Advanced Diagnostic & Interventional Radiology Research Center | ADC-derived spatial features can accurately classify adnexal

Advanced Diagnostic & Interventional Radiology Research Center | ADC-derived spatial features can accurately classify adnexal
| Dec 13 2025
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Advanced Diagnostic & Interventional Radiology Research Center

COVID-19 pandemic 

During the COVID-19 pandemic, the Radiology Research Center at Tehran University of Medical Sciences continued its research activities despite the challenges posed by the increased demand for CT scans of COVID-19 patients and the necessity of adhering to strict health protocols. This center played a crucial role in improving medical imaging techniques, optimizing diagnostic protocols, and advancing technologies related to CT scan image analysis.

Faculty members, researchers, and staff remained committed to ensuring the safety and well-being of healthcare professionals and patients while actively engaging in imaging data analysis, developing artificial intelligence algorithms for faster disease detection, publishing scientific articles, and presenting their findings at international conferences. These efforts aimed to enhance diagnostic accuracy, improve treatment processes, and alleviate pressure on healthcare systems.

 

Key achievements of the Radiology Research Center during the COVID-19 pandemic include:


✔️ Development and optimization of lung imaging protocols for faster and more accurate COVID-19 diagnosis
✔️ Implementation of artificial intelligence technologies for automated CT scan analysis and reduced diagnosis time
✔️ Publication of high-impact research articles on innovative imaging methods for COVID-19 patients
✔️ Participation in national and international projects focused on COVID-19 diagnosis and patient management

The center remains dedicated to advancing research in medical imaging and continues to contribute as a leading scientific institution in improving the quality of diagnostic and therapeutic services.

 

Some of the center's significant achievements during the pandemic include:

 

  • Release Date : Apr 7 2024 - 13:34
  • : 10
  • Study time : 1 minute(s)

ADC-derived spatial features can accurately classify adnexal lesions

ADC-derived spatial features can classify adnexal lesions {faces}

Background: The role of quantitative apparent diffusion coefficient (ADC) maps in differentiating adnexal masses is unresolved

Purpose/hypothesis: To propose an objective diagnostic method devised based on spatial features for predicting benignity/malignancy of adnexal masses in ADC maps

Study type: Prospective

Population: In all, 70 women with sonographically indeterminate and histopathologically confirmed adnexal masses (38 benign, 3 borderline, and 29 malignant) were considered for this study

Field strength/sequence: Conventional and diffusion-weighted magnetic resonance (MR) images (b-values = 50, 400, 1000 s/mm2 ) were acquired on a 3T scannerAssessment: For each patient, two radiologists in consensus manually delineated lesion borders in whole ADC map volumes, which were consequently analyzed using spatial models (first-order histogram [FOH], gray-level co-occurrence matrix [GLCM], run-length matrix [RLM], and Gabor filters). Two independent radiologists were asked to identify the attributed (benign/malignant) classes of adnexal masses based on morphological features on conventional MRI.

Statistical tests: Leave-one-out cross-validated feature selection followed by cross-validated classification were applied to the feature space to choose the spatial models that best discriminate benign from malignant adnexal lesions. Two schemes of feature selection/classification were evaluated: 1) including all benign and malignant masses, and 2) scheme 1 after excluding endometrioma, hemorrhagic cysts, and teratoma (14 benign, 29 malignant masses). The constructed feature subspaces for benign/malignant lesion differentiation were tested for classification of benign/borderline/malignant and also borderline/malignant adnexal lesions.

Results: The selected feature subspace consisting of RLM features differentiated benign from malignant adnexal masses with a classification accuracy of ∼92%. The same model discriminated benign, borderline, and malignant lesions with 87% and borderline from malignant with 100% accuracy. Qualitative assessment of the radiologists based on conventional MRI features reached an accuracy of 80%.

Data conclusion: The spatial quantification methodology proposed in this study, which works based on cellular distributions within ADC maps of adnexal masses, may provide a helpful computer-aided strategy for objective characterization of adnexal masses.

  • Article_DOI : 10.1002/jmri.25854
  • Author(s) : hamidreza saligheh rad,anahita fathi kazerooni
  • News Group : research,research article
  • News Code : 278385
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