Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/
 under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

The aim was to develop clinical guidelines for multi-parametric MRI of the prostate by a group of prostate MRI experts from the European Society of Urogenital Radiology (ESUR), based on literature evidence and consensus expert opinion. True evidence-based guidelines could not be formulated, but a compromise, reflected by “minimal” and “optimal” requirements has been made. The scope of these ESUR guidelines is to promulgate high quality MRI in acquisition and evaluation with the correct indications for prostate cancer across the whole of Europe and eventually outside Europe. The guidelines for the optimal technique and three protocols for “detection”, “staging” and “node and bone” are presented. The use of endorectal coil vs. pelvic phased array coil and 1.5 vs. 3 T is discussed. Clinical indications and a PI-RADS classification for structured reporting are presented. Key Points
 • This report provides guidelines for magnetic resonance imaging (MRI) in prostate cancer. • Clinical indications, and minimal and optimal imaging acquisition protocols are provided. • A structured reporting system (PI-RADS) is described.

https://link.springer.com/content/pdf/10.1007%2Fs00330-011-2377-y.pdf

ESUR prostate MR guidelines 2012

Inappropriate activation of the renin-angiotensin system, which plays a central role in the regulation of blood pressure, electrolyte, and volume homeostasis, may represent a major risk factor for hypertension, heart attack, and stroke. Mounting evidence from clinical studies has demonstrated an inverse relationship between circulating vitamin D levels and the blood pressure and/or plasma renin activity, but the mechanism is not understood. We show here that renin expression and plasma angiotensin II production were increased severalfold in vitamin D receptor-null (VDR-null) mice, leading to hypertension, cardiac hypertrophy, and increased water intake. However, the salt- and volume-sensing mechanisms that control renin synthesis are still intact in the mutant mice. In wild-type mice, inhibition of 1,25-dihydroxyvitamin D(3) [1,25(OH)(2)D(3)] synthesis also led to an increase in renin expression, whereas 1,25(OH)(2)D(3) injection led to renin suppression. We found that vitamin D regulation of renin expression was independent of calcium metabolism and that 1,25(OH)(2)D(3) markedly suppressed renin transcription by a VDR-mediated mechanism in cell cultures. Hence, 1,25(OH)(2)D(3) is a novel negative endocrine regulator of the renin-angiotensin system. Its apparent critical role in electrolytes, volume, and blood pressure homeostasis suggests that vitamin D analogues could help prevent or ameliorate hypertension.

/pdf/1-25-dihydroxyvitamin-d3-is-a-negative-endocrine-regulator-1md2kfd2zg.pdf

1,25-Dihydroxyvitamin D3 is a negative endocrine regulator of the renin-angiotensin system

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

Improving breast cancer diagnosis with computer-aided diagnosis

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (A/sub z/=0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (A/sub z/=0.80).

A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

PURPOSE: Area under a receiver operating characteristic (ROC) curve (Az) is widely used as an index of diagnostic performance However, Az is not a meaningful summary of clinical diagnostic performance when high sensitivity must be maintained clinically The authors developed a new ROC partial area index, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region MATERIALS AND METHODS: The mathematical formation of the partial area index was derived from the conventional binormal model Statistical tests of apparent differences in this index were formulated analogous to that of Az One common statistical test involving the partial area index was validated by computer simulations under realistic conditions RESULTS: An example in mammography illustrates a situation in which the partial area index is more meaningful than Az in measuring clinical diagnostic performance CONCLUSION: The partial area index can be used as a m

A receiver operating characteristic partial area index for highly sensitive diagnostic tests

Our study suggests that the combination of diffusion-weighted and dynamic contrast-enhanced MR imaging has the potential to improve the differentiation of prostate cancer from benign prostatic hyperplasia in the central gland.

Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging.

The findings of this retrospective study suggest that computer-aided diagnosis based on a combination of quantitative imaging features derived from multiparametric endorectal MR images might be effective in distinguishing prostate cancer from normal prostate tissue and that apparent diffusion coefficient features and volume transfer constant moderately correlate with tumor Gleason score.

/pdf/quantitative-analysis-of-multiparametric-prostate-mr-images-2wv9h4xegv.pdf

Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.

Yulei Jiang

Papers

Improving breast cancer diagnosis with computer-aided diagnosis

A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

A receiver operating characteristic partial area index for highly sensitive diagnostic tests

Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging.

Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.