Patient preferences for treatment modalities for localised prostate cancer.

Objectives: To assess the patient preferences and utility scores for the different conventional and innovative treatment modalities for localised prostate cancer (PCa). Subjects and Methods: Patients treated for localised PCa and healthy volunteers were invited to fill out a treatment-outcome scenario questionnaire. Participants ranked six different treatments for localised PCa from most to least favourable, prior to information. In a next step, treatment procedures, toxicity, risk of biochemical recurrence and follow-up regimen were comprehensibly described for each of the six treatments (i.e. treatment-outcome scenarios), after which patients re-ranked the six treatments. Additionally, participants gave a visual analogue scale (VAS) and time trade-off (TTO) score for each scenario. Differences between utility scores were tested by Friedman tests with post hoc Wilcoxon signed-rank tests. Results: Eighty patients and twenty-nine healthy volunteers were included in the study. Before receiving treatment-outcome scenario information, participants ranked magnetic resonance-guided adaptive radiotherapy most often as their first choice (35%). After treatment information was received, active surveillance was most often ranked as the first choice (41%). Utility scores were significantly different between the six treatment-outcome scenarios, and active surveillance, non- and minimal-invasive treatments received higher scores. Conclusions: Active surveillance and non-invasive treatment for localised PCa were the most preferred options by PCa patients and healthy volunteers and received among the highest utility scores. Treatment preferences change after treatment information is received.

Implementation of Magnetic Resonance Imaging-Guided Radiation Therapy in Routine Care: Opportunities and Challenges in the United States.

Purpose: Magnetic resonance image (MRI)-guided radiation therapy with the 1.5 Tesla magnetic resonance linear accelerator (MR-Linac) is a rapidly evolving and emerging treatment. The MR-Linac literature mainly focused on clinical and technological factors in technology implementation, but it is relatively silent on health care system-related factors. Consequently, there is a lack of understanding of opportunities and barriers in implementing the MR-Linac from a health care system perspective. This study addresses this gap with a case study of the US health care system. Methods and Materials: An exploratory, qualitative research design was used. Data collection consisted of 23 semistructured interviews ranging from clinical experts at the radiation therapy and radiology department to insurance commissioners in 7 US hospitals. Analysis of opportunities and barriers was guided by the Nonadoption, Abandonment, Scale-up, Spread and Sustainability framework for new medical technologies in health care organizations. Results: Opportunities included high-precision MR-guidance during radiation therapy with potential continued technical advances and better patient outcomes. MR-Linac also offers opportunities for research, professional, and economic development. Barriers included the lack of empirical evidence of clinical effectiveness, technological complexity, and large staffing and structural investments. Furthermore, the presence of patients with disadvantaged socioeconomic background, and the lack of appropriate reimbursement as well as regulatory conditions can hinder technology implementation. Conclusions: Our study confirms the current literature on implementing the MR-Linac, but also reveals additional challenges for the US health care system. Alongside the well-known clinical and technical factors, also professional, socioeconomic, market, and governing influences affect technology implementation. These findings highlight new connections to facilitate technology uptake and provide a richer start to understanding its long-term effect.

Early health economic analysis of 1.5 T MRI-guided radiotherapy for localized prostate cancer: Decision analytic modelling.

BACKGROUND AND PURPOSE: 1.5 Tesla magnetic resonance imaging radiotherapy linear accelerator (MR-Linac) is gaining interest for treatment of localized prostate cancer. Clinical evidence is lacking and it therefore remains uncertain whether MR-Linac is cost-effective. An early health economic analysis was performed to calculate the necessary relative reduction in complications and the maximum price of MR-Linac (5 fractions) to be cost-effective compared to 5, 20 and 39 fractionation schedules of external beam radiotherapy (EBRT) and low-dose-rate (LDR) brachytherapy. MATERIALS AND METHODS: A state transition model was developed for men with localized prostate cancer. Complication rates such as grade ≥2 urinary, grade ≥2 bowel and sexual complications, and utilities were based on systematic literature searches. Costs were estimated from a Dutch healthcare perspective. Threshold analyses were performed to identify the thresholds of complications and costs for MR-Linac to be cost-effective, while holding other outcomes such as biochemical progression and mortality constant. One-way sensitivity analyses were performed to outline uncertainty outcomes. RESULTS: At €6460 per patient, no reductions in complications were needed to consider MR-Linac cost-effective compared to EBRT 20 and 39 fractions. Compared to EBRT 5 fractions and LDR brachytherapy, MR-Linac was found to be cost-effective when complications are relatively reduced by 54% and 66% respectively. Results are highly sensitive to the utilities of urinary, bowel and sexual complications and the probability of biochemical progression. CONCLUSIONS: MR-Linac is found to be cost-effective compared to 20 and 39 fractions EBRT at baseline. For MR-Linac to become cost-effective over 5 fractions EBRT and LDR brachytherapy, it has to reduce complications substantially or be offered at lower costs.

Problems and Promises of Introducing the Magnetic Resonance Imaging Linear Accelerator Into Routine Care: The Case of Prostate Cancer.

The new radiotherapy high field, 1.5 Tesla MRI-guided linear accelerator (MR-Linac) is being clinically introduced. Sensing and evaluating opportunities and barriers at an early stage will facilitate its eventual scale-up. This study investigates the opportunities and barriers to the implementation of MR-Linac into prostate cancer care based on 43 semi-structured interviews with Dutch oncology care professionals, hospital and division directors, patients, payers and industry. The analysis was guided by the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability framework of new medical technologies and services. Opportunities included: the acquirement of (1) advanced MRI-guided radiotherapy technology with (2) the potential for improved patient outcomes and (3) economic benefits, as well as (4) professional development and (5) a higher hospital quality profile. Barriers included: (1) technical complexities, (2) substantial staffing and structural investments, (3) the current lack of empirical evidence of clinical benefits, (4) professional silos, and (5) the presence of patient referral patterns. While our study confirms the expected technical and clinical prospects from the literature, it also reveals economic, organizational, and socio-political challenges.

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.

OBJECTIVE: The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. MATERIALS AND METHODS: Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. RESULTS: Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI's potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a "local champion." Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. CONCLUSION: In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. KEY POINTS: • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.