of lymphocyte (LYMPH #), platelet (PLT), retinol-binding protein (RBP), carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) were identified as risk factors and included into the model. The C-indices were 0.830 and 0.788 in the internal validation by 1000 bootstrap resamples. The calibration plot revealed excellent agreement between the predicted and the actual probability. Decision curve analysis (DCA) showed better net benefits with a wider range of threshold probability (net clinical benefit was 1%–58%). The model was further externally validated in patients between 2020 and 2021 with a C-index of 0.818.

Conclusions

We developed and validated a nomogram to predict the risk of BM in SCLC patients, which could help clinicians to rationally schedule follow-ups and promptly implement interventions.     

A novel seven-gene risk profile in BALF to identify high-risk patients with idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression.MethodsWe obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the {"type":"entrez-geo","attrs":{"text":"GPL14550","term_id":"14550"}}GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the {"type":"entrez-geo","attrs":{"text":"GPL17077","term_id":"17077"}}GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated.ResultsA nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression.ConclusionsThe novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.     

Dynamic nomogram for persistent organ failure in acute biliary pancreatitis: Development and validation in a retrospective study

BackgroundPersistent organ failure (POF) increases the risk of death in patients with acute biliary pancreatitis (ABP). Currently, there is no early risk assessment tool for POF in patients with ABP.AimsTo establish and validate a dynamic nomogram for predicting the risk of POF in ABP.MethodsThis was a retrospective study of 792 patients with ABP, with 595 cases in the development group and 197 cases in the validation group. Least absolute shrinkage and selection operator regression screened the predictors of POF, and logistic regression established the model (P < 0.05). A dynamic nomogram showed the model. We evaluated the model's discrimination, calibration, and clinical effectiveness; used the bootstrap method for internal validation; and conducted external validation in the validation group.ResultsNeutrophils, haematocrit, serum calcium, and blood urea nitrogen were predictors of POF in ABP. In the development group and validation group, the areas under the receiver operating characteristic curves (AUROCs) were 0.875 and 0.854, respectively, and the Hosmer-Lemeshow test (P > 0.05) and calibration curve showed good consistency between the actual and prediction probability. Decision curve analysis showed that the dynamic nomogram has excellent clinical value.ConclusionThis dynamic nomogram helps with the early identification and screening of high-risk patients with POF in ABP.     

Preoperative prediction model of microvascular invasion in patients with hepatocellular carcinoma

BackgroundMicrovascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC.MethodsA total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsLogistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility.ConclusionThe nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions.     

Development and validation of a nomogram for predicting the overall survival of patients with gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are increasing in incidence. Clinicians urgently need a method that can effectively predict the prognosis of GEP-NENs.A total of 14770 GEP-NENs patients with pathologically confirmed between 1975 and 2016 were obtained from the surveillance, epidemiology, and end results database. All the patients were divided into primary (n = 10377) and validation (n = 4393) cohorts based on the principle of random grouping. Multivariate Cox proportional hazards proportional hazards regression analysis was performed to evaluate predictors associated with overall survival, and a nomogram was constructed based on the primary cohort. An independent external validation cohort and comparison with the eighth edition American Joint Committee on Cancer TNM staging system were subsequently used to assess the predictive performance of the nomogram.The multivariate Cox model indicated that age, tumour differentiation, and distant metastases were independent predictors associated with overall survival. With respect to the primary cohort, the nomogram exhibited better discriminatory power than the TNM classification (C-index: 0.821 vs 0.738). Discrimination was also superior to that of TNM classification for the validation cohort (C-index: 0.823 vs 0.738). The calibrated nomogram predicted 3- and 5-years survival rate that closely corresponded to the actual survival rate.This study developed and validated a prognostic nomogram applied to patients with GEP-NENs, which may help clinicians make reasonable prognostic judgments and treatment plans to a certain extent.     

Dynamic nomogram for long-term survival in patients with non-small cell lung cancer after pneumonectomy

BackgroundThe study aims to identify prognostic factors of overall survival (OS) in patients who had pneumonectomy, in order to develop a practical dynamic nomogram model.MethodsA total of 2,255 patients with non-small cell lung cancer (NSCLC) who underwent pneumonectomy were identified from 2010–2015 in the Surveillance, Epidemiology, and End Results (SEER) database. The cohort was divided into a training (2011–2015) and a validation [2010] cohort. A nomogram and a risk classification system were constructed from the independent survival factors in multivariable analysis. The predictive accuracy of the nomogram was measured through internal and external validation.ResultsIndependent prognostic factors associated with OS were gender, age, pathology, tumor size, N stage, chemotherapy, and radiotherapy. The C-index of the nomogram for OS was 0.675 (95% CI: 0.655–0.694). Similarly, the AUC of the model was 0.733, 0.709, and 0.701 for the 1-, 3-, and 5-year OS, respectively. The calibration curves for survival demonstrated good agreement. Significant statistical differences were found in the OS of patients within different risk groups. An online calculation tool was established for clinical use.ConclusionsThis novel nomogram was able to provide a reliable prognosis for survival in patients with NSCLC undergoing pneumonectomy.     

