Improving survival prediction of oesophageal cancer patients treated with external beam radiotherapy for dysphagia

INTRODUCTION

The recent POLDER trial investigated the effects of external beam radiotherapy (EBRT) on dysphagia caused by incurable oesophageal cancer. An estimated life expectancy of minimally three months was required for inclusion. However, nearly one-third of the included patients died within three months. The aim of this study was to investigate if the use of prediction models could have improved the physician's estimation of the patient's survival.

METHODS

Data from the POLDER trial (N = 110) were linked to the Netherlands Cancer Registry to retrieve patient, tumour, and treatment characteristics. Two published prediction models (the SOURCE model and Steyerberg model) were used to predict three-month survival for all patients included in the POLDER trial. Predicted survival probabilities were dichotomised and the accuracy, sensitivity, specificity, and the area under the curve (AUC) were used to evaluate the predictive performance.

RESULTS

The SOURCE and Steyerberg model had an accuracy of 79% and 64%, and an AUC of 0.76 and 0.60 (p = .017), respectively. The SOURCE model had higher specificity across survival cut-off probabilities, the Steyerberg model had a higher sensitivity beyond the survival probability cut-off of 0.7. Using optimal cut-off probabilities, SOURCE would have wrongfully included 16/110 patients into the POLDER and Steyerberg 34/110.

CONCLUSION

The SOURCE model was found to be a more useful decision aid than the Steyerberg model. Results showed that the SOURCE model could be used for three-month survival predictions for patients that are considered for palliative treatment of dysphagia caused by oesophageal cancer in addition to clinicians' judgement.

Improving survival prediction of patients treated with external beam radiotherapy for dysphagia in esophageal cancer using prediction models

358

Background: The recently published POLDER trial investigated the effects of external beam radiotherapy (EBRT) on dysphagia caused by incurable esophageal cancer. As the effects of EBRT were presumed not to be immediate, an estimated life expectancy of minimally three months was required for inclusion. However, nearly a third of the included patients died within three months. The aim of this study was to investigate if the use of prediction models could have improved the physician’s estimation of the patient’s survival, and thus the eligibility for EBRT treatment. Methods: Data from the POLDER trial (N = 110) were linked to the Netherlands Cancer Registry to retrieve additional patient, tumor and treatment characteristics. Two published prediction models (the SOURCE model and Steyerberg model) were used to predict overall survival for all patients included in the POLDER trial. Predicted survival probabilities were dichotomized (predicted deceased/alive at three months) and the positive predictive value, negative predictive value, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the predictive performance. DeLong’s test was used to test the difference between the AUCs of the SOURCE and Steyerberg models for statistical significance. Results: In the POLDER trial, 35 patients were unjustly presumed to survive three months. Predicting survival at three months, the SOURCE and Steyerberg model had an AUC of 0.76 and 0.60 respectively. The difference between the AUCs of the models was significant (p =.017). Under optimal survival cut-off scores, SOURCE would have incorrectly predicted 16 patients to survive three months. For the Steyerberg model this was 22 patients. Furthermore, using SOURCE under these cut-off scores, seven patients were incorrectly predicted to not survive three months compared to 18 patients using the Steyerberg model. Conclusions: Results showed that the SOURCE and Steyerberg models could have improved survival predictions compared to clinical judgement alone. The SOURCE model was found to be a more useful decision aid than the Steyerberg model as it was more accurate. Accepting that a small proportion of patients are incorrectly predicted not to survive three months and are not considered for EBRT treatment, we recommend using the SOURCE model for patients that are considered for palliative treatment of dysphagia caused by esophageal cancer.

Informing Patients With Esophagogastric Cancer About Treatment Outcomes by Using a Web-Based Tool and Training: Development and Evaluation Study

Background

Due to the increasing use of shared decision-making, patients with esophagogastric cancer play an increasingly important role in the decision-making process. To be able to make well-informed decisions, patients need to be adequately informed about treatment options and their outcomes, namely survival, side effects or complications, and health-related quality of life. Web-based tools and training programs can aid physicians in this complex task. However, to date, none of these instruments are available for use in informing patients with esophagogastric cancer about treatment outcomes.

Objective

This study aims to develop and evaluate the feasibility of using a web-based prediction tool and supporting communication skills training to improve how physicians inform patients with esophagogastric cancer about treatment outcomes. By improving the provision of treatment outcome information, we aim to stimulate the use of information that is evidence-based, precise, and personalized to patient and tumor characteristics and is communicated in a way that is tailored to individual information needs.

