ArticleMolecular imaging and biology2019
Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression.
Article in Molecular imaging and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 37 citations in OpenAlex.
- A machine learning model for predicting lymph node positivity in ovarian cancer: development, validation, and clinical application.Frontiers in oncology · 2025Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma.Insights into imaging · 2024Article
- Quantitative dynamic contrast-enhanced parameters and intravoxel incoherent motion facilitate the prediction of TP53 status and risk stratification of early-stage endometrial carcinoma.Radiology and oncology · 2023Article
- The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer.BMC medical imaging · 2022Article
- Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions.BMC medical imaging · 2022Article
- Apparent diffusion coefficient histogram analysis for differentiating solid ovarian tumors.Frontiers in oncology · 2022Article
- A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone.Frontiers in oncology · 2022Article
- An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer.Journal of clinical medicine · 2021Article
- Whole-body diffusion-weighted MRI of normal lymph nodes: prospective apparent diffusion coefficient histogram and nodal distribution analysis in a healthy cohort.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- The Feasibility of Combining ADC Value With Texture Analysis of TFrontiers in oncology · 2021Article
- The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer.Frontiers in oncology · 2021Article
- Using Diffusion-Weighted MRI to Predict Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Feasibility Study.Frontiers in endocrinology · 2020Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the potential of apparent diffusion coefficient (ADC) histogram parameters in epithelial ovarian cancer (EOC) for distinguishing different tumor stages and determining lymph node status and correlations between ADC values and p53 and Ki-67 expression. PROCEDURES: Forty-nine EOC patients underwent preoperative magnetic resonance imaging. Staging and lymph node status were determined postoperatively. ADC values were measured using histogram analysis and compared between groups. Relationships between ADCs and Ki-67 and p53 expression were explored.
resultsDC parameters differed significantly between stage I vs II, I vs III, and I vs IV. The parameters were significantly lower in the lymph node-positive group than in the lymph node-negative group, were significantly negatively correlated with Ki-67 labeling index, and were all significantly lower in the mutation-type p53 group than in the wild-type p53 group.
conclusionsADC histogram analysis can help discriminate stage I from advanced-stage EOC and predict lymph node metastasis. ADC parameters were correlated with Ki-67 labeling index; the parameters may help indicate p53 expression.
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