Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy

Cost-effectiveness analysis of hydrogel spacer for rectal toxicity reduction in prostate external beam radiotherapy

INTRODUCTION

Contemporary methods of external beam radiotherapy for prostate cancer have reduced toxicity rates through beam modulation and image guidance, however, rectal injury has not been eliminated completely in this population. For patients at greatest risk of developing rectal toxicities, hydrogel spacers are a viable option for risk reduction. Translation of clinical trial results into routine clinical practice relies on an understanding of the economic implications. This study completed a cost-effectiveness analysis of hydrogel spacers in the Australian healthcare setting.

METHOD

Simulation of possible health states following treatment was performed using a Markov model. Model outcomes included the incremental cost-effectiveness ratio and the net monetary benefit (NMB) at three published willingness-to-pay thresholds derived from literature. Probabilistic sensitivity analyses were provided on these results. A baseline cohort without hydrogel spacer use was compared to treat all and selective use cohorts. Cost variation scenarios were also investigated to assess the impact of hydrogel spacer cost on outcomes.

RESULTS

Using hydrogel spacers in a selective cohort was more likely to be cost-effective than giving to all patients (NMB -$43 versus -$997, respectively); however, the incremental cost-effectiveness ratio was not below the $28 000 willingness-to-pay threshold for a healthcare provider perspective. These outcomes were influenced by large parameter uncertainty. Cost variation strategies are worth investigating further as a method to achieve willingness-to-pay threshold targets.

CONCLUSION

The influence of parameter uncertainty currently limits the cost-effectiveness of this intervention in the Australian public health setting. However, a cost variation solution has been demonstrated to improve cost-effectiveness estimates for selected patients and should be examined further.

Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

Introduction

Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIR^TM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.

Methods

This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC_DLR, ERC_Conv, Non-ERC_DLR, and Non-ERC_Conv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor.

Results

The Non-ERC_DLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor.

Conclusion

Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.