Strategising Template-Guided Needle Placement for MR-targeted Prostate Biopsy

Gayo, Iani; Saeed, Shaheer U.; Barratt, Dean C.; Clarkson, Matthew J.; Hu, Yipeng

doi:10.1007/978-3-031-17979-2_15

Cited by 2 publications

(3 citation statements)

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“…This supports the suggestions made by [ 5 ] that strategies should also consider aiming around the edges of the lesion. Building on previous work in [ 6 ], we train models that can generalise to unseen patients, rather than training patient-specific models for each new patient. Outside of this clinical application, our work is similar to [ 7 ] where the procedure is simulated using 3D masks derived from real patient data.…”

Section: Discussionmentioning

confidence: 99%

“…To investigate the robustness of the agents to registration effects, Gaussian noise is added to the positions of both the prostate and lesion, thereby simulating movement within the observed states, following the previous work [ 6 ]. The scale of Gaussian noise added is varied to simulate different levels of target registration error (TRE), which are in line with levels of registration errors reported in previous studies [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, to the best of our knowledge, there are limited studies that quantify the effectiveness of this strategy, or that search for alternative or optimum sampling strategies. In [ 6 ], reinforcement learning (RL) is used to learn patient-specific targeting strategies. An interesting finding is that RL learned strategies that adapted to the size of the lesions, by spreading the needles more for smaller lesions, achieving a similar detection rate as larger lesions.…”

Section: Introductionmentioning

confidence: 99%

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

Gayo,

Saeed,

Bonmati

et al. 2024

Int J CARS

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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. Our open source code implementation is available here.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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