Towards quality control and harmonization of deep learning CT radiomics: An in-silico feasibility study with virtual...

Radiomic imaging biomarkers are increasingly studied in oncology as a means to support disease prognosis and personalized treatment planning. While deep learning (DL) offers scalable alternatives to handcrafted radiomic features, DL-derived biomarkers are sensitive to variations in image acquisition protocols and scanner hardware-even within a single imaging modality. To ensure reliable and reproducible biomarker estimation, it is essential to (1) provide clinicians with quantitative uncertainty estimates associated with biomarker predictions, and (2) address acquisition-induced variability through harmonization strategies that render consistent performance across diverse imaging conditions.

To evaluate uncertainty estimation as a reliability metric for biomarker prediction and to assess the role of image harmonization in improving cross-scanner inference, we develop deep learning radiomics (DLR) models for joint estimation of biomarkers and associated uncertainties and apply linear harmonization filters to standardize imaging conditions.

We constructed hybrid digital phantoms by embedding 20,000 virtual colorectal liver metastases into 20 clinical CT liver images to generate metastases-laden simulated scans. The proposed DLR models jointly estimated a selected set of biomarkers and their associated aleatoric uncertainties from simulated images. Models were trained, validated, and tested using a 70:20:10 data split. Variability in CT image acquisition was modelled using two scanner types, two reconstruction kernels, and three x-ray tube current settings. DLR performance was evaluated under direct inference (matched training and test scanners), cross-scanner inference, and harmonized inference using linear harmonization filters. Quantitative evaluation was based on correlation coefficients and root mean squared errors (RMSEs).

The estimated uncertainties were consistently higher for biomarker predictions with larger deviations from ground truth. Excluding high-uncertainty predictions improved concordance between predictions and ground truth biomarkers. The proportion of uncertain predictions increased under cross-scanner inference relative to direct inference, indicating reduced biomarker reliability under heterogeneous imaging conditions. Furthermore, application of harmonization filters reduced RMSEs by an average of 43% across biomarkers and experiments during cross-scanner inference, demonstrating improved cross-scanner consistency.

In this controlled in-silico study, uncertainty estimation provided a practical reliability metric for DL-based radiomic biomarker prediction, while image harmonization improved reproducibility across heterogeneous acquisition conditions. These findings demonstrate methodological feasibility within a simulation-based framework but have not yet been validated on clinical CT data, motivating future clinical validation and translation.