|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Although the CT findings are nonspecific, typical MR imaging findings, including those on the ADC map and dynamic contrast-enhanced MR imaging, may be helpful for differentiating glomangiopericytomas from other hypervascular tumors in the head and neck.
|
31806599 |
2020 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Tumor ADC and tumor size on MRI may be useful parameters in association with pathologic upgrading in biopsy-proven grade I endometrial cancer.
|
31206949 |
2020 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted.
|
31431368 |
2020 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Multivariate analysis revealed that reverse target sign on hepatobiliary phase in FNHs (p = 0.009), iso or hyperintensity on ADC map in FNHs and HCAs (p = 0.009, < 0.001, respectively), central hypointensity on arterial phase in NETs (p = 0.001), hepatobiliary target sign in ICCs (p = 0.002), the presence of septum and capsule in HCCs (all p < 0.001) were significant independent features of each tumor group over other tumor groups.
|
31134313 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
The max R was 0.557 (p = 2.04E-14) in T<sub>1</sub>C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67.
|
30315419 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
For predicting high-grade meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (β=0.874, P=0.110), peritumoral edema (β=0.554, P=0.042), tumor border (β=0.862, P=0.024), tumor location (β=0.545, P=0.039) for morphological characteristics, and tumor size (β=4×10<sup>-5</sup>, P=0.004), QSM kurtosis (β=-5×10<sup>-3</sup>, P=0.058), QSM entropy (β=-0.067, P=0.054), maximum ADC (β=-1.6×10<sup>-3</sup>, P=0.003), ADC kurtosis (β=-0.013, P=0.014) for quantitative characteristics.
|
31136748 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ.
|
31439233 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Maximum standardized uptake value (SUVmax) and ADC values were correlated with pathologic grade, extent of invasion, solid tumor size, and tumor cellularity.Mean solid tumor size (low: 1.7 ± 3.0 mm, indeterminate: 13.9 ± 14.2 mm, and high grade: 30.3 ± 13.5 mm) and SUVmax (low: 1.5 ± 0.2, indeterminate: 3.5 ± 2.5, and high grade: 15.3 ± 0) had a significant relationship with pathologic grade based on 95% confidence intervals (P = .01 and P < .01, respectively).
|
31335678 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Test-retest differences in ADC for each tumour, was scaled to their estimated measurement uncertainty, and 95% confidence limits were calculated, with a null hypothesis that there is no difference between the model distribution and the data.
|
30846790 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Diffusion-weighted (DWI) MRI and ADC maps can reveal regions of high cellularity as surrogate for active tumor.
|
30219612 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Changes in the ADC values and viable tumor fraction supported the fact that the antitumor effect of ADMBs were enhanced by evidence of necrosis ratio (over 70%) and survival tumor cell fraction (20%).
|
31022951 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
The relationship between diffusion-weighted MRI (DW-MRI) imaging biomarkers (apparent diffusion coefficient - ADC) and tumor fibrosis measurement by pathological methods was assessed by Pearson correlation coefficient.
|
30468734 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
We measured ADC within these tumors using a voxel-based 3D whole-tumor measurement method.
|
31694820 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Volumetric segmentation of ADC allowed the identification of the tumor cores, which were smaller in A-IDH<sup>wt</sup> (p < 0.001).
|
30756272 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
All ADC values had good reliability regardless of whether the tumour border was included in quantitative analysis.
|
30599864 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
The mean tumor ADC values at baseline and across the cycles of NAC were significantly different for the responder group.
|
29742918 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
The time signal curve (TIC), early enhancement rate (EER) and ADC values were measured, morphological characteristics were recorded, and the correlation between each image feature and molecular subtypes, prognostic factors (tumor size, pathological grade, lymph node metastasis, ER, PR, HER2, Ki67) was analyzed.
|
30810823 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
The result of ADC values between different zones of tumor showed ADC mean of EC rose from central zone to peripheral zone of tumor gradually and ADC range widened gradually.
|
31511958 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.
|
31484577 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Low minimum ADC values (p = 0.001), unfavorable tumor histopathology (p < 0.001) and the presence of microvascular invasion (p < 0.001) were risk factors for tumor recurrence, while ADC<sub>mean</sub> (p = 0.111) and DWI<sub>T/L</sub> (p = 0.093) showed no significant difference between the groups.
|
31730015 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison.
|
31324948 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Mean ADC values of sinonasal tumors and ADC ratios (ADC<sub>mean</sub> of the tumor to ADC<sub>mean</sub> of pterygoid muscles) were compared with the histopathological diagnosis by utilizing the Kruskal-Wallis non-parametric test.
|
30769222 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Five MRI features demonstrated a significant correlation with malignant histology: irregular borders (p = 0.03); "T2 dark" areas (p = 0.02); presence of central necrosis (p = 0.01); presence of high signal on b1000 DWI (p < 0.001); ADC value lower than 0.82 × 10<sup>-3</sup> mm<sup>2</sup>/s; hyperenhancement of the tumor relative to the myometrium on post-contrast images (p = 0.02); and type 3 enhancing curve on DCE.
|
31250178 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
Functional maps exhibit better discriminative values than anatomical images for discriminating the pathological features of CSCC, with ADC maps showing the best discrimination performance for LN metastasis and V<sub>e</sub> maps showing the best discriminative value for LVSI and tumor grade.
|
30230114 |
2019 |
|
Neoplasms
|
0.100 |
Biomarker
|
group |
BEFREE |
ADC values were obtained using different ROI placement methods: segmentation ADC values of the entire volume (vADC), random ADC values were obtained in 10 different ROI points, and a single ROI in the ADC of the internal auditory canal portion of the tumor.
|
30738940 |
2019 |