The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy. : WestminsterResearch

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Title	The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy.
Type	Journal article
Authors	Gayo, I.
	Saeed, Shaheer U
	Bonmati Coll, E.
	Barratt, Dean C
	Clarkson, Matthew J
	Hu, Yipeng
Abstract	Magnetic resonance (MR) imaging targeted prostate cancer (PCa) biopsy enables precise sampling of MR-detected lesions, establishing its importance in recommended clinical practice. Planning for the ultrasound-guided procedure involves pre-selecting needle sampling positions. However, performing this procedure is subject to a number of factors, including MR-to-ultrasound registration, intra-procedure patient movement and soft tissue motions. When a fixed pre-procedure planning is carried out without intra-procedure adaptation, these factors will lead to sampling errors which could cause false positives and false negatives. Reinforcement learning (RL) has been proposed for procedure plannings on similar applications such as this one, because intelligent agents can be trained for both pre-procedure and intra-procedure planning. However, it is not clear if RL is beneficial when it comes to addressing these intra-procedure errors. In this work, we develop and compare imitation learning (IL), supervised by demonstrations of predefined sampling strategy, and RL approaches, under varying degrees of intra-procedure motion and registration error, to represent sources of targeting errors likely to occur in an intra-operative procedure. Based on results using imaging data from 567 PCa patients, we demonstrate the efficacy and value in adopting RL algorithms to provide intelligent intra-procedure action suggestions, compared to IL-based planning supervised by commonly adopted policies. The improvement in biopsy sampling performance for intra-procedure planning has not been observed in experiments with only pre-procedure planning. These findings suggest a strong role for RL in future prospective studies which adopt intra-procedure planning. Our open source code implementation is available here . [Abstract copyright: © 2024. The Author(s).]
Keywords	Reinforcement learning
	Prostate cancer
	Biopsy
	Planning
Journal	International Journal of Computer Assisted Radiology and Surgery
Journal citation	19, p. 1003–1012
ISSN	1861-6429
Year	2024
Publisher	Springer Nature
Publisher's version	s11548-024-03084-4.pdf License CC BY 4.0 File Access Level Open (open metadata and files)
Digital Object Identifier (DOI)	https://doi.org/10.1007/s11548-024-03084-4
PubMed ID	38451359
Published online	07 Mar 2024
Project	C18281/A19169
	EP/T029404/1
Funder	Cancer Research UK
Page range	10.1007/s11548-024-03084-4

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The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy.

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