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Background: Differentiating benign and malignant thyroid nodules is important for treatment planning and prognostic, yet an ideal method is lacking.
Purpose: To investigate whether microstructural parameters from time-dependent diffusion MRI (td-dMRI) can accurately distinguish between benign and malignant thyroid nodules.
Study Type: Single-center, prospective.
Population: 232 participants (median age, 48.0 years; IQR, 34.0-53.0) with pathologically diagnosed thyroid nodules.
Field Strength/sequence: 3.0 T td-dMRI with two oscillating gradient spin-echo sequences and one pulsed gradient spin-echo sequence.
Assessment: Histopathology was the reference standard for benign and malignant nodules identification. Clinical factors (sex, age, lesion location, and tumor size) and td-dMRI-derived parameters (cell diameter, cellularity, intracellular volume fraction, extracellular diffusivity, and ADCs) were analyzed via univariate and multivariate regression. Chinese Thyroid Imaging Reporting and Data System (TI-RADS) model and two combined models integrating significant clinical and td-dMRI features with and without TI-RADS were developed and compared.
Statistical Tests: Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare model AUCs. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) tests were employed for performance comparison. A p value < 0.05 was considered significant.
Results: Tumor size (maximum diameter) (odds ratio [OR], 0.309) and cellularity (OR, 2.430) from td-dMRI were independently associated with malignant thyroid nodules. The AUC of the combined model based on the two features was not significantly different to that of TI-RADS alone (0.847 vs. 0.891, p = 0.169). The combined model integrating tumor maximum diameter, cellularity, and TI-RADS significantly improved diagnostic accuracy compared to TI-RADS alone (AUC: 0.941 vs. 0.891; IDI = 0.134 [95% confidence interval (CI), 0.085-0.183]; NRI = 0.762 [95% CI, 0.522-0.979]).
Data Conclusion: The combined model integrating tumor maximum diameter, cellularity based on td-dMRI, and TI-RADS has the potential to differentiate between benign thyroid nodules and PTC.
Evidence Level: 1.
Technical Efficacy: Stage 2.
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http://dx.doi.org/10.1002/jmri.70065 | DOI Listing |
J Pathol Transl Med
September 2025
Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo, Egypt.
Background: Prostate cancer is one of the most common malignancies in males worldwide. Serum prostate-specific antigen is a frequently employed biomarker in the diagnosis and risk stratification of prostate cancer; however, it is known for its low predictive accuracy for disease progression. New prognostic biomarkers are needed to distinguish aggressive prostate cancer from low-risk disease.
View Article and Find Full Text PDFCurr Opin Endocrinol Diabetes Obes
October 2025
Department of Surgery, American Mission Hospital, Manama, Bahrain.
Purpose Of Review: To review the current medical evidence in the diagnosis and management of thyroid nodules.
Recent Findings: The widespread use of imaging modalities in recent years has led to frequent discovery of incidental thyroid nodules. These nodules are mostly benign (over 90%), hence precise insight in evaluating nodules of concern and following up other nodules is important to avoid unnecessary surgeries and its complications.
Ann Med
December 2025
Department of Gynecology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China.
Objective: To evaluate preoperative serum calcium levels and their association with deep infiltrating endometriosis (DIE) in ovarian endometrioma.
Design: A retrospective, observational cohort study.
Participants: A total of 2,557 women who underwent surgery for benign ovarian tumors were initially enrolled.
Ultrasound Obstet Gynecol
September 2025
Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, Milan, Italy.
Acad Radiol
September 2025
Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey (E.E.).
Purpose: This study aimed to evaluate the performance of ChatGPT (GPT-4o) in interpreting free-text breast magnetic resonance imaging (MRI) reports by assigning BI-RADS categories and recommending appropriate clinical management steps in the absence of explicitly stated BI-RADS classifications.
Methods: In this retrospective, single-center study, a total of 352 documented full-text breast MRI reports of at least one identifiable breast lesion with descriptive imaging findings between January 2024 and June 2025 were included in the study. Incomplete reports due to technical limitations, reports describing only normal findings, and MRI examinations performed at external institutions were excluded from the study.