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ObjectiveTo develop and externally validate a predictive model for detection of significant prostate cancer. Patients and MethodsDevelopment of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. ResultsIn all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer. ConclusionsIndividualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of overdetection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.

Abstract-Case studies and small trials suggest that acupuncture may effectively treat hypertension, but no large randomized trials have been reported. 3 Modalities of complementary and alternative medicine, including acupuncture, are being used by patients with increasing frequency, 4 but these therapies lack demonstrated efficacy and safety for treating cardiovascular disease and hypertension. 5 Acupuncture has been used in traditional Chinese medicine (TCM) to treat symptoms related to hypertension for Ͼ2500 years. 6 Today, acupuncture is commonly used to treat hypertension in China and the West. [7][8][9] The efficacy of acupuncture is well supported for treating postoperative dental pain 10 and nausea 11,12 with few reported adverse effects. 13 Acupuncture has been found effective for treating a number of other acute 14 -16 and chronic 17,18 conditions in a growing number of randomized trials, although opinion differs on the role of placebo effects. 19,20 Mechanistic studies have demonstrated effects of acupuncture on the activity and plasma concentrations of blood pressure modulators, including: renin, aldosterone, angiotensin II, norepinephrine, serotonin, enkephalins, and ␤-endorphins. [21][22][23][24][25][26][27][28][29] The efficacy of acupuncture for treating hypertension is suggested by a large number of published case series and uncontrolled trials. 22,23,25,30 -32 Three randomized trials 33-35 reported significant reductions in BP relative to randomly assigned control groups treated for 4 to 8 weeks, whereas 3 others did not report significant effects of acupuncture relative to control subjects. 36 -38 They were all relatively small trials (nϭ10 to 68), and all but Yin et al 35 were limited

Multiparametric magnetic resonance imaging reported by expert radiologists achieved an excellent negative predictive value and a moderate positive predictive value for significant prostate cancer at 1.5 and 3.0 Tesla.

In men with an abnormal prostate specific antigen/digital rectal examination, multiparametric magnetic resonance imaging detected significant prostate cancer with an excellent negative predictive value and moderate positive predictive value. The use of multiparametric magnetic resonance imaging to diagnose significant prostate cancer may result in a substantial number of unnecessary biopsies while missing a minimum of significant prostate cancers.

Purpose To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). Materials and Methods 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify ten features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. Results Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (p<0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (p > 0.14) were obtained for all institutions. Conclusions A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ.

Background• The diagnosis of prostate cancer has long been plagued by the absence of an imaging tool that reliably detects and localises significant tumours. Recent evidence suggests that multi-parametric MRI could improve the accuracy of diagnostic assessment in prostate cancer. This review serves as a background to a recent USANZ position statement. It aims to provide an overview of MRI techniques and to critically review the published literature on the clinical application of MRI in prostate cancer. Technical Aspects• The combination of anatomical (T2-weighted) MRI with at least two of the three functional MRI parameterswhich include diffusion-weighted imaging, dynamic contrast-enhanced imaging and spectroscopy -will detect greater than 90% of significant (moderate to high risk) tumours; however MRI is less reliable at detecting tumours that are small (<0.5 cc), low grade (Gleason score 6) or in the transitional zone. The higher anatomical resolution provided by 3-Tesla magnets and endorectal coils may improve the accuracy, particularly in primary tumour staging.