Methods

We designed a web-based, physician-assisted prediction tool—Source—to be used during consultations by using an iterative, user-centered approach. The accompanying communication skills training was developed based on specific learning objectives, literature, and expert opinions. The Source tool was tested in several rounds—a face-to-face focus group with 6 patients and survivors, semistructured interviews with 5 patients, think-aloud sessions with 3 medical oncologists, and interviews with 6 field experts. In a final pilot study, the Source tool and training were tested as a combined intervention by 5 medical oncology fellows and 3 esophagogastric outpatients.

Results

The Source tool contains personalized prediction models and data from meta-analyses regarding survival, treatment side effects and complications, and health-related quality of life. The treatment outcomes were visualized in a patient-friendly manner by using pictographs and bar and line graphs. The communication skills training consisted of blended learning for clinicians comprising e-learning and 2 face-to-face sessions. Adjustments to improve both training and the Source tool were made according to feedback from all testing rounds.

Conclusions

The Source tool and training could play an important role in informing patients with esophagogastric cancer about treatment outcomes in an evidence-based, precise, personalized, and tailored manner. The preliminary evaluation results are promising and provide valuable input for the further development and testing of both elements. However, the remaining uncertainty about treatment outcomes in patients and established habits in doctors, in addition to the varying trust in the prediction models, might influence the effectiveness of the tool and training in daily practice. We are currently conducting a multicenter clinical trial to investigate the impact that the combined tool and training have on the provision of information in the context of treatment decision-making.

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 sublocation, 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.

SOURCE: Prediction Models for Overall Survival in Patients With Metastatic and Potentially Curable Esophageal and Gastric Cancer

BACKGROUND

Personalized prediction of treatment outcomes can aid patients with cancer when deciding on treatment options. Existing prediction models for esophageal and gastric cancer, however, have mostly been developed for survival prediction after surgery (ie, when treatment has already been completed). Furthermore, prediction models for patients with metastatic cancer are scarce. The aim of this study was to develop prediction models of overall survival at diagnosis for patients with potentially curable and metastatic esophageal and gastric cancer (the SOURCE study).

METHODS

Data from 13,080 patients with esophageal or gastric cancer diagnosed in 2015 through 2018 were retrieved from the prospective Netherlands Cancer Registry. Four Cox proportional hazards regression models were created for patients with potentially curable and metastatic esophageal or gastric cancer. Predictors, including treatment type, were selected using the Akaike information criterion. The models were validated with temporal cross-validation on their C-index and calibration.

RESULTS

The validated model's C-index was 0.78 for potentially curable gastric cancer and 0.80 for potentially curable esophageal cancer. For the metastatic models, the c-indices were 0.72 and 0.73 for esophageal and gastric cancer, respectively. The 95% confidence interval of the calibration intercepts and slopes contain the values 0 and 1, respectively.

CONCLUSIONS

The SOURCE prediction models show fair to good c-indices and an overall good calibration. The models are the first in esophageal and gastric cancer to predict survival at diagnosis for a variety of treatments. Future research is needed to demonstrate their value for shared decision-making in clinical practice.

Health-related quality of life in curatively-treated patients with esophageal or gastric cancer: A systematic review and meta-analysis

Surgery and chemoradiotherapy can potentially cure esophageal and gastric cancer patients, although they may impact health-related quality of life (HRQoL). We aim to systemically review and meta-analyze literature to determine the effect of curative treatments on HRQoL in esophageal and gastric cancer.- A systematic search was performed identifying studies assessing HRQoL. Meta-analyses were performed on baseline and subsequent time-points.- From the 6067 articles retrieved, 49 studies were included (61 % low quality). Meta-analyses showed short-term HRQoL differences between esophageal cancer patients receiving definitive chemoradiotherapy (dCRT), neoadjuvant chemo(radio)therapy (nC(R)T), or surgery alone (p < 0.001), with better HRQoL with nC(R)T and surgery compared to dCRT. Over the course of 12 months, no HRQoL difference was identified between treatments in esophageal cancer (p = 0.633). Esophagectomy, but not gastrectomy, resulted in a clinically relevant decline in HRQoL. No long-term HRQoL differences were identified between curative treatments in esophageal and gastric cancer. More high-quality HRQoL studies are warranted.

