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Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer.

Authors :
Zhou M
Huang H
Bao D
Chen M
Source :
Clinical imaging [Clin Imaging] 2024 Dec; Vol. 116, pp. 110327. Date of Electronic Publication: 2024 Oct 20.
Publication Year :
2024

Abstract

Aim: This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes.<br />Materials and Methods: Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX <subscript>-median</subscript> ) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance.<br />Results: Pre-treatmentβ <subscript>-10th</subscript> percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ <subscript>-median</subscript> showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %).<br />Discussion: The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1873-4499
Volume :
116
Database :
MEDLINE
Journal :
Clinical imaging
Publication Type :
Academic Journal
Accession number :
39454478
Full Text :
https://doi.org/10.1016/j.clinimag.2024.110327