Back to Search Start Over

Using Apparent Diffusion Coefficient (ADC) of Endometrial Cancer MRI to Determine P53 Molecular Subtypes.

Authors :
Zhang F
Wang T
Ning Y
Li S
Chen X
Zhang G
Zhang H
Source :
Current medical imaging [Curr Med Imaging] 2024; Vol. 20, pp. e15734056289592.
Publication Year :
2024

Abstract

Background: Endometrial Cancer (EC) is a highly heterogeneous cancer comprising both histological and molecular subtypes. Using a non-invasive modality method to trigger these subtypes as early as possible can aid clinicians in establishing individualized treatment.<br />Purpose: The study aimed to clarify the value of the Apparent Diffusion Coefficient (ADC) of EC MRI in determining molecular subtypes.<br />Material and Methods: We retrospectively recruited 109 patients with pathologically proven EC (78 endometrioid cancers and 31 non-endometrioid cancers) with available molecular classification from a tertiary centre. MRI was prospectively performed a month prior to surgery; images were blindly interpreted by two experienced radiologists with consensus reading. The ADC value was measured by an experienced radiologist on the commercially available processing workstation. Interoperator measurement consistency was calculated.<br />Results: Our sample comprised 17 PLOE, 32 MSI-H, 31 NSMP, and 29 P53abn ECs. Clinical information did not differ significantly among the groups. The maximum diameter and volume of the lesions differed among the groups. The ADC value in the maximal area (ADCarea) or region of interest (ROI, ADCroi) in the P53abn group was higher than that in the other groups (894.0 ±12.6 and 817.5 ± 83.3 x10-6 mm2/s). The ADC mean values were significantly different between the P53abn group and the other groups (P = 0.000). The nomogram showed the highest discriminative ability to distinguish P53abn EC from other types (AUC: 0.859).<br />Conclusion: Our results have suggested the quantitative MR characteristics (ADC values) derived from preoperative EC MRI to provide useful information in preoperatively determining P53abn cancer.

Details

Language :
English
ISSN :
1573-4056
Volume :
20
Database :
MEDLINE
Journal :
Current medical imaging
Publication Type :
Academic Journal
Accession number :
38639282
Full Text :
https://doi.org/10.2174/0115734056289592240408061811