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  1. Home
  3. Photon-counting Detector Ct Spectral Reconstructions For Radiomics-based Liver Lesion Classification: A Multicenter Study.
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  3. Photon-counting Detector Ct Spectral Reconstructions For Radiomics-based Liver Lesion Classification: A Multicenter Study.

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Photon-counting Detector CT Spectral Reconstructions for Radiomics-based Liver Lesion Classification: A Multicenter

Friedrich L Pietsch, Abhinay K Vellala, Florian Haag

    Investigative Radiology
    |April 2, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Photon Counting Detector CT (PCD-CT) with radiomics and machine learning accurately differentiates benign and malignant liver lesions. Higher energy reconstructions and virtual non-contrast (VNC) images provide the most stable and reliable results for lesion classification.

    Keywords:
    diagnostic imaginglivermachine learningmaterial decomposition imagingoncologyphoton-counting CTradiomicsspectral imagingvirtual monochromatic imaging

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    Area of Science:

    • Medical Imaging
    • Radiology
    • Artificial Intelligence in Medicine

    Background:

    • Accurate noninvasive classification of hepatic lesions is challenging with conventional CT.
    • Photon Counting Detector CT (PCD-CT) offers spectral imaging for enhanced tissue characterization.
    • Radiomics and machine learning show promise for improving diagnostic accuracy.

    Purpose of the Study:

    • Evaluate radiomics-based machine learning for differentiating benign and malignant liver lesions.
    • Utilize multispectral images from contrast-enhanced PCD-CT.
    • Assess the impact of different spectral datasets on classification performance.

    Main Methods:

    • Multicenter study using PCD-CT data from 378 patients with hepatic lesions.
    • Automated lesion segmentation using a nnU-Net model.
  • Radiomic feature extraction from 9 spectral datasets (40-180 keV, VNC, IDM).
  • Patient-based classification using Random Forest models.

    • Main Results:

    • Random Forest model trained on VNC achieved the highest performance (AUC: 0.899, Accuracy: 83.5%).
    • Models tested on higher energy levels (110, 140, 180 keV) and VNC showed the most stable performance (median AUC ≥ 0.83).
    • Lower energy levels (40, 50 keV) and Iodine Density Maps (IDM) yielded lower and more variable results.

    Conclusions:

    • Radiomics-based machine learning reliably differentiates benign and malignant hepatic lesions on PCD-CT.
    • Higher energy reconstructions and VNC images are crucial for stable classification performance.
    • Integrating these findings into clinical workflows can aid noninvasive risk stratification and reduce invasive procedures.