Nomogram for prediction of long-term survival with hepatocellular carcinoma based on NK cell counts

IntroductionAmong all immune cells, natural killer (NK) cells play an important role as the first line of defense against tumor. The purpose of our study is to observe whether the NK cell counts can predict the overall survival of patients with hepatocellular carcinoma (HCC).MethodsTo develop a novel model, from January 2010 to June 2015, HCC patients enrolled in Beijing Ditan hospital were divided into training and validation cohort. Cox multiple regression analysis was used to analyze the independent risk factors for 1-year, 3-year and 5-year overall survival (OS) of patients with HCC, and the nomogram was used to establish the prediction model. In addition, the decision tree was established to verify the contribution of NK cell counts to the survival of patients with HCC.ResultsThe model used in predicting overall survival of HCC included six variables (namely, NK cell counts, albumin (ALB) level, alpha-fetoprotein (AFP) level, portal vein tumor thrombus (PVTT), tumor number and treatment). The C-index of nomogram model in HCC patients predicting 1-year, 3-year and 5-year overall survival was 0.858, 0.788 and 0.782 respectively, which was higher than tumor–lymph node–metastasis (TNM) staging system, Okuda, model for end-stage liver disease (MELD), MELD-Na, the Chinese University Prognostic Index (CUPI) and Japan Integrated Staging (JIS) scores (p < 0.001). The decision tree showed the specific 5-year OS probability of HCC patients under different risk factors, and found that NK cell counts were the third in the column contribution.ConclusionsOur study emphasizes the utility of NK cell counts for exploring interactions between long-term survival of HCC patients and predictor variables.     

Development of a computed tomography-based radiomics nomogram for prediction of transarterial chemoembolization refractoriness in hepatocellular carcinoma

BACKGROUND Some patients with hepatocellular carcinoma(HCC)are more likely to experience disease progression despite continuous transarterial chemoembolization(TACE),which is called TACE refractoriness.At present,it is still difficult to predict TACE refractoriness,although some models/scoring systems have been developed.At present,radiological-based radiomics models have been successfully applied to predict cancer patient prognosis.AIM To develop and validate a computed tomography(CT)-based radiomics nomogram for the pre-treatment prediction of TACE refractoriness.METHODS This retrospective study consisted of a training dataset(n=137)and an external validation dataset(n=81)of patients with clinically/pathologically confirmed HCC who underwent repeated TACE from March 2009 to March 2016.Radiomics features were retrospectively extracted from preoperative CT images of the arterial phase.The pre-treatment radiomics signature was generated using least absolute shrinkage and selection operator Cox regression analysis.A CT-based radiomics nomogram incorporating clinical risk factors and the radiomics signature was built and verified by calibration curve and decision curve analyses.The usefulness of the CT-based radiomics nomogram was assessed by Kaplan-Meier curve analysis.We used the concordance index to conduct head-to-head comparisons of the radiomics nomogram with the other four models(Assessment for Retreatment with Transarterial Chemoembolization score;α-fetoprotein,Barcelona Clinic Liver Cancer,Child-Pugh,and Response score;CT-based radiomics signature;and clinical model).All analyses were conducted according to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement.RESULTS The median duration of follow-up was 61.3 mo(interquartile range,25.5-69.3 mo)for the training cohort and 67.1 mo(interquartile range,32.4-71.3 mo)for the validation cohort.The median number of TACE sessions was 4(range,3-7)in both cohorts.Eight radiomics features were chosen from 869 candidate features to build a radiomics signature.The CT-based radiomics nomogram included the radiomics score(hazard ratio=3.9,95%confidence interval:3.1-8.8,P<0.001)and four clinical factors and classified patients into high-risk(score>3.5)and low-risk(score≤3.5)groups with markedly different prognoses(overall survival:12.3 mo vs 23.6 mo,P<0.001).The accuracy of the nomogram was considerably higher than that of the other four models.The calibration curve and decision curve analyses verified the usefulness of the CT-based radiomics nomogram for clinical practice.CONCLUSION The newly constructed CT-based radiomics nomogram can be used for the pretreatment prediction of TACE refractoriness,which may provide better guidance for decision making regarding further TACE treatment.     

儿童法洛四联症根治术后机械通气时间延长风险的预测模型建立与内部验证

目的 建立并内部验证预测法洛四联症(tetralogy of Fallot,TOF)根治术后机械通气时间延长(prolonged mechanical ventilation, PMV)风险的列线图模型。 方法 连续入选2019年6月至12月在我院行TOF根治术的6月龄到6岁患儿，并回顾性分析其临床数据。PMV定义为术后机械通气持续时间超过48h。基于入选的患儿做为训练集开发预测PMV风险的列线图模型。采用最小绝对收缩与选择算子(The least absolute shrinkage and selection operator, LASSO)回归模型用于列线图模型的变量选择；应用多因素logistic回归分析来建立预测模型，该模型纳入由LASSO回归模型所选择的所有变量。采用C指数，校准图和决策曲线分析(Decision curve analysis, DCA)评估预测模型的准确性，一致性和临床实用性。采用Bootstrap重复抽样的方法对模型进行内部验证。 结果 入选的109名患儿，分为机械通气延长组（PMV组）（n=32，占29.4%）与非机械通气延长组（非PMV组）（n=77，占70.6%）。PMV组患儿术后机械通气时间显著长于非PMV组（P<0.01）。多因素logistic回归分析显示术前McGoon比<1.5（OR=3.564，95%CI:1.078-11.782，P<0.05），术中较长的体外循环时间（OR=1.020，95%CI:1.007-1.032，P<0.01）和术后较低的左室射血分数（OR=0.885，95%CI:0.792-0.988，P<0.05）为术后PMV的独立预测因素。并且，该模型具有良好的一致性和区分能力，C指数为0.774。模型经过内部验证后，校正曲线表现良好，C指数较高，等于0.756。DCA表明，当阈概率在大于2%且小于76%的范围内，ICU医师做出改变通气策略的干预决定，列线图模型具有很好的临床效果。 结论 我们开发并内部验证一种高精度的列线图模型，以协助ICU医生进行与术后PMV相关的临床决策。然而，在推荐用于临床实践之前，该模型需要进行外部验证。