Background: The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels.
With growing concerns for the sustainability of the financial burden that health care—and especially cancer services—poses on the national budgets, the role of health economic analyses in coverage decisions is likely to grow. One of the strategies for the biomedical research field—also in oncology research—to foster coverage and health system implementation, is to anticipate this new role and to involve health technology assessment techniques earlier in various stages of translational research.In this article, we elaborate on the early involvement of health technology assessment in translational research and the concept of Coverage with Evidence Development in The Netherlands Cancer Institute and give two case examples that are currently ongoing: (1) tumour infiltrating lymphocytes therapy for metastatic melanoma; and (2) high-dose chemotherapy for BRCA1-like subgroup in triple-negative breast cancer.We conclude with recommendations for institutional policy.
Comprehensive Cancer Centres (CCCs) serve as critical drivers for improving cancer survival. In Europe, we have developed an Excellence Designation System (EDS) consisting of criteria to assess “excellence” of CCCs in translational research (bench to bedside and back), with the expectation that many European CCCs will aspire to this status.
Background Established breast cancer guidelines and online tools use clinico-pathological factors including age, tumor size and grade to evaluate the risk of recurrence and select patients who are eligible to receive adjuvant chemotherapy (ACT). One of the online tools, PREDICT plus, was recently updated and is the first tool to include HER2 status and method of detection in risk assessment. Another tool to better guide AST decisions is the 70-gene signature. The 70-gene signature is a gene-expression classifier that was developed and extensively validated to predict the risk of distant recurrence in breast cancer. In clinical practice, an ad-hoc combination of clinico-pathological guidelines and gene-expression classifiers are used. The aim of this study is to evaluate the combination of the PREDICT plus tool and the 70-gene signature to optimize adjuvant systemic treatment decisions. Methods For 427 patients participating in the RASTER study (cT1-3N0M0) a 70-gene signature result was available. PREDICT risk estimates at 5 (P5) and 10 (P10) years after diagnosis were calculated using the following variables: age, method of detection, tumor size, tumor grade, number of positive nodes, estrogen receptor, and HER2 status. Patients were considered high risk if their survival probability was less than 95% at 5 years and/or 90% at 10 years. Five-year distant-recurrence-free-interval (DRFI) and distant-disease-free-survival (DDFS) probabilities were evaluated between subgroups based on the 70-gene signature and PREDICT plus. Results Median follow-up was 61.6 months. Patients with a low risk 70-gene signature (n = 219) had a 5-year DRFI of 97.0% (CI: 94.7-99.4) compared to 91.7% (CI: 87.9-95.7) for the 70-gene signature high risk patients (n = 208). The 5-year DRFI for patients with a P5 low risk (n = 228) is 96.8% (CI: 94.2-99.4) compared to 91.7% (CI: 87.9-95.7) for P5 high risk (n = 199). The 5-year DRFI for patients with a P10 low risk (n = 168) is 98.0% (CI: 95.7-100) compared to 92.1% (CI: 88.7-95.6) for P10 high risk (n = 259). ACT data, DRFI and DDFS probabilities in all subgroups are shown in table 1. Among the patients who had a low risk according to PREDICT at 5 years, but a high risk at 10 years (n = 60), the 5-year DRFI was 100% when their tumor was tested as low risk based on the 70-gene signature (13% received CT) compared to 84% in case of a high risk result (55% received CT)(p = 0.03). Table 170-gene signatureP5P10ACT (%)5-year DRFI (%) (95% CI)5-years DDFS (%) (95% CI)LowLowLow8/132 (6)97 (94-100)96 (92-100)HighLowLow21/36 (59)10097 (92-100)LowHighLowxxxxxxxxxLowLowHigh5/38 (13)100100HighHighLowxxxxxxxxxHighLowHigh12/22 (55)84 (69-100)84 (69-100)LowHighHigh20/49 (41)94 (87-100)94 (87-100)HighHighHigh136/150 (91)91 (87-96)89 (84-94) Conclusion Combining PREDICT plus with the 70-gene signature may help to identify early stage node-negative breast cancer patients for whom limited adjuvant systemic treatment might be appropriate and for whom overtreatment can be avoided. Especially in case of a low risk assessment by PREDICT at 5 years but a high risk at 10 years, the 70-gene signature may aid to select those patients at a high risk of recurrence who will benefit most from ACT. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-06-13.
The Organisation of European Cancer Institutes OECI working group on Health Economics and Cost Benefit in Oncology suggests four actions that are needed to improve alignment and integration between clinicians, researchers, and Health Technology Assessment (HTA) experts and agencies: 1) HTA expertise is necessary close to or within the comprehensive cancer centres (CCC); 2) HTA expertise should be physically present throughout the translational research process; 3) Appropriate knowledge is necessary within the research staff; 4) Close cooperation between translational researchers, clinicians, and health economists guarantees clinical ownership.Fulfilling these conditions may help the translational research field in oncology to interact with agencies and efficiently move innovative technologies through the translational research stages into that of implementation and diffusion. This brings innovative treatments faster to the patient with a greater chance of reimbursement.
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