Dataset Information

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

ABSTRACT:

Objective

To compare the capability of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) histogram analysis in epithelial ovarian tumor categorization.

Methods

We retrospectively recruited 52 patients with pathologically proven ovarian serous epithelial cancer from our institution. ADC histogram analysis was performed using FeAture Explorer software after outlining the whole lesion area on the ADC map. The ADC histogram parameter difference between subgroups was compared; the correlation between the quantitative parameters on MRI and Ki-67 expression was calculated for both groups.

Results

The repeatability of ADC measurements across the two methods was good; the area method (ADCarea) had better performance in repeatability than the ROI method (ADCroi). The ADCroi, ADCarea, Ktrans, and Kep values significantly differed between the groups (P < 0.05). The histogram parameters (percent10, entropy, minimum, range and variance) and DCE parameter (Ktrans) were strongly correlated with Ki-67 expression (P = 0.000), while the conventional ADC measurements were not significantly correlated with Ki-67 expression (P > 0.05). Overall, Ktrans had the best diagnostic performance for discriminating type I with type II ovarian cancers (AUC = 0.826).

Conclusion

In the present study, both diffusion-weighted imaging (DWI) and DCE MRI could help classify ovarian cancer patients with high accuracy. ADC histogram analysis could accurately reflect the proliferative capability of tumor cells to some extent.

SUBMITTER: Zhou Q

PROVIDER: S-EPMC10086884 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

Zhou Qing Q Jin Jun J Wang Yingfang Y Wang Yida Y Wang Tianping T Zhang He H Liu Yuantao Y

American journal of translational research 20230315 3

<h4>Objective</h4>To compare the capability of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) histogram analysis in epithelial ovarian tumor categorization.<h4>Methods</h4>We retrospectively recruited 52 patients with pathologically proven ovarian serous epithelial cancer from our institution. ADC histogram analysis was performed using FeAture Explorer software after outlining the whole lesion area on the ADC map. The ADC histogram param ...[more]

PMID: 37056838

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Project description:PurposeGlioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.MethodsPre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.ResultsGrade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.ConclusionsADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

Project description:BackgroundAccording to hormone receptor (HR) status, human epidermal growth factor 2 positive (HER2+) breast carcinoma can be divided into HR- and HR+, with different treatment and prognosis. We analyzed the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings, apparent diffusion coefficient (ADC) value and the combination of DCE-MRI and ADC value of HER2+/HR- breast carcinoma.MethodsTotally 259 cases (96 HR-, 163 HR+) of pathologically verified HER2+ breast carcinoma were collected. Patients underwent DCE-MRI and diffusion weighted imaging (DWI). The morphological characteristics, internal enhancement characteristics, early enhancement rate (EER), and time-signal intensity curves (TIC) were recorded, and ADC values were measured. The relationship between each feature and HER2+/HR- breast cancer was analyzed. Area under the cures (AUC) was used to compare diagnostic performance of DCE-MRI, ADC value and the combination of DCE-MRI and ADC value.ResultsHER2+/HR- breast cancer presented as non-mass enhancement (NME), mass with NME, whereas HER2+/HR+ breast cancer presented as mass (P<0.001). HR- cases showed a round or oval shape with circumscribed margins, whereas HR+ cases showed an irregular mass with irregular or spiculated margins (P=0.001, P=0.028). The size of the mass, the internal enhancement characteristics, EER, and TIC did not differ significantly between the two HER2+ breast carcinomas. The ADC values for HR- and HR+ breast cancers were [1.2 (1.14, 1.33)] ×10-3 mm2/s and [1.0 (0.89, 1.11)] ×10-3 mm2/s, respectively, which were statistically significant (Z=-9.119, P<0.001). The ADC value can be used for diagnosing HER2+/HR- breast carcinoma, with the threshold value of 1.095×10-3 mm2/s [negative predictive value (NPV) of 89.8%, sensitivity of 86.5% and specificity of 70.6%]. The AUCs of ADC value, DCE-MRI, and DCE-MRI combined with ADC value were 0.839, 0.689 and 0.860, respectively. AUC of the DCE-MRI combined with ADC value was significantly higher than DCE-MRI alone (P<0.0001).ConclusionsThe diagnostic performance of the DCE-MRI combined with ADC value was good in diagnosing HER2+/HR- breast cancers. MRI is an effective tool in diagnosing HER2+/HR- breast carcinoma, which will help select the clinical treatment plan and determine the prognosis.

Dataset Information

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

Objective

Methods

Results

Conclusion

Publications

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

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