Development and Validation of a Nomogram for Predicting Survival in Patients with Advanced Pancreatic Ductal Adenocarcinoma

Abstract

This study aimed to develop and validate an effective prognostic nomogram for advanced PDAC patients. We conducted a prospective multicenter cohort study involving 1,526 advanced PDAC patients from three participating hospitals in China between January 1, 2004 and December 31, 2013. Two thirds of the patients were randomly assigned to the training set (n = 1,017), and one third were assigned to the validation set (n = 509). Multivariate cox regression analysis was performed to identify significant prognostic factors for overall survival to develop the nomogram. Internal and external validation using C-index and calibration curve were conducted in the training set and validation set respectively. As results, seven independent prognostic factors were identified: age, tumor stage, tumor size, ALT (alanine aminotransferase), ALB (albumin), CA 19-9, HBV infection status, and these factors were entered into the nomogram. The proposed nomogram showed favorable discrimination and calibration both in the training set and validation set. The C-indexes of the training set and validation set were 0.720 and 0.696 respectively, which were both significantly higher than that of the staging system (C-index = 0.613, P < 0.001). In conclusion, the proposed nomogram may be served as an effective tool for prognostic evaluation of advanced PDAC.

Validation and application of a prognostic model for patients with advanced pancreatic cancer receiving palliative chemotherapy

“…In line with the GS model, the current study helped identify performance status, tumor stage, albumin, and NLR as independent prognostic factors that predict survival outcome. Several prognostic models have been developed for survival prediction among APC patients receiving first‐line palliative chemotherapy . Apart from patient factors (age, sex, performance status, weight loss), laboratory variables (albumin, white cell count, NLR, tumor markers) and tumor characteristics (stage, tumor size, metastatic organ, ascites) have all been reported to be independent prognosticators in different prognostic models .…”

“…Table summarizes and compares 13 published prognostic models for the prediction of the survival outcome of PC patients receiving palliative chemotherapy from a literature review . Only one of the models was designed prospectively ; the others were retrospective studies.…”

“…Table summarizes and compares 13 published prognostic models for the prediction of the survival outcome of PC patients receiving palliative chemotherapy from a literature review . Only one of the models was designed prospectively ; the others were retrospective studies. Of the 13 prognostic models, some were used for the prediction of survival outcome specifically for gemcitabine‐based treatment, while the other models consisted a broad spectrum of chemotherapeutic regimens .…”

“…The performance reflected by C‐index ranged from 0.66 to 0.80 among these prognostic models. Only five models were validated by application to a different cohort . Because the GS model is easily applied and had good performance in term of C‐index value in the original and the current cohort, we believe this model to be widely applicable by clinicians to predict survival outcome in APC patients receiving first‐line palliative chemotherapy.…”

“…[14][15][16][17][18][19][20][21][22][23][24][25] Apart from patient factors (age, sex, performance status, weight loss), laboratory variables (albumin, white cell count, NLR, tumor markers) and tumor characteristics (stage, tumor size, metastatic organ, ascites) have all been reported to be independent prognosticators in different prognostic models. [14][15][16][17][18][19][20][21][22][23][24][25] Although no consistent prognosticators were reported among different reports, all models showed that in addition to tumor factors, patient features also were relevant to prognosis in PC patients. Consequently, the GS model was constructed by the combination of tumor-and patient-related factors, which may be more effective at predicting survival outcome than either tumor or patient factors alone.…”