eprintid: 10189315
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/18/93/15
datestamp: 2024-03-19 08:40:48
lastmod: 2024-03-19 08:40:48
status_changed: 2024-03-19 08:40:48
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Gayo, Iani JMB
creators_name: Saeed, Shaheer U
creators_name: Bonmati, Ester
creators_name: Barratt, Dean C
creators_name: Clarkson, Matthew J
creators_name: Hu, Yipeng
title: The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F42
keywords: Biopsy, Planning, Prostate cancer, Reinforcement learning
note: © The Author(s), 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
abstract: Purpose:
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.//

Methods:
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.//

Results:
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.//

Conclusions:
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.
date: 2024-03-07
date_type: published
official_url: https://doi.org/10.1007/s11548-024-03084-4
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2260025
doi: 10.1007/s11548-024-03084-4
medium: Print-Electronic
pii: 10.1007/s11548-024-03084-4
lyricists_name: Barratt, Dean
lyricists_name: Hu, Yipeng
lyricists_name: Clarkson, Matthew
lyricists_name: Saeed, Shaheer Ullah
lyricists_id: DBARR55
lyricists_id: YHUXX66
lyricists_id: MJCLA42
lyricists_id: SUSAE80
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
funding_acknowledgements: C28070/A30912 [Cancer Research UK]; C28070/A30912 [Cancer Research UK]; C28070/A30912 [Cancer Research UK]; C73666/A31378 [Cancer Research UK]; C73666/A31378 [Cancer Research UK]; C73666/A31378 [Cancer Research UK]; EP/T029404/1 [Cancer Research UK]; EP/T029404/1 [Cancer Research UK]
full_text_status: public
publication: International Journal of Computer Assisted Radiology and Surgery
event_location: Germany
issn: 1861-6410
citation:        Gayo, Iani JMB;    Saeed, Shaheer U;    Bonmati, Ester;    Barratt, Dean C;    Clarkson, Matthew J;    Hu, Yipeng;      (2024)    The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy.                   International Journal of Computer Assisted Radiology and Surgery        10.1007/s11548-024-03084-4 <https://doi.org/10.1007/s11548-024-03084-4>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10189315/1/s11548-024-03084-4.pdf