Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

Li, Ge; Chen, Jiang-Zhi; Chen, Shi; Lin, Shengzhe; Pan, Wei; Meng, Zihui; Cai, Xinran; Chen, Yanling

doi:10.1002/cam4.2959

Cited by 15 publications

(10 citation statements)

References 34 publications

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“…The 8th AJCC system defines T4 stage as the pancreatic tumor has invaded the celiac axis, common hepatic artery, or superior mesenteric artery, which obviously leads to a poor prognosis. Tumor differentiation and age were also significantly associated with clinical outcomes, which is in agreement with previous studies ( 35 , 36 ). Adjuvant chemotherapy is one element of comprehensive treatment for PDAC and is recommended in all patients ( 38 ), while radiotherapy or chemo-radiotherapy, especially in R1 resection, can be considered to improve OS of the patients ( 39 ).…”

Section: Discussionsupporting

confidence: 92%

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Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study

Shao

et al. 2021

Front. Surg.

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Background: Total pancreatectomy (TP) seems to be experiencing a renaissance in recent years. In this study, we aimed to determine the long-term survival of pancreatic ductal adenocarcinoma (PDAC) patients who underwent TP by comparing with pancreaticoduodenectomy (PD), and formulate a nomogram to predict overall survival (OS) for PDAC individuals following TP.Methods: Patients who were diagnosed with PDAC and received PD (n = 5,619) or TP (n = 1,248) between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. OS and cancer-specific survival (CSS) of the PD and TP groups were compared using Kaplan-Meier method and log-rank test. Furthermore, Patients receiving TP were randomly divided into the training and validation cohorts. Univariate and multivariate Cox regression were applied to identify the independent factors affecting OS to construct the nomogram. The performance of the nomogram was measured according to concordance index (C-index), calibration plots, and decision curve analysis (DCA).Results: There were no significant differences in OS and CSS between TP and PD groups. Age, differentiation, AJCC T stage, radiotherapy, chemotherapy, and lymph node ratio (LNR) were identified as independent prognostic indicators to construct the nomogram. The C-indexes were 0.67 and 0.69 in the training and validation cohorts, while 0.59 and 0.60 of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system. The calibration curves showed good uniformity between the nomogram prediction and actual observation. DCA curves indicated the nomogram was preferable to the AJCC staging system in terms of the clinical utility. A new risk stratification system was constructed which could distinguish patients with different survival risks.Conclusions: For PDAC patients following TP, the OS and CSS are similar to those who following PD. We developed a practical nomogram to predict the prognosis of PDAC patients treated with TP, which showed superiority over the conventional AJCC staging system.

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Section: Discussionsupporting

confidence: 92%

“…In this study, N stage turned out not to be a predictor in the model, whereas LNR was taken into account instead. LNR contains information on both the number of positive nodes and the total number of nodes evaluated, and increased LNR may better indicate the tendency of metastasis, as was reported in a previous study ( 35 ).…”

Section: Discussionmentioning

confidence: 74%

Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study

Shao

et al. 2021

Front. Surg.

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“…As this kind of ranking based concordance measurement is suitable to evaluate predicted outcomes with censored data, various concordance indices are developed using different methods to handle censoring such as Uno's C-index using IPCW. Besides concordance indices, other predictability metrics such as the time-dependent AUC, which applies a similar idea as the AUC in binary classification problem but needs to divide the whole time interval into multiple time points, are also adopted in some survival studies (Li et al, 2020). Given that the model predictability could actually be measured by multiple types of indices, we suggest that hybrid evaluation metrics should be applied in practice to provide relatively comprehensive assessments for the fitted model.…”

Section: Suggestion To Look At Multiple Indicesmentioning

confidence: 99%

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Zhang

Wong

Mann

et al. 2021

Preprint

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Survival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of the performance of survival models mostly focus on classical clinical data with traditional statistical survival models, with prediction accuracy being often the only measurement of model performance. Moreover, survival analysis approaches for censored omics data have not been fully studied. The typical solution is to truncate survival time, to define a new status variable, and to then perform a binary classification analysis.Here, we develop a benchmarking framework that compares survival models for both clinical datasets and omics datasets, and that not only focuses on classical statistical survival models but also incorporates state-of-art machine learning survival models with multiple performance evaluation measurements including model predictability, stability, flexibility and computational issues. Our comprehensive comparison framework shows that optimality is dataset and analysis method dependent. The key result is that there is no one size fits all solution for any of the criteria and any of the methods. Some methods with a high C-index suffer from computational exhaustion and instability. The implications of our framework give researchers an insight on how different survival model implementations vary over real world datasets. We highlight that care is needed when selecting methods and recommend specifically not to consider the C-index as the only performance evaluation metric as alternative metrics measure other performance aspects.Code availabilityhttps://github.com/SydneyBioX/SurvBenchmarkContactjean.yang@sydney.edu.au

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“…Thus, the findings regarding the prognosis of PDAC at different primary tumor locations remain inconsistent and even conflicting. Use of the TNM staging system for stratifying PDAC patients in order to determine the precise prognosis is questionable (10). Until now, research of the survival difference for PDAC at different locations after curative-intent surgical resection has been rare.…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of Novel Nomograms for Predicting Prognosis of Pancreatic Ductal Adenocarcinoma After Surgery According to Different Primary Cancer Locations

Liao

Chen

et al. 2021

Front. Oncol.

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Background/AimsPancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations.Methods1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set.ResultsPrimary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8th AJCC staging system, and achieved clinical validity.ConclusionsPHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery.

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Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

Cited by 15 publications

References 34 publications

Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study

Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Construction and Validation of Novel Nomograms for Predicting Prognosis of Pancreatic Ductal Adenocarcinoma After Surgery According to Different Primary Cancer Locations

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