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Context: Canagliflozin is a sodium glucose cotransporter 2 inhibitor developed to treat type 2 diabetes mellitus (T2DM)., Objective: Our objective is to describe the effects of canagliflozin on bone mineral density (BMD) and bone biomarkers in patients with T2DM., Design: This was a randomized study, consisting of a 26-week, double-blind, placebo-controlled period and a 78-week, double-blind, placebo-controlled extension., Setting: This study was undertaken in 90 centers in 17 countries., Patients: Patients were aged 55-80 years (N = 716) and whose T2DM was inadequately controlled on a stable antihyperglycemic regimen., Interventions: Canagliflozin 100 or 300 mg or placebo were administered once daily., Outcome and Measures: BMD was assessed using dual-energy x-ray absorptiometry at weeks 26, 52, and 104. Bone strength was assessed using quantitative computed tomography and finite element analysis at week 52. Serum collagen type 1 β-carboxy-telopeptide, osteocalcin, and estradiol were assessed at weeks 26 and 52., Results: Canagliflozin doses of 100 and 300 mg were associated with a decrease in total hip BMD over 104 weeks, (placebo-subtracted changes: -0.9% and -1.2%, respectively), but not at other sites measured (femoral neck, lumbar spine, or distal forearm). No meaningful changes in bone strength were observed. At week 52, canagliflozin was associated with an increase in collagen type 1 β-carboxy-telopeptide that was significantly correlated with a reduction in body weight, an increase in osteocalcin, and, in women, a decrease in estradiol., Conclusions: In older patients with T2DM, canagliflozin showed small but significant reductions in total hip BMD and increases in bone formation and resorption biomarkers, due at least in part to weight loss.

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.

The development and implementation of quantitative imaging biomarkers has been hampered by the inconsistent and often incorrect use of terminology related to these markers. Sponsored by the Radiological Society of North America, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claims. Where possible, this working group adapted existing definitions derived from national or international standards bodies rather than invent new definitions for these terms. This terminology also serves as a foundation for the design of studies that evaluate the technical performance of quantitative imaging biomarkers and for studies of algorithms that generate the quantitative imaging biomarkers from clinical scans. This paper provides examples of research studies and quantitative imaging biomarker claims that use terminology consistent with these definitions as well as examples of the rampant confusion in this emerging field. We provide recommendations for appropriate use of quantitative imaging biomarker terminological concepts. It is hoped that this document will assist researchers and regulatory reviewers who examine quantitative imaging biomarkers and will also inform regulatory guidance. More consistent and correct use of terminology could advance regulatory science, improve clinical research, and provide better care for patients who undergo imaging studies., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Unlabelled: Quantitative imaging biomarkers could speed the development of new treatments for unmet medical needs and improve routine clinical care. However, it is not clear how the various regulatory and nonregulatory (eg, reimbursement) processes (often referred to as pathways) relate, nor is it clear which data need to be collected to support these different pathways most efficiently, given the time- and cost-intensive nature of doing so. The purpose of this article is to describe current thinking regarding these pathways emerging from diverse stakeholders interested and active in the definition, validation, and qualification of quantitative imaging biomarkers and to propose processes to facilitate the development and use of quantitative imaging biomarkers. A flexible framework is described that may be adapted for each imaging application, providing mechanisms that can be used to develop, assess, and evaluate relevant biomarkers. From this framework, processes can be mapped that would be applicable to both imaging product development and to quantitative imaging biomarker development aimed at increasing the effectiveness and availability of quantitative imaging., Supplemental Material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100800/-/DC1., (RSNA, 2011)

Medical imaging has seen substantial and rapid technical advances during the past decade, including advances in image acquisition devices, processing and analysis software, and agents to enhance specificity. Traditionally, medical imaging has defined anatomy, but increasingly newer, more advanced, imaging technologies provide biochemical and physiologic information based on both static and dynamic modalities. These advanced technologies are important not only for detecting disease but for characterizing and assessing change of disease with time or therapy. Because of the rapidity of these advances, research to determine the utility of quantitative imaging in either clinical research or clinical practice has not had time to mature. Methods to appropriately develop, assess, regulate, and reimburse must be established for these advanced technologies. Efficient and methodical processes that meet the needs of stakeholders in the biomedical research community, therapeutics developers, and health care delivery enterprises will ultimately benefit individual patients. To help address this, the authors formed a collaborative program-the Quantitative Imaging Biomarker Alliance. This program draws from the very successful precedent set by the Integrating the Healthcare Enterprise effort but is adapted to the needs of imaging science. Strategic guidance supporting the development, qualification, and deployment of quantitative imaging biomarkers will lead to improved standardization of imaging tests, proof of imaging test performance, and greater use of imaging to predict the biologic behavior of tissue and monitor therapy response. These, in turn, confer value to corporate stakeholders, providing incentives to bring new and innovative products to market., (© RSNA, 2011.)

PurposeA magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.