Neural-network dose-prediction for rectal spacer stratification in dose-escalated prostate radiotherapy

TY - JOUR

T1 - Neural-network dose-prediction for rectal spacer stratification in dose-escalated prostate radiotherapy

AU - Thomas, Christopher

AU - Dregely, Isabel

AU - Oksuz, Ilkay

AU - Urbano, Teresa Guerrero

AU - Greener, Tony

AU - King, Andrew P

AU - Barrington, Sally F

N1 - Funding Information: Christopher Thomas and Sally F. Barrington acknowledge support from the National Institute for Health Research and Social Care (NIHR) (RP‐2‐16‐07‐001). King's College London and UCL Comprehensive Cancer Imaging Centre is funded by Cancer Research UK and Engineering and Physical Sciences Research Council (EPSRC) in association with the Medical Research Council (UK) and Department of Health and Social Care (England). The research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St. Thomas’ NHS Foundation Trust and King's College London. This work was also supported by the Wellcome/EPSRC Centre for Medical Engineering at King's College London (WT 203148/Z/16/Z) and by the EPSRC (EP/P001009/1). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. Funding Information: Christopher Thomas and Sally F. Barrington acknowledge support from the National Institute for Health Research and Social Care (NIHR) (RP-2-16-07-001). King's College London and UCL Comprehensive Cancer Imaging Centre is funded by Cancer Research UK and Engineering and Physical Sciences Research Council (EPSRC) in association with the Medical Research Council (UK) and Department of Health and Social Care (England). The research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St. Thomas? NHS Foundation Trust and King's College London. This work was also supported by the Wellcome/EPSRC Centre for Medical Engineering at King's College London (WT 203148/Z/16/Z) and by the EPSRC (EP/P001009/1). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. Publisher Copyright: © 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.

PY - 2022/4

Y1 - 2022/4

N2 - Purpose: To develop a knowledge-based decision-support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time- and resource-intensive radiotherapy (RT) planning. Methods: Forty-four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose-escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions. The network was used to predict rectal doses for 14 unseen test patients, with associated toxicity risks calculated according to published data. All metrics obtained using the network were compared to conventionally planned values. Results: The neural network stratified patients with an accuracy of 100% based on optimal rectal dose–volume histogram constraints and 78.6% based on mandatory constraints. The network predicted dose-derived grade 2 rectal bleeding risk within 95% confidence limits of -1.9% to +1.7% of conventional risk estimates (risk range 3.5%–9.9%) and late grade 2 fecal incontinence risk within -0.8% to +1.5% (risk range 2.3%–5.7%). Prediction of high-resolution 3D dose distributions took 0.7 s. Conclusions: The feasibility of using a neural network to provide rapid decision support for RS insertion prior to RT has been demonstrated, and the potential for time and resource savings highlighted. Directly after target and healthy tissue delineation, the network is able to (i) risk stratify most patients with a high degree of accuracy to prioritize which patients would likely derive greatest benefit from RS insertion and (ii) identify patients close to the stratification threshold who would require conventional planning.

AB - Purpose: To develop a knowledge-based decision-support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time- and resource-intensive radiotherapy (RT) planning. Methods: Forty-four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose-escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions. The network was used to predict rectal doses for 14 unseen test patients, with associated toxicity risks calculated according to published data. All metrics obtained using the network were compared to conventionally planned values. Results: The neural network stratified patients with an accuracy of 100% based on optimal rectal dose–volume histogram constraints and 78.6% based on mandatory constraints. The network predicted dose-derived grade 2 rectal bleeding risk within 95% confidence limits of -1.9% to +1.7% of conventional risk estimates (risk range 3.5%–9.9%) and late grade 2 fecal incontinence risk within -0.8% to +1.5% (risk range 2.3%–5.7%). Prediction of high-resolution 3D dose distributions took 0.7 s. Conclusions: The feasibility of using a neural network to provide rapid decision support for RS insertion prior to RT has been demonstrated, and the potential for time and resource savings highlighted. Directly after target and healthy tissue delineation, the network is able to (i) risk stratify most patients with a high degree of accuracy to prioritize which patients would likely derive greatest benefit from RS insertion and (ii) identify patients close to the stratification threshold who would require conventional planning.

UR - http://www.scopus.com/inward/record.url?scp=85126048763&partnerID=8YFLogxK

U2 - 10.1002/mp.15575

DO - 10.1002/mp.15575

M3 - Article

C2 - 35218024

SN - 2473-4209

VL - 49

SP - 2172

EP - 2182

JO - Medical Physics

JF - Medical Physics

IS - 4

ER -