OBJECTIVE To determine minimum threshold levels of activity set by surgeons for urological cancer surgery, and to relate threshold levels to stated current procedural volume. METHODS In all, 307 consultant urological surgeons were sent a questionnaire asking them to state for four urological cancer operations of different complexity their current procedural volume; whether minimum volume thresholds per surgeon should be implemented; and if so, the level of such thresholds; 212 (69%) replied. RESULTS For all four procedures ≥ 75% of surgeons advocated the setting of a minimum volume threshold. Overall, surgeons set the highest thresholds for radical prostatectomy and the lowest for radical cystectomy with continent diversion. There was no significant association between either the principle of supporting minimum volume thresholds or the level of such a threshold and the number of years worked as a consultant surgeon. The level of surgeon‐derived minimum thresholds increased with increasing surgeon procedural volume. CONCLUSION Most surgeons supported the principle of setting minimum volume thresholds. These thresholds appear to be influenced by current procedural volume and by procedural complexity. By setting thresholds greater than their current volume, some surgeons implicitly indicate that their current volume is insufficient to maintain their surgical competency.
Purpose of review The aim of active surveillance is to avoid radical treatment and its side-effects in men who have truly low risk prostate cancer, whilst offering radical treatment to those men who are at higher risk of local progression or metastatic disease. The traditional tools used to attribute these risk categories are prostate specific antigen, digital rectal examination, transrectal biopsy and their repeated application over time. MRI is emerging as a tool which may be able to more accurately determine the risk of significant disease at diagnosis and progression of disease over time. This review will examine the role of MRI in men on active surveillance. Recent findings The body of work on MRI as a tool for the detection of significant cancer is rapidly increasing, both in men undergoing initial assessment for prostate cancer risk, and in those who have low risk cancer on standard transrectal ultrasound guided biopsy. In addition, the use of MRI as a tool to detect change in prostate cancer is being explored by a small number of groups. Summary Multiparametric MRI is a useful tool in the initial assessment and surveillance of men who choose to avoid radical treatment when first diagnosed with localized prostate cancer.
Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-specific IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a task-agnostic IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed algorithm using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images, from 850 patients. The proposed reward shaping strategy, with appropriately weighted task-specific and task-agnostic qualities, successfully identified samples that need re-acquisition due to defected imaging process.
One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels: First, it is shown that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these (generalised) linear models are proposed as hyperparameters, to weigh multiple networks that independently represent individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference, for much improved efficiency. Experimental results based on data from 850 patients, for the application of automating radiologist labelling multi-parametric MR, compare the proposed combiner networks with other commonly-adopted end-to-end networks. Using the added advantages of obtaining and interpreting the modality combining rules, in terms of the linear weights or odds-ratios on individual image modalities, three clinical applications are presented for prostate cancer segmentation, including modality availability assessment, importance quantification and rule discovery.