scholarly journals OP08.03: Preoperative prediction of uterine sarcoma using T2 weighted imaging and apparent diffusion coefficient (ADC) on MRI and its clinical application

2018 ◽  
Vol 52 ◽  
pp. 86-86
Author(s):  
Y. Oka ◽  
M. Tamori
2020 ◽  
Vol 61 (12) ◽  
pp. 1724-1732
Author(s):  
Bin Yan ◽  
Xiufen Liang ◽  
Tingting Zhao ◽  
Caixia Ding ◽  
Ming Zhang

Background The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown. Purpose To explore the utility of standard deviation (SD) of the ADC (ADCSD) for predicting the tumor grade in patients with EC. Material and Methods We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADCmean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman’s linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADCSD and the ADCmean) and the Ki-67 index. The Kruskal–Wallis and Mann–Whitney U tests were used to compare differences in the index results among tumor grades. Results A significant difference in ADCSD was observed among the tumor grades ( P=0.000), and the ADCSD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10−6mm2 /s, P=0.000). A statistically significant positive correlation was observed between ADCSD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADCSD ≥240.2×10−6mm2 /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively. Conclusion Based on the intratumor heterogeneity of EC, ADCSD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.


2021 ◽  
Vol 11 ◽  
Author(s):  
Chunli Li ◽  
Jiandong Yin

PurposeTo develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Materials and MethodsOne hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram.ResultsThe AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility.ConclusionsThe radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.


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