Gayo, Iani;
Saeed, Shaheer;
Barratt, Dean;
Clarkson, Matthew;
Hu, Yipeng;
(2022)
Strategising template-guided needle placement for MR-targeted prostate biopsy.
In: Ali, S and van der Sommen, F and Papież, BW and van Eijnatten, M and Jin, Y and Kolenbrander, I, (eds.)
Cancer Prevention Through Early Detection. CaPTion 2022.
(pp. pp. 149-158).
Springer: Cham, Switzerland.
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Abstract
Clinically significant prostate cancer has a better chance to be sampled during ultrasound-guided biopsy procedures, if suspected lesions found in pre-operative magnetic resonance (MR) images are used as targets. However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequential decision making process that involves navigating an ultrasound probe and placing a series of sampling needles for potentially multiple targets. This work aims to learn a reinforcement learning (RL) policy that optimises the actions of continuous positioning of 2D ultrasound views and biopsy needles with respect to a guiding template, such that the MR targets can be sampled efficiently and sufficiently. We first formulate the task as a Markov decision process (MDP) and construct an environment that allows the targeting actions to be performed virtually for individual patients, based on their anatomy and lesions derived from MR images. A patient-specific policy can thus be optimised, before each biopsy procedure, by rewarding positive sampling in the MDP environment. Experiment results from fifty four prostate cancer patients show that the proposed RL-learned policies obtained a mean hit rate (HR) of 93% and an average cancer core length (CCL) of 11 mm, which compared favourably to two alternative baseline strategies designed by humans, without hand-engineered rewards that directly maximise these clinically relevant metrics. Perhaps more interestingly, it is found that the RL agents learned strategies that were adaptive to the lesion size, where spread of the needles was prioritised for smaller lesions. Such a strategy has not been previously reported or commonly adopted in clinical practice, but led to an overall superior targeting performance, achieving higher HR (93% vs 76%) and measured CCL (11.0 mm vs 9.8 mm) when compared with intuitively designed strategies.
| Type: | Proceedings paper |
|---|---|
| Title: | Strategising template-guided needle placement for MR-targeted prostate biopsy |
| Event: | CaPTion @ MICCAI2022 Workshop (Cancer Prevention through early detecTion) |
| Location: | Singapore |
| Dates: | 22 Sep 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-17979-2_15 |
| Publisher version: | https://doi.org/10.1007/978-3-031-17979-2_15 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Reinforcement learning, Prostate cancer, Targeted biopsy, Planning |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152546 |
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