a genetic-based fuzzy-tabu controller for mobile robot randomised navigation in unknown environment

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Radiotherapy to the primary tumour for newly diagnosed, metastatic prostate cancer (STAMPEDE): a randomised controlled phase 3 trial

BACKGROUND: Local treatment of the prostate might not only improve local control, but also slow the progression of metastatic disease. We hypothesised that radiotherapy (RT) to the prostate would improve overall survival in men presenting with metastatic prostate cancer (PCa) and that the survival benefit would be greater in men with a lower metastatic burden. METHOD: STAMPEDE is a multi-arm multi-stage platform protocol that included a randomised phase III comparison to test the above hypotheses. Standard-of-care (SOC) was lifelong ADT, with up-front docetaxel permitted from Dec-2015. Stratified randomisation within 12 weeks on ADT allocated pts 1:1 to SOC or SOC+RT. Men allocated to RT received daily (55Gy/20f over 4 weeks) or weekly (36Gy/6f over 6 weeks) RT, started ≤8 weeks after randomisation or completion of docetaxel. The RT schedule was nominated before randomisation. The primary outcome measure was death from any cause; secondary outcome measures included failure-free survival (FFS). Comparison of SOC vs SOC+RT for survival had 90% power at 2.5% 1-sided alpha for hazard ratio (HR) of 0.75, requiring approximately 267 control arm deaths. Analyses used Cox proportional hazards & flexible parametric models, adjusted for stratification factors. A pre-specified subgroup analysis tested the effects of prostate RT by baseline metastatic burden. RESULTS: 2061 men with newly-diagnosed M1 PCa were randomised from Jan 2013 to Sep 2016. Randomised groups were well balanced: median age 68yrs; median PSA 97ng/ml; 18% early docetaxel; metastatic burden: 40% lower metastatic burden, 54% higher metastatic burden, 6% unknown in the group as a whole. Prostate RT improved FFS (HR=0.76, 95%CI 0.68, 0.84; p=3.36x10-7 60 ) but not overall survival (HR=0.92, 95%CI 0.80, 1.06; p=0.266). Pre-specified subgroup analysis showed 62 improved overall survival for prostate RT in 819 men with a lower metastatic burden 63 (HR=0.68, 95%CI 0.52, 0.90; p=0.007) but not in 1120 men with a higher metastatic burden (HR=1.07, 95%CI 0.90, 1.28; p=0.300). RT was well-tolerated during (G3-4 5% SOC+RT) and after treatment (G3-4 <1% SOC, 4% SOC+RT). CONCLUSIONS: Radiotherapy to the prostate did not improve survival for unselected patients with newly-diagnosed metastatic prostate cancer, but, in a pre-specified subgroup analysis, did improve survival in men with a lower metastatic burden. Therefore, prostate radiotherapy should be a standard treatment option for men with oligometastatic disease

Radiotherapy to the primary tumour for newly diagnosed, metastatic prostate cancer (STAMPEDE): a randomised controlled phase 3 trial.

Based on previous findings, we hypothesised that radiotherapy to the prostate would improve overall survival in men with metastatic prostate cancer, and that the benefit would be greatest in patients with a low metastatic burden. We aimed to compare standard of care for metastatic prostate cancer, with and without radiotherapy.This article is freely available via Open Access

An Alternative Approach to FCM Activation for Modeling Dynamic Systems

Recurrent neural models such as fuzzy cognitive maps are well established in decision modeling through progressive variations of system’s concepts. However, existing activation functions have shortcomings such as lack of sensitivity to initial concepts’ weights that is due to exaggerated focus on training of network’s causal links. Therefore, in most cases decision outputs converge toward lower and higher extremes and do not represent gray scales. Another disadvantage is that, current models require sufficient time delay for convergence towards results. This makes FCM unable to handle transient changes in input. A new technique has been examined in this paper using a real-life example to improve FCM activation in terms of fast response to dynamic stimuli. A simple expert model of hexapod locomotion is developed without focus on weight training. The system’s response to stimuli is evaluated through a complete six-phase stride to validate the effectiveness of the developed activation function

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

BackgroundCerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP. ObjectiveThis paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data. MethodsCustom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective. ResultsUpon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years. ConclusionsAnecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=36742