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Whole-tumor histogram analysis of apparent diffusion coefficients for predicting lymphovascular space invasion in stage IB-IIA cervical cancer.
- Source :
- Frontiers in Oncology; 2023, p1-9, 9p
- Publication Year :
- 2023
-
Abstract
- Objectives: To investigate the value of apparent diffusion coefficient (ADC) histogram analysis based on whole tumor volume for the preoperative prediction of lymphovascular space invasion (LVSI) in patients with stage IB-IIA cervical cancer. Methods: Fifty consecutive patients with stage IB-IIA cervical cancer were stratified into LVSI-positive (n = 24) and LVSI-negative (n = 26) groups according to the postoperative pathology. All patients underwent pelvic 3.0T diffusion-weighted imaging with b-values of 50 and 800 s/mm2 preoperatively. Whole-tumor ADC histogram analysis was performed. Differences in the clinical characteristics, conventional magnetic resonance imaging (MRI) features, and ADC histogram parameters between the two groups were analyzed. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of ADC histogram parameters in predicting LVSI. Results: ADCmax, ADCrange, ADC90, ADC95, and ADC99 were significantly lower in the LVSI-positive group than in the LVSI-negative group (all P-values < 0.05), whereas no significant differences were reported for the remaining ADC parameters, clinical characteristics, and conventional MRI features between the groups (all P-values > 0.05). For predicting LVSI in stage IB-IIA cervical cancer, a cutoff ADCmax of 1.75×10-3 mm2/s achieved the largest area under ROC curve (Az) of 0.750, followed by a cutoff ADCrange of 1.36×10-3 mm2/s and ADC99 of 1.75×10-3 mm2/s (Az = 0.748 and 0.729, respectively), and the cutoff ADC90 and ADC95 achieved an Az of <0.70. Conclusion: Whole-tumor ADC histogram analysis has potential value for preoperative prediction of LVSI in patients with stage IB-IIA cervical cancer. ADCmax, ADCrange, and ADC99 are promising prediction parameters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2234943X
- Database :
- Complementary Index
- Journal :
- Frontiers in Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 164773584
- Full Text :
- https://doi.org/10.3389/fonc.2023.1206659