Prognostic value of patient-reported quality of life for survival in oesophagogastric cancer: analysis from the population-based POCOP study

Background Accumulating evidence of trials demonstrates that patient-reported health-related quality of life (HRQoL) at diagnosis is prognostic for overall survival (OS) in oesophagogastric cancer. However, real-world data are lacking. Moreover, differences in disease stages and tumour-specific symptoms are usually not taken into consideration. The aim of this population-based study was to assess the prognostic value of HRQoL, including tumour-specific scales, on OS in patients with potentially curable and advanced oesophagogastric cancer. Methods Data were derived from the Netherlands Cancer Registry and the patient reported outcome registry (POCOP). Patients included in POCOP between 2016 and 2018 were stratified for potentially curable (cT1-4aNallM0) or advanced (cT4b or cM1) disease. HRQoL was measured with the EORTC QLQ-C30 and the tumour-specific OG25 module. Cox proportional hazards models assessed the impact of HRQoL, sociodemographic and clinical factors (including treatment) on OS. Results In total, 924 patients were included. Median OS was 38.9 months in potentially curable patients (n = 795) and 10.6 months in patients with advanced disease (n = 129). Global Health Status was independently associated with OS in potentially curable patients (HR 0.89, 99%CI 0.82-0.97), together with several other HRQoL items: appetite loss, dysphagia, eating restrictions, odynophagia, and body image. In advanced disease, the Summary Score was the strongest independent prognostic factor (HR 0.75, 99%CI 0.59-0.94), followed by fatigue, pain, insomnia and role functioning. Conclusion In a real-world setting, HRQoL was prognostic for OS in patients with potentially curable and advanced oesophagogastric cancer. Several HRQoL domains, including the Summary Score and several OG25 items, could be used to develop or update prognostic models.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data

Background: A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Materials and Methods: Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal- external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Results: Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sub location, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. Conclusions: A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice

Accelerating Advances in Cancer Care Research: A Lookback at the 21st Century Cures Act in 2020

The 21st Century Cures Act (Cures Act), signed into law in 2016, was designed to advance new therapies by modernizing clinical trials, funding research initiatives, and accelerating the development and use of health information technology. To analyze the current issues in cancer care related to the implementation and impact of the Cures Act, NCCN convened a multistakeholder working group. Participants discussed the legislation's impact on the oncology community since enactment and identified the remaining gaps and challenges as experienced by stakeholders. In June 2020, the policy recommendations of the working group were presented at the virtual NCCN Policy Summit: Accelerating Advances in Cancer Care Research: A Lookback at the 21st Century Cures Act in 2020. The summit consisted of informative discussions and a multistakeholder panel to explore the recommendations and the future of the Cures Act. This article explores identified policy recommendations from the NCCN Working Group and the NCCN Policy Summit, and analyzes opportunities to advance innovative cancer care and patient access to data.Experimentele farmacotherapi