Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation

Abstract

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

Keywords: QIBA; multi-class classification; multi-parametric quantitative imaging biomarkers (mp-QIBs); multiparametric classification; phenotype classification.

Figure 1:

An overview of the atherosclerotic plaque classification task. A) Standardized CTA images are acquired in accordance with the existing QIBA profile. B) Images are used to classify tissue type and location at each cross section along the vessel wall. Vessel morphology is validated against histological truth. C) Cross sectional views along the vessel depict tissue type and location as color overlays to the vessel wall. D) Area and location of tissue at each cross section is used as input into a convolutional neural network to generate a phenotype classification (subclinical, stable, unstable) at each vessel cross section. In this way, variations in raw images are filtered out, and focus remains on validated tissue characteristics. Phenotype classification is compared against agreement with an expert pathologist blinded to imaging results.