scholarly journals Development and Validation of a Nomogram to Predict Survival in Pancreatic Head Ductal Adenocarcinoma After Pancreaticoduodenectomy

2021 ◽  
Vol 11 ◽  
Author(s):  
Feng Peng ◽  
Tingting Qin ◽  
Min Wang ◽  
Hebin Wang ◽  
Chao Dang ◽  
...  
Keyword(s):  
Clinical Utility ◽  
Cox Regression ◽  
External Validation ◽  
Pancreatic Head ◽  
Tnm Stage ◽  
University Hospital ◽  
Ductal Adenocarcinoma ◽  
Kaplan Meier ◽  
Staging Systems ◽  
Differentiation Degree

BackgroundPancreatic head ductal adenocarcinoma (PHDAC) patients with the same tumor-node-metastasis (TNM) stage may share different outcomes after pancreaticoduodenectomy (PD). Therefore, a novel method to identify patients with poor prognosis after PD is urgently needed. We aimed to develop a nomogram to estimate survival in PHDAC after PD.MethodsTo estimate survival after PD, a nomogram was developed using the Tongji Pancreatic cancer cohort comprising 355 PHDAC patients who underwent PD. The nomogram was validated under the same conditions in another cohort (N = 161) from the National Taiwan University Hospital. Prognostic factors were assessed using LASSO and multivariate Cox regression models. The nomogram was internally validated using bootstrap resampling and then externally validated. Performance was assessed using concordance index (c-index) and calibration curve. Clinical utility was evaluated using decision curve analysis (DCA), X-tile program, and Kaplan–Meier curve in both training and validation cohorts.ResultsOverall, the median follow-up duration was 32.17 months, with 199 deaths (64.82%) in the training cohort. Variables included in the nomogram were age, preoperative CA 19-9 levels, adjuvant chemotherapy, Tongji classification, T stage, N stage, and differentiation degree. Harrell’s c-indices in the internal and external validation cohorts were 0.79 (95% confidence interval [CI], 0.76–0.82) and 0.83 (95% CI, 0.78–0.87), respectively, which were higher than those in other staging systems. DCA showed better clinical utility.ConclusionThe nomogram was better than TNM stage and Tongji classification in predicting PHDAC patients’ prognosis and may improve prognosis-based selection of patients who would benefit from PD.

scholarly journals A Novel Nomogram to Predict Prolonged Survival After Hepatectomy in Repeat Recurrent Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiongxuan Fang ◽  
Ruifeng Yang ◽  
Dongbo Chen ◽  
Ran Fei ◽  
Pu Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Repeat Hepatectomy ◽  
Discriminative Ability ◽  
Training Cohort ◽  
Recurrent Hepatocellular Carcinoma ◽  
Kaplan Meier ◽  
Staging Systems ◽  
Better Than

Background: Repeat hepatectomy is an important treatment for patients with repeat recurrent hepatocellular carcinoma (HCC).Methods: This study was a multicenter retrospective analysis of 1,135 patients who underwent primary curative liver resection for HCC. One hundred recurrent patients with second hepatectomy were included to develop a nomogram to predict the risk of post-recurrence survival (PRS). Thirty-eight patients in another institution were used to externally validate the nomogram. Univariate and multivariate Cox regression analyses were used to identify independent risk factors of PRS. Discrimination, calibration, and the Kaplan–Meier curves were used to evaluate the model performance.Results: The nomogram was based on variables associated with PRS after HCC recurrence, including the tumor, node, and metastasis (TNM) stage; albumin and aspartate aminotransferase levels at recurrence; tumor size, site, differentiation of recurrences; and time to recurrence (TTR). The discriminative ability of the nomogram, as indicated by the C statistics (0.758 and 0.811 for training cohort and external validation cohorts, respectively), was shown, which was better than that of the TNM staging system (0.609 and 0.609, respectively). The calibration curves showed ideal agreement between the prediction and the real observations. The area under the curves (AUCs) of the training cohort and external validation cohorts were 0.843 and 0.890, respectively. The Kaplan–Meier curve of the established nomogram also performed better than those of both the TNM and the BCLC staging systems.Conclusions: We constructed a nomogram to predict PRS in patients with repeat hepatectomy (RH) after repeat recurrence of HCC.

