Predicting low-risk prostate cancer from transperineal saturation biopsies

Introduction: To assess the performance of five previously described clinicopathological definitions of low-risk prostate cancer (PC). Materials and Methods: Men who underwent radical prostatectomy (RP) for clinical stage ≤T2, PSA \u3c10 ng/mL, Gleason score \u3c8 PC, diagnosed by transperineal template-guided saturation biopsy were included. The performance of five previously described criteria (i.e., criteria 1–5, criterion 1 stringent (Gleason score 6 + ≤5mm total max core length PC + ≤3mm max per core length PC) up to criterion 5 less stringent (Gleason score 6-7 with ≤5% Gleason grade 4) was analysed to assess ability of each to predict insignificant disease in RP specimens (defined as Gleason score ≤6 and total tumour volume \u3c2.5mL, or Gleason score 7 with ≤5% grade 4 and total tumour volume \u3c0.7 mL). Results: 994 men who underwent RP were included. Criterion 4 (Gleason score 6) performed best with area under the curve of receiver operating characteristics 0.792. At decision curve analysis, criterion 4 was deemed clinically the best performing transperineal saturation biopsy-based definition for low-risk PC. Conclusions: Gleason score 6 disease demonstrated a superior trade-off between sensitivity and specificity for clarifying low-risk PC that can guide treatment and be used as reference test in diagnostic studies. prostate cancer screening (PSA), testing practices, United Kingdom, Australia, qualitative stud

A decision framework for product global outsourcing in small and medium-sized companies.

This research will focus on small and medium-sized companies. A decision framework of Product Global Outsourcing (PGO) will be presented. This framework integrates and links the elements that could have impacts on product global outsourcing decision-making, and systematically analyzes the decision-making process step by step. The objective of this framework is to present a simplified and reified approach of PGO decision-making for small and medium-sized companies. The framework is organized in five main levels: (1) environment analysis - the identification of the actual situations surrounding and impacting upon the company, including the external and internal environment; (2) total cost analysis - a total cost mathematical model will be developed; (3) objective analysis - setup the product global outsourcing goals from five aspects; (4) strategy and business planning analysis - identify the company\u27s strategy to achieve its objectives, and provide the business planning, such as supplier selection, contract negotiation and project execution management; and (5) risk analysis. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M46. Source: Masters Abstracts International, Volume: 44-03, page: 1476. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

A qualitative comparison of how older breast cancer survivors process treatment information regarding endocrine therapy.

BACKGROUND:It remains unclear how information about aromatase inhibitors (AI) impacts women's decision-making about persistence with endocrine therapy. PURPOSE:To describe and compare how women treated for primary early stage breast cancer either persisting or not persisting with an AI received, interpreted, and acted upon AI-related information. DESIGN:Thematic analysis was used to sort and compare the data into the most salient themes. PARTICIPANTS:Women (N = 54; 27 persisting, 27 not persisting with an AI) aged 65-93 years took part in qualitative interviews. RESULTS:Women in both subgroups described information similarly in terms of its value, volume, type, and source. Aspects of AI-related information that either differed between the subgroups or were misunderstood by one or both subgroups included: (1) knowledge of AI or tamoxifen prior to cancer diagnosis, (2) use of online resources, (3) misconceptions about estrogen, hormone replacement therapies and AI-related symptoms, and (4) risk perception and the meaning and use of recurrence statistics such as Oncotype DX. CONCLUSIONS:Persisters and nonpersisters were similar in their desire for more information about potential side effects and symptom management at AI prescription and subsequent appointments. Differences included how information was obtained and interpreted. Interactive discussion questions are shared that can incorporate these findings into clinical settings

Adjuvant vs. salvage radiation therapy in men with high-risk features after radical prostatectomy: Survey of North American genitourinary expert radiation oncologists

INTRODUCTION: The management of patients with high-risk features after radical prostatectomy (RP) is controversial. Level 1 evidence demonstrates that adjuvant radiation therapy (RT) improves survival compared to no treatment; however, it may overtreat up to 30% of patients, as randomized clinical trials (RCTs) using salvage RT on observation arms failed to reveal a survival advantage of adjuvant RT. We, therefore, sought to determine the current view of adjuvant vs. salvage RT among North American genitourinary (GU) radiation oncology experts. METHODS: A survey was distributed to 88 practicing North American GU physicians serving on decision-making committees of cooperative group research organizations. Questions pertained to opinions regarding adjuvant vs. salvage RT for this patient population. Treatment recommendations were correlated with practice patterns using Fisher's exact test. RESULTS: Forty-two of 88 radiation oncologists completed the survey; 23 (54.8%) recommended adjuvant RT and 19 (45.2%) recommended salvage RT. Recommendation of active surveillance for Gleason 3+4 disease was a significant predictor of salvage RT recommendation (p=0.034), and monthly patient volume approached significance for recommendation of adjuvant over salvage RT; those seeing <15 patients/month trended towards recommending adjuvant over salvage RT (p=0.062). No other demographic factors approached significance. CONCLUSIONS: There is dramatic polarization among North American GU experts regarding optimal management of patients with high-risk features after RP. Ongoing RCTs will determine whether adjuvant RT improves survival over salvage RT. Until then, the almost 50/50 division seen from this analysis should encourage practicing clinicians to discuss the ambiguity with their patients

Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert ≥3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score

BACKGROUND: Although multiparametric magnetic resonance imaging (MRI) has high sensitivity, its lower specificity leads to a high prevalence of false-positive lesions requiring biopsy. OBJECTIVE: To develop and externally validate a scoring system for MRI-detected Prostate Imaging Reporting and Data System (PIRADS)/Likert ≥3 lesions containing clinically significant prostate cancer (csPCa). DESIGN, SETTING, AND PARTICIPANTS: The multicentre Rapid Access to Prostate Imaging and Diagnosis (RAPID) pathway included 1189 patients referred to urology due to elevated age-specific prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE); April 27, 2017 to October 25, 2019. INTERVENTION: Visual-registration or image-fusion targeted and systematic transperineal biopsies for an MRI score of ≥4 or 3 + PSA density ≥0.12 ng/ml/ml. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Fourteen variables were used in multivariable logistic regression for Gleason ≥3 + 4 (primary) and Gleason ≥4 + 3, and PROMIS definition 1 (any ≥4 + 3 or ≥6 mm any grade; secondary). Nomograms were created and a decision curve analysis (DCA) was performed. Models with varying complexity were externally validated in 2374 patients from six international cohorts. RESULTS AND LIMITATIONS: The five-item Imperial RAPID risk score used age, PSA density, prior negative biopsy, prostate volume, and highest MRI score (corrected c-index for Gleason ≥3 + 4 of 0.82 and 0.80-0.86 externally). Incorporating family history, DRE, and Black ethnicity within the eight-item Imperial RAPID risk score provided similar outcomes. The DCA showed similar superiority of all models, with net benefit differences increasing in higher threshold probabilities. At 20%, 30%, and 40% of predicted Gleason ≥3 + 4 prostate cancer, the RAPID risk score was able to reduce, respectively, 11%, 21%, and 31% of biopsies against 1.8%, 6.2%, and 14% of missed csPCa (or 9.6%, 17%, and 26% of foregone biopsies, respectively). CONCLUSIONS: The Imperial RAPID risk score provides a standardised tool for the prediction of csPCa in patients with an MRI-detected PIRADS/Likert ≥3 lesion and can support the decision for prostate biopsy. PATIENT SUMMARY: In this multinational study, we developed a scoring system incorporating clinical and magnetic resonance imaging characteristics to predict which patients have prostate cancer requiring treatment and which patients can safely forego an invasive prostate biopsy. This model was validated in several other countries

Supplier Portfolio Selection and Optimum Volume Allocation: A Knowledge Based Method

Selection of suppliers and allocation of optimum volumes to suppliers is a strategic business decision. This paper presents a decision support method for supplier selection and the optimal allocation of volumes in a supplier portfolio. The requirements for the method were gathered during a case study that was conducted within the logistics unit of Shell Chemicals Europe. The proposed method is based on the classical view by Sprague and Carlson of sequence and interaction of the different phases of decision making in a decision support system and supports Kraljic’s portfolio approach for supplier management. This method aims to help the managers in making decisions on the allocation of volumes to suppliers while simultaneously trying to satisfy conflicting objectives of improvement in benefit and reduction in risk. A mathematical model to struc-ture the problem is presented, knowledge elicited from the managers is used to parameterize the mathemati-cal model and a multi-objective, hierarchical optimization procedure produces ‘trade-off’ outputs. The man-agers can also conduct interactive post optimization ‘what-if’ analysi

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1&nbsp;cc&nbsp;&lt;&nbsp;4000&nbsp;cGy (P&nbsp;=&nbsp;0.01), chest wall dose to 30&nbsp;cc&nbsp;&lt;&nbsp;1900&nbsp;cGy (P&nbsp;=&nbsp;0.035), rib Dmax&nbsp;&lt;&nbsp;5100&nbsp;cGy (P&nbsp;=&nbsp;0.05) and lung dose to 1000&nbsp;cc&nbsp;&lt;&nbsp;70&nbsp;cGy (P&nbsp;=&nbsp;0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1&nbsp;cc, chest wall dose to 30&nbsp;cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

Do Interventions Designed to Support Shared Decision-Making Reduce Health Inequalities? : A Systematic Review and Meta-Analysis

Copyright: © 2014 Durand et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Increasing patient engagement in healthcare has become a health policy priority. However, there has been concern that promoting supported shared decision-making could increase health inequalities. Objective: To evaluate the impact of SDM interventions on disadvantaged groups and health inequalities. Design: Systematic review and meta-analysis of randomised controlled trials and observational studies.Peer reviewe

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table