Mathematical Modeling of CA19-9 Normalization in Pancreatic Cancer Patients

https://openworks.mdanderson.org/sumexp21/1077/thumbnail.jp

Enhancement Pattern Mapping for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis

BACKGROUND AND AIMS: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions. METHODS: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels. RESULTS: With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73-0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82-0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance. CONCLUSION: With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%

Region-of-Interest Reconstruction from Truncated Cone-Beam CT

This thesis presents a novel algorithm in 3D computed tomography (CT) dedicated to accurate region of interest (ROI) reconstruction from truncated cone-beam projections. Here data acquisition involves cone-beam x-ray sources positioned on any piecewise smooth 3D-curve satisfying the very generic, classical Tuy's conditions and uses only x-rays passing through the ROI. Our ROI-reconstruction algorithm implements an iterative procedure where we systematically alternate intermediary reconstructions by an exact non-truncated cone-beam inversion operator, with an effective density regularization method. We validate the accuracy of our ROI-reconstruction algorithm for a 3D Shepp-Logan phantom, a 3D image of a Mouse, and a 3D image of a human jaw, for different cone-beam acquisition curves, including the twin-orthogonal circles and the spherical spiral curve, by simulating ROI-censored cone-beam data and our iterative ROI-reconstruction for a family of spherical ROIs of various radii. The main result is that, provided the density function is sufficiently regular and the ROI radius is larger than a critical radius, our procedure converges to an $\epsilon$-accurate reconstruction of the density function within the ROI. Our extensive numerical experiments compute the critical radius for various accuracy levels $\epsilon$. These results indicate that our ROI reconstruction is a promising step towards addressing the dose-reduction problem in CT imaging.Mathematics, Department o