scholarly journals Comprehensive analysis of genomic mutation signature and tumor mutation burden for prognosis of intrahepatic cholangiocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Zhang ◽  
Qi Li ◽  
Jialu Fu ◽  
Zhechuan Jin ◽  
Jingbo Su ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Cox Regression ◽  
Tnm Stage ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Tumor Mutation Burden ◽  
Kaplan Meier ◽  
Mutation Burden ◽  
Significant Difference ◽  
Genomic Mutation

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is a highly lethal malignancy of the biliary tract. Analysis of somatic mutational profiling can reveal new prognostic markers and actionable treatment targets. In this study, we explored the utility of genomic mutation signature and tumor mutation burden (TMB) in predicting prognosis in iCCA patients. Methods Whole-exome sequencing and corresponding clinical data were collected from the ICGC portal and cBioPortal database to detect the prognostic mutated genes and determine TMB values. To identify the hub prognostic mutant signature, we used Cox regression and Lasso feature selection. Mutation-related signature (MRS) was constructed using multivariate Cox regression. The predictive performances of MRS and TMB were assessed using Kaplan–Meier (KM) analysis and receiver operating characteristic (ROC). We performed a functional enrichment pathway analysis using gene set enrichment analysis (GSEA) for mutated genes. Based on the MRS, TMB, and the TNM stage, a nomogram was constructed to visualize prognosis in iCCA patients. Results The mutation landscape illustrated distributions of mutation frequencies and types in iCCA, and generated a list of most frequently mutated genes (such as Tp53, KRAS, ARID1A, and IDH1). Thirty-two mutated genes associated with overall survival (OS) were identified in iCCA patients. We obtained a six-gene signature using the Lasso and Cox method. AUCs for the MRS in the prediction of 1-, 3-, and 5-year OS were 0.759, 0.732, and 0.728, respectively. Kaplan–Meier analysis showed a significant difference in prognosis for patients with iCCA having a high and low MRS score (P < 0.001). GSEA was used to show that several signaling pathways, including MAPK, PI3K-AKT, and proteoglycan, were involved in cancer. Conversely, survival analysis indicated that TMB was significantly associated with prognosis. GSEA indicated that samples with high MRS or TMB also showed an upregulated expression of pathways involved in tumor signaling and the immune response. Finally, the predictive nomogram (that included MRS, TMB, and the TNM stage) demonstrated satisfactory performance in predicting survival in patients with iCCA. Conclusions Mutation-related signature and TMB were associated with prognosis in patients with iCCA. Our study provides a valuable prognostic predictor for determining outcomes in patients with iCCA.

scholarly journals Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

2021 ◽  
Vol 15 ◽  
pp. 117955492110241
Author(s):  
Hongkai Zhuang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Shanzhou Huang ◽  
Yuanfeng Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Pancreatic Head ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy. Methods: We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018. Results: Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model. Conclusions: A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

Abstract TP209: Effect of Chemotherapy on Stroke Risk in Cancer Patients

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Takaya Kitano ◽  
Tsutomu Sasaki ◽  
Yasufumi Gon ◽  
Kenichi Todo ◽  
Shuhei Okazaki ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Propensity Scores ◽  
Stroke Risk ◽  
Cox Regression ◽  
Treatment Plan ◽  
University Hospital ◽  
Chemotherapy Group ◽  
Kaplan Meier ◽  
Increased Risk ◽  
The Impact

