Role of serial multiparametric magnetic resonance imaging in prostate cancer active surveillance

AIM:To examine whether addition of 3T multiparametric magnetic resonance imaging(mp MRI)to an active surveillance protocol could detect aggressive or progressive prostate cancer.METHODS:Twenty-three patients with low risk disease were enrolled on this active surveillance study,all of which had Gleason score 6 or less disease.All patients had clinical assessments,including digital rectal examination and prostate specific antigen(PSA)testing,every 6 mo with annual 3T mp MRI scans with gadolinium contrast and minimum sextant prostate biopsies.The MRI images were anonymized of patient identifiers and clinical information and each scan underwentradiological review without the other results known.Descriptive statistics for demographics and follow-up as well as the sensitivity and specificity of mp MRI to identify prostate cancer and progressive disease were calculated.RESULTS:During follow-up(median 24.8 mo)11 of 23 patients with low-risk prostate cancer had disease progression and were taken off study to receive definitive treatment.Disease progression was identified through upstaging of Gleason score on subsequent biopsies for all 11 patients with only 2 patients also having a PSA doubling time of less than 2 years.All 23 patients had biopsy confirmed prostate cancer but only 10 had a positive index of suspicion on mp MRI scans at baseline(43.5% sensitivity).Aggressive disease prediction from baseline mpM RI scans had satisfactory specificity(81.8%)but low sensitivity(58.3%).Twentytwo patients had serial mp MRI scans and evidence of disease progression was seen for 3 patients all of whom had upstaging of Gleason score on biopsy(30% specificity and 100% sensitivity).CONCLUSION:Addition of mp MRI imaging in active surveillance decision making may help in identifying aggressive disease amongst men with indolent prostate cancer earlier than traditional methods.

Dynamical modeling and controllability analysis of a flexible needle in soft tissue

This paper is concerned with deriving a dynamic model of a moderately flexible needle inserted into soft tissue,where the model’s output is the needle deflection.The main advantages of the proposed dynamic modeling approach are that the presented model structure involves parameters that are all measurable or identifiable by simple exper-iments and that it considers the same inputs that are currently used in the clinical practice of manual needle insertion.Conventional manual needle insertion suffers from the fact that flexible needles bend during insertion and their trajectories often vary from those planned,resulting in positioning errors.Enhancement of needle insertion accuracy via robot-assisted needle steering has received significant attention in the past decade.A common assumption in previous research has been that the needle behavior during insertion can be adequately described by static models relating the needle’s forces and torques to its deflection.For closed-loop control purposes,however,a dynamic model of the flexible needle in soft tissue is desired.In this paper,we propose a Lagrangian-based dynamic model for the coupled needle/tissue system,and analyze the response of the dynamic system.Steerability(controllability)analysis is also performed,which is only possible with a dynamic model.The proposed dynamic model can serve as a cornerstone of future research into designing dynamics-based control strategies for closed-loop needle steering in soft tissue aimed at minimizing position error.