The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research

Abstract

the goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. one problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (Roi) are calibrated by further Rois such as air, adipose tissue, liver, etc. that are used as control regions (cR). our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by statusquo radiomics stability analysis techniques to only collect information from the cRs which is relevant given a respective task. these criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the cRs. our calibration enhanced the performance for centrilobular emphysema prediction in a copD study and prediction of patients' one-year-survival in an oncological study.

Radiomics in medical imaging—“how-to” guide and critical reflection

“…Apart from the variations in scanners and settings, radiomic feature values are also influenced by patient variabilities, e.g., geometry, which impact the levels of noise and presence of artifacts in an image. Therefore, the aim of a recent study was to quantify these so-called "non-reducible technical variations" and stabilize the radiomic features accordingly [33].…”

Unraveling the interplay of image formation, data representation and learning in CT‐based COPD phenotyping automation: The need for a meta‐strategy

“…We also analyze the impact of incorporating additional features from NL‐ROIs. These features may be helpful for two reasons, either by providing relevant biological information or, indirectly, by internally calibrating the measurements within the lung by features in NL‐ROIs 40,41 . For these meta‐configurations the mRMR algorithm is always used to slightly reduce the impact of overfitting.…”

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

“…Differences in slice thickness, voxel sizes and convolutional kernels can be normalised using a range of approaches such as voxel-size resampling, batch effect correlation, and grey-level normalisation [ 109 , 110 , 111 ]. A predictive internal calibration approach was shown to improve performance of emphysema prediction in a COPD study [ 112 ]. Moving to an ML based automated approach for segmentation has higher accuracy and reduced variability compared to manual segmentation [ 113 ].…”