Introduction: Chemotherapy may be a cause of cancer-associated stroke, but whether it increases stroke risk remains uncertain. We aimed to clarify the impact of chemotherapy on stroke risk in cancer patients. Methods: We investigated 27,932 patients enrolled in a hospital-based cancer registry at Osaka University Hospital between 2007 and 2015. The registry collects clinical data, including cancer status (site and stage), on all patients treated for cancer. Of them, 19,006 patients with complete data were included. A validated algorithm was used to identify stroke events within 2 years of cancer diagnosis. Patients were divided based on whether their initial treatment plan included chemotherapy. The association between chemotherapy and stroke was analyzed using the Kaplan-Meier method and stratified Cox regression. Results: Of the 19,006 patients, 5,887 (31%) patients were in the chemotherapy group. Non-targeted chemotherapy was used in 5,371 patients. Stroke occurred in 44 patients (0.75%) in the chemotherapy group and 51 patients (0.39%) in the no-chemotherapy group. Kaplan-Meier curve analysis showed that patients in the chemotherapy group had a higher stroke risk than patients in the no-chemotherapy group (HR 1.84; 95% CI 1.23-2.75; Figure [A]). However, this difference was insignificant after adjustment for cancer status using inverse probability of treatment weighting with propensity scores (HR 1.20; 95% CI 0.76-1.91; Figure [B]). Similarly, in the stratified Cox regression model, chemotherapy was not associated with stroke after adjustment for cancer status (HR 1.26; 95% CI 0.78-2.03). These findings were consistent with analysis wherein the effect of chemotherapy was treated as a time-dependent covariate (HR 1.02; 95% CI 0.55-1.88). Conclusions: In this population, the elevated stroke risk in cancer patients who received chemotherapy was presumably due to advanced cancer stage; chemotherapy was not associated with the increased risk of stroke.

PPM1D is Associated With Prognosis of Bladder Cancer in the Chinese Population

2020 ◽  
Author(s):  
Shuangqing Cao ◽  
Lei Zheng
Keyword(s):  
Bladder Cancer ◽  
Mrna Expression ◽  
Cox Regression ◽  
Histological Grade ◽  
Tnm Stage ◽  
Cox Regression Analysis ◽  
Relative Expression ◽  
Kaplan Meier ◽  
Normal Bladder ◽  
Cancer Tissues

Abstract Background: Present study was to investigate the relative expression and prognostic performance of protein phosphatase magnesium/manganese-dependent 1D (PPM1D) in bladder cancer.Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) assay was performed to examine the relative expression of PPM1D mRNA in bladder cancer tissues and adjacent normal bladder tissues. The associations of PPM1D mRNA expression with clinicopathological features and the prognostic value were statistically analyzed via Chi-square test, Kaplan-Meier method and Cox regression analysis.Results: In comparison to adjacent normal tissues, PPM1D mRNA expression was obviously increased in bladder cancer tissues (P<0.001). Abnormal PPM1D expression was remarkably related to histological grade (P=0.017), TNM stage (P=0.032) and lymph nodes metastasis (P=0.035). Kaplan-Meier method showed that a close relationship was found between PPM1D expression and overall survival time (P=0.000). Multivariate analysis indicated that PPM1D expression (P=0.000, HR=3.530, 95%CI: 2.001-6.228) was a promising independent predictor for the prognosis of bladder cancer patients, as well as TNM stage (P=0.042, HR=1.768, 95%CI: 1.021-3.062).Conclusion: Taken together, our data showed that the potential performance of PPM1D as a prognostic biomarker and therapeutic target of bladder cancer.

scholarly journals The Clinical and Pathological Significance of Nectin-2 and DDX3 Expression in Pancreatic Ductal Adenocarcinomas

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shan Liang ◽  
Zhulin Yang ◽  
Daiqiang Li ◽  
Xiongying Miao ◽  
Leping Yang ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Poor Prognosis ◽  
Genetic Basis ◽  
Malignant Disease ◽  
Tnm Stage ◽  
Ductal Adenocarcinoma ◽  
Node Metastasis ◽  
Kaplan Meier ◽  
Poor Differentiation