SOURCE: Prediction models for overall survival in patients with metastatic and potentially curable esophageal and gastric cancer

301

Background: Prediction models in cancer care can provide personalized prediction outcomes and can aid in shared decision making. Existing prediction models for esophageal and gastric cancer (EGC), however, are mostly aimed at predicting survival after a curative treatment has already been completed. The aim of this study is to develop prediction models, called SOURCE, to predict overall survival at diagnosis in potentially curable and metastatic EGC patients. Methods: The data from 12,756 EGC patients diagnosed between 2014-2017 were retrieved from the prospective Netherlands Cancer Registry. Four Cox regression models were created for potentially curable and metastatic cancers of the esophagus and stomach. Predictors, including treatment type, were selected using the Akaike Information Criterion. The models were validated with temporal cross-validation on their concordance-index (c-index) and calibration. Results: The validated model’s c-index is 0.76 for potentially curable cancer. For the metastatic models, the c-indices are 0.71 and 0.70 for esophageal and gastric cancer, respectively. The calibration intercepts and slopes lie in the 95% confidence interval of 0 and 1, respectively. The included model variables are given in Table. Conclusions: The SOURCE prediction models show fair c-indices and an overall good calibration. The models are the first in EGC to include treatment as a predictor. The models predict survival at diagnosis for a variety of treatments and therefore could have a high clinical applicability. Future research is needed to demonstrate the effect on shared decision making in clinical practice.

[Table: see text]

TOXview: a novel graphical presentation of cancer treatment toxicity profiles

Background: Toxicity profiles play a crucial role in the choice between specific palliative chemotherapy regimens. To optimize the quality of life for cancer patients, patients should be adequately informed about potential toxicities before undergoing chemotherapy. Therefore, we constructed TOXviews, a novel graphical presentation and overview of toxicity profiles to improve information provision about adverse events. As an example, we analyzed first-line chemotherapy regimens for advanced esophagogastric cancer (AEGC). Methods: We searched PubMed, EMBASE, CENTRAL, ASCO and ESMO for prospective phase II or III randomized controlled trials (RCTs) on palliative first-line systemic treatment for AEGC until February 2017. We extracted proportions of Common Terminology Criteria for Adverse Events grade 1-2 (mild) and 3-4 (severe) adverse events from each chemotherapy arm and pooled these by using single-arm meta-analysis. Toxicity profiles per chemotherapy regimen were visualized in bidirectional bar charts with pooled proportions plus 95% confidence intervals. For comparative analysis, chemotherapy regimens were grouped in singlets, doublets and triplets. Results: We included 92 RCTs with a total of 16,963 patients. TOXviews for 3 fluoropyrimidine singlets, 5 cisplatin-containing doublets (C-doublets), 10 fluoropyrimidine non-cisplatin containing doublets (F-doublets), 4 anthracycline-containing triplets (A-triplets) and 5 taxane-containing triplets (T-triplets) were constructed. C-doublets, A-triplets and T-triplets all showed an increased incidence of grade 3-4 adverse events and clinically relevant grade 1-2 adverse events compared to F-doublets. Conclusion: TOXview provides a new graphical presentation and overview of chemotherapy toxicities. TOXviews can be used to educate physicians about the incidences of AEs of systemic therapy and improve informed decision-making.

COMplot, A Graphical Presentation of Complication Profiles and Adverse Effects for the Curative Treatment of Gastric Cancer: A Systematic Review and Meta-Analysis

Background: For the curative treatment of gastric cancer, several neoadjuvant, and adjuvant treatment-regimens are available which have shown to improve overall survival. No overview is available regarding toxicity and surgery related outcomes. Our aim was to construct a novel graphical method concerning adverse events (AEs) associated with multimodality treatment and perform a meta-analysis to compare different clinically relevant cytotoxic regimens with each other.

Methods: The PubMed, EMBASE, CENTRAL, and ASCO/ESMO databases were searched up to May 2019 for randomized controlled trials investigating curative treatment regimens for gastric cancer. To construct single and bidirectional bar-charts (COMplots), grade 1–2 and grade 3–5 AEs were extracted per cytotoxic regimen. For surgery-related outcomes a pre-specified set of complications was used. Thereafter, treatment-arms comparing the same regimens were combined in a single-arm random-effects meta-analysis and pooled-proportions were calculated with 95% confidence-intervals. Comparative meta-analyses were performed based on clinical relevance and compound similarity.

