Background There is little consensus on a standard approach to analysing bone scan images. The Bone Scan Index (BSI) is predictive of survival in patients with progressive prostate cancer (PCa), but the popularity of this metric is hampered by the tedium of the manual calculation. Objective Develop a fully automated method of quantifying the BSI and determining the clinical value of automated BSI measurements beyond conventional clinical and pathologic features. Design, setting, and participants We conditioned a computer-assisted diagnosis system identifying metastatic lesions on a bone scan to automatically compute BSI measurements. A training group of 795 bone scans was used in the conditioning process. Independent validation of the method used bone scans obtained ≤3 mo from diagnosis of 384 PCa cases in two large population-based cohorts. An experienced analyser (blinded to case identity, prior BSI, and outcome) scored the BSI measurements twice. We measured prediction of outcome using pretreatment Gleason score, clinical stage, and prostate-specific antigen with models that also incorporated either manual or automated BSI measurements. Measurements The agreement between methods was evaluated using Pearson’s correlation coefficient. Discrimination between prognostic models was assessed using the concordance index (C-index). Results and limitations Manual and automated BSI measurements were strongly correlated (ρ = 0.80), correlated more closely (ρ = 0.93) when excluding cases with BSI scores ≥10 (1.8%), and were independently associated with PCa death (p < 0.0001 for each) when added to the prediction model. Predictive accuracy of the base model (C-index: 0.768; 95% confidence interval [CI], 0.702–0.837) increased to 0.794 (95% CI, 0.727–0.860) by adding manual BSI scoring, and increased to 0.825 (95% CI, 0.754–0.881) by adding automated BSI scoring to the base model. Conclusions Automated BSI scoring, with its 100% reproducibility, reduces turnaround time, eliminates operator-dependent subjectivity, and provides important clinical information comparable to that of manual BSI scoring.

Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies.

BackgroundMeasurement of plasma concentration of natriuretic peptides (NPs) is suggested to be of value in diagnosis of cardiac disease in dogs, but many factors other than cardiac status may influence their concentrations. Dog breed potentially is 1 such factor.ObjectiveTo investigate breed variation in plasma concentrations of pro‐atrial natriuretic peptide 31‐67 (proANP 31‐67) and N‐terminal B‐type natriuretic peptide (NT‐proBNP) in healthy dogs.Animals535 healthy, privately owned dogs of 9 breeds were examined at 5 centers as part of the European Union (EU) LUPA project.MethodsAbsence of cardiovascular disease or other clinically relevant organ‐related or systemic disease was ensured by thorough clinical investigation. Plasma concentrations of proANP 31‐67 and NT‐proBNP were measured by commercially available ELISA assays.ResultsOverall significant breed differences were found in proANP 31‐67 (P < .0001) and NT‐proBNP (P < .0001) concentrations. Pair‐wise comparisons between breeds differed in approximately 50% of comparisons for proANP 31‐67 as well as NT‐proBNP concentrations, both when including all centers and within each center. Interquartile range was large for many breeds, especially for NT‐proBNP. Among included breeds, Labrador Retrievers and Newfoundlands had highest median NT‐proBNP concentrations with concentrations 3 times as high as those of Dachshunds. German Shepherds and Cavalier King Charles Spaniels had the highest median proANP 31‐67 concentrations, twice the median concentration in Doberman Pinschers.Conclusions and Clinical ImportanceConsiderable interbreed variation in plasma NP concentrations was found in healthy dogs. Intrabreed variation was large in several breeds, especially for NT‐proBNP. Additional studies are needed to establish breed‐specific reference ranges.

on behalf of the LADIS study group BACKGROUND AND PURPOSE: The corpus callosum (CC) is the most important structure involved in the transmission of interhemispheric information. The aim of this study was to investigate the potential correlation between regional age-related white matter changes (ARWMC) and atrophy of CC in elderly subjects.

AimComputer-aided diagnosis (CAD) software for bone scintigrams have recently been introduced as a clinical quality assurance tool. The purpose of this study was to compare the diagnostic accuracy of two CAD systems, one based on a European and one on a Japanese training database, in a group of bone scans from Japanese patients.MethodThe two CAD software are trained to interpret bone scans using training databases consisting of bone scans with the desired interpretation, metastatic disease or not. One software was trained using 795 bone scans from European patients and the other with 904 bone scans from Japanese patients. The two CAD softwares were evaluated using the same group of 257 Japanese patients, who underwent bone scintigraphy because of suspected metastases of malignant tumors in 2009. The final diagnostic results made by clinicians were used as gold standard.ResultsThe Japanese CAD software showed a higher specificity and accuracy compared to the European CAD software [81 vs. 57 % (p < 0.05) and 82 vs. 61 % (p < 0.05), respectively]. The sensitivity was 90 % for the Japanese CAD software and 83 % for the European CAD software (n.s).ConclusionThe CAD software trained with a Japanese database showed significantly higher performance than the corresponding CAD software trained with a European database for the analysis of bone scans from Japanese patients. These results could at least partly be caused by the physical differences between Japanese and European patients resulting in less influence of attenuation in Japanese patients and possible different judgement of count intensities of hot spots.

BackgroundThe objective of this study was firstly to develop and evaluate an automated method for the detection of new lesions and changes in bone scan index (BSI) in serial bone scans and secondly to evaluate the prognostic value of the method in a group of patients receiving chemotherapy.MethodsThe automated method for detection of new lesions was evaluated in a group of 266 patients using the classifications by three experienced bone scan readers as a gold standard. The prognostic value of the method was assessed in a group of 31 metastatic hormone-refractory prostate cancer patients who were receiving docetaxel. Cox proportional hazards were used to investigate the association between percentage change in BSI, number of new lesions and overall survival. Kaplan-Meier estimates of the survival function were used to indicate a significant difference between patients with an increase/decrease in BSI or those with two or more new lesions or less than two new lesions.ResultsThe automated method detected progression defined as two or more new lesions with a sensitivity of 93% and a specificity of 87%. In the treatment group, both BSI changes and the number of new metastases were significantly associated with survival. Two-year survival for patients with increasing and decreasing BSI from baseline to follow-up scans were 18% and 57% (p = 0.03), respectively. Two-year survival for patients fulfilling and not fulfilling the criterion of two or more new lesions was 35% and 38% (n.s.), respectively.ConclusionsAn automated method can be used to calculate the number of new lesions and changes in BSI in serial bone scans. These imaging biomarkers contained prognostic information in a small group of patients with prostate cancer receiving chemotherapy.

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Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects.This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download.The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.