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant disease, but the genetic basis of PDAC is still unclear. In this study, Nectin-2 and DDX3 expression in 106 PDAC, 35 peritumoral tissues, 55 benign pancreatic lesions, and 13 normal pancreatic tissues were measured by immunohistochemical methods. Results showed that the percentage of positive Nectin-2 and DDX3 expression was significantly higher in PDAC tumors than in peritumoral tissues, benign pancreatic tissues, and normal pancreatic tissues (P<0.01). The percentage of cases with positive Nectin-2 and DDX3 expression was significantly lower in PDAC patients without lymph node metastasis and invasion and having TNM stage I/II disease than in patients with lymph node metastasis, invasion, and TNM stage III/IV disease (P<0.05orP<0.01). Positive DDX3 expression is associated with poor differentiation of PDAC. Kaplan-Meier survival analysis showed that positive Nectin-2 and DDX3 expression were significantly associated with survival in PDAC patients (P<0.001). Cox multivariate analysis revealed that positive Nectin-2 and DDX3 expression were independent poor prognosis factors in PDAC patients. In conclusion, positive Nectin-2 and DDX3 expression are associated with the progression and poor prognosis in PDAC patients.

scholarly journals A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

2021 ◽  
Vol 9 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Mindi Ma ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Dna Replication ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

scholarly journals External validation of three nomograms predicting survival using an international cohort of patients with resected pancreatic head ductal adenocarcinoma

2021 ◽  
Vol 108 (Supplement_4) ◽  
Author(s):  
M Schneider ◽  
I Labgaa ◽  
D Vrochides ◽  
A Zerbi ◽  
G Nappo ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Sensitivity And Specificity ◽  
Goodness Of Fit ◽  
Neoadjuvant Treatment ◽  
Pancreatic Head ◽  
Tumor Grade ◽  
Ductal Adenocarcinoma ◽  
Youden Index ◽  
Predictive Values ◽  
Kaplan Meier

Abstract Objective Lymph node ratio (LNR, positive lymph nodes/collected lymph nodes during surgery) was identified as an important prognostic factor of survival in resected pancreatic cancer. Several nomograms based on LNR were recently proposed to predict survival after pancreatoduodenectomy (PD). The present study aimed to externally validate 3 published nomograms using an international cohort. Methods Consecutive patients with ductal adenocarcinoma of the pancreatic head who underwent PD without neoadjuvant treatment from 6 tertiary centers in Europe and the USA were retrospectively collected from 2000 to 2017. Patients with metastases at diagnosis, R2 resection, missing data regarding LNR, and who died within 90 postoperative days were excluded. The 3 selected nomograms were the updated Amsterdam nomogram (including LNR, adjuvant therapy, margin status, and tumor grade), the nomogram by Pu et al. (including LNR, age, tumor grade, and T stage) and the nomogram by Li et al. (including LNR, age, tumor location, grade, size, and TNM stage). Overall survivals (OS) were calculated using Kaplan-Meier method. For the validation, calibration (Hosmer-Lemeshow test), discrimination capacity (ROC curves for 3-year OS), and clinical utility (sensitivity and specificity at the value of Youden index) were assessed. Results After exclusion of 95 patients with metastases, R2 resection, and who died within 90 postoperative days, 1167 patients were included. Median OS of the entire cohort was 23 months (95% confidence interval: 21-24). For the 3 nomograms, Kaplan-Meier curves showed significant diminution of OS with increasing scores (p &lt; 0.01 for the 3 nomograms). All nomograms showed good calibration (non significant Hosmer-Lemeshow goodness-of-fit tests). For the updated Amsterdam nomogram, the area under the ROC curve (AUROC) for 3-year OS was 0.66. Sensitivity and specificity were 73% and 50%. Regarding the nomogram by Pu et al., the AUROC was 0.67. Sensitivity and specificity were 65% and 60%. For the nomogram by Li et al., the AUROC was 0.67, while sensitivity and specificity were 56% and 71%. Conclusion The 3 selected nomograms were validated using an external international cohort and displayed interesting and comparable predictive values. Those nomograms may be used in clinical practice to estimate survival after PD for ductal adenocarcinoma.

Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 164-164 ◽  
Author(s):  
Woo Jin Hyung ◽  
Taeil Son ◽  
Minseok Park ◽  
Hansang Lee ◽  
Youn Nam Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Survival Probability ◽  
Cox Regression ◽  
External Validation ◽  
Prognosis Prediction ◽  
Average Accuracy ◽  
Staging Systems

164 Background: Staging systems for cancer are critical to predict the prognosis of patients. Current staging systems for gastric cancer have limitations to predict individualized and precise prediction of patient’s survival after treatment. We aimed to develop prediction model based on deep learning by estimating the survival probability of patients who underwent gastrectomy. Methods: To predict the survival probability, we used a deep neural network model which consisted of 5 layers: input layer, 3 fully connected layer, and output layer with 8 characteristics (age, sex, histology, depth of tumor, number of metastatic and examined lymph node, presence of distant metastasis, and resection extent) of patients which was previously published Yonsei prediction model using Cox regression. Each layer functioned as the nonlinear weighted sum of lower layer. Five-year overall survival was predicted using the deep learning method and it was compared to Yonsei prediction model. The average area under the curve (AUC) was compared between the models. For internal validation, 5-fold cross validations were carried out. We also performed external validation with a dataset from another hospital (n = 1549). . Results: Deep learning predicted 5-year overall survival of patients with an average accuracy of 83.5% in the test set. The average AUC of deep learning by integrating 8 characteristics was significantly higher than that of Yonsei prediction model (AUC: 0.844 vs. 0.831, P < 0.001) with the same variables. In the external validation the average accuracy of survival prediction was 84.1%. The AUC was also greater in a dataset from other hospital in Korea (AUC: 0.852 vs. 0.847, P = 0.023) Conclusions: Prognosis prediction with deep learning showed superior survival predictive power compared to prediction model using Cox regression. It can provide individualized and precise stratification based on the risk using characteristics of gastric cancer patients.

The prognostic value of baseline neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for predicting clinical outcome in patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) treated with liposomal irinotecan (nal-IRI; MM-398) + 5-fluorouracil and leucovorin (5-FU/LV) vs 5-FU/LV.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15795-e15795 ◽  
Author(s):  
Andrea Wang-Gillam ◽  
Li-Tzong Chen ◽  
Chung-Pin Li ◽  
Gyorgy Bodoky ◽  
Andrew Dean ◽  
...  
Keyword(s):  
Cox Regression ◽  
Prognostic Significance ◽  
Progression Free Survival ◽  
Exploratory Analysis ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Kaplan Meier ◽  
Hazard Ratios ◽  
Liposomal Irinotecan

e15795 Background: Increased NLR and PLR have been associated with poor survival in several malignancies. Here we report the association of NLR and PLR with overall survival (OS) and progression-free survival (PFS) in the NAPOLI-1 trial (NCT01494506), which evaluated nal-IRI+5-FU/LV for the treatment of mPDAC patients (pts) after disease progression following gemcitabine-based therapy. Methods: Pts missing baseline NLR/PLR data were excluded. Medians reflect Kaplan-Meier estimates; hazard ratios (HRs) reflect Cox regression analysis. P values in this exploratory analysis are descriptive. Results: Of 116 evaluable pts in the nal-IRI+5-FU/LV arm, 82 (71%) had NLR ≤5 and 44 (38%) had PLR ≤150 (data cutoff: Nov 16, 2015). Of 105 evaluable pts in the 5-FU/LV control arm, 73 (70%) had NLR ≤5 and 36 (34%) had PLR ≤150. In pts with baseline NLR ≤5 or PLR ≤150, median OS and PFS were significantly longer in the nal-IRI+5-FU/LV treatment arm vs the 5-FU/LV control arm (Table). In pts with baseline NLR >5 or PLR >150, median OS and PFS were numerically longer in the treatment vs control arm, but differences were less compelling (95% CIs for HRs included 1). Conclusions: Median OS and PFS were improved with nal-IRI+5-FU/LV vs 5-FU/LV in pts with baseline NLR ≤5 or PLR ≤150. This exploratory analysis extends the prognostic significance of NLR and PLR to the post-gemcitabine setting. Clinical trial information: NCT01494506. [Table: see text]

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