Results: In total 16 RCTs (n = 4,526 patients) were included investigating pre-operative-therapy and 39 RCTs investigating adjuvant-therapy (n = 13,732 patients). Pre-operative COMplots were created for among others; 5-fluorouracil/leucovorin-oxaliplatin-docetaxel (FLOT), epirubicin-cisplatin-fluoropyrimidine (ECF), cisplatin-fluoropyrimidine (CF), and oxaliplatin-fluoropyrimidine (FOx). Pre-operative FLOT showed a minor increase in grade 1–2 and grade 3–4 AEs compared to pre-operative ECF, CF, and FOx. A pooled analysis of patients who had received pre-operative therapy compared to patients who underwent direct surgery did not reveal any significant difference in surgery related morbidity/mortality. When we compared three commonly used adjuvant regimens; S-1 had the lowest amount of grade 3–4 AEs compared to capecitabine with oxaliplatin (CAPOX) and 5-FU with radiotherapy (5-FU+RT).

Conclusion: COMplot provides a novel tool to visualize and compare treatment related AEs for gastric cancer. Based on our comparisons, pre-operative FLOT had a manageable toxicity profile compared to other pre-operative doublet or triplet regimens. We found no evidence indicating surgical outcomes might be hampered by pre-operative therapy. Adjuvant S-1 had a more favorable toxicity profile compared to CAPOX and 5-FU+RT.

Quality of Life During Palliative Systemic Therapy for Esophagogastric Cancer: Systematic Review and Meta-Analysis

Background

Palliative systemic therapy can prolong life and reduce tumor-related symptoms for patients with advanced esophagogastric cancer. However, side effects of treatment could negatively affect health-related quality of life (HRQoL). Our aim was to review the literature and conduct a meta-analysis to examine the effect of palliative systemic therapy on HRQoL.

Methods

EMBASE, Medline, and Central were searched for phase II/III randomized controlled trials until April 2018 investigating palliative systemic therapy and HRQoL. Meta-analysis was performed on baseline and follow-up summary values of global health status (GHS) and other European Organisation for Research and Treatment of Cancer scales. A clinically relevant change and difference of 10 points (scale 0–100) was set to assess the course of HRQoL over time within treatment arms as well as between arms.

Results

We included 43 randomized controlled trials (N = 13 727 patients). In the first-line and beyond first-line treatment setting, pooled baseline GHS mean estimates were 54.6 (95% confidence interval = 51.9 to 57.3) and 57.9 (95% confidence interval = 55.7 to 60.1), respectively. Thirty-nine (81.3%) treatment arms showed a stable GHS over the course of time. Anthracycline-based triplets, fluoropyrimidine-based doublets without cisplatin, and the addition of trastuzumab to chemotherapy were found to have favorable HRQoL outcomes. HRQoL benefit was observed for taxane monotherapy and several targeted agents over best supportive care beyond first line.

Conclusions

Patients reported impaired GHS at baseline and generally remained stable over time. Anthracycline-based triplets and fluoropyrimidine-based doublets without cisplatin may be preferable first-line treatment options regarding HRQoL for HER2-negative disease. Taxanes and targeted agents could provide HRQoL benefit beyond first line compared with best supportive care.

SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

Prediction models are only sparsely available for metastatic oesophagogastric cancer. Because treatment in this setting is often preference-based, decision-making with the aid of a prediction model is wanted. The aim of this study is to construct a prediction model, called SOURCE, for the overall survival in patients with metastatic oesophagogastric cancer. Data from patients with metastatic oesophageal (n = 8010) or gastric (n = 4763) cancer diagnosed during 2005⁻2015 were retrieved from the nationwide Netherlands cancer registry. A multivariate Cox regression model was created to predict overall survival for various treatments. Predictor selection was performed via the Akaike Information Criterion and a Delphi consensus among experts in palliative oesophagogastric cancer. Validation was performed according to a temporal internal-external scheme. The predictive quality was assessed with the concordance-index (c-index) and calibration. The model c-indices showed consistent discriminative ability during validation: 0.71 for oesophageal cancer and 0.68 for gastric cancer. The calibration showed an average slope of 1.0 and intercept of 0.0 for both tumour locations, indicating a close agreement between predicted and observed survival. With a fair c-index and good calibration, SOURCE provides a solid foundation for further investigation in clinical practice to determine its added value in shared decision making.