Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions

Background: Ovarian cancer is a common female malignancy frequently identified at advanced stages. Diffusion-weighted imaging (DWI) provides valuable information on structural traits of tissue and is used as an imaging biomarker in OST cancer prognosis. Post-processing of three-dimensional apparent...

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Main Authors: Ali Reza Eftekhari Moghadam, Milad Jalilian, Forouzan Absalan, Farhad Heidari
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2025-05-01
Series:Advanced Biomedical Research
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Online Access:https://journals.lww.com/10.4103/abr.abr_138_24
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author Ali Reza Eftekhari Moghadam
Milad Jalilian
Forouzan Absalan
Farhad Heidari
author_facet Ali Reza Eftekhari Moghadam
Milad Jalilian
Forouzan Absalan
Farhad Heidari
author_sort Ali Reza Eftekhari Moghadam
collection DOAJ
description Background: Ovarian cancer is a common female malignancy frequently identified at advanced stages. Diffusion-weighted imaging (DWI) provides valuable information on structural traits of tissue and is used as an imaging biomarker in OST cancer prognosis. Post-processing of three-dimensional apparent diffusion coefficient (ADC) maps has proven useful in evaluating variable tumors, although its position in ovarian cancer prognosis is until now not well defined. Consequently, our foremost objective was to assess the sensitivity and efficiency of DWI (T1 and T2) and ADC maps in malignant and benign ovarian lesions prognosis. Matherials and Methods: A total of 58 patients with undetermined ovarian masses in ultrasound were referred to MRI for more accurate diagnosis. The signals of DWI (qualitative) and ADC values (quantitative DWI) of the lesion components were analyzed separately. Student’s t-test and receiver operating characteristic (ROC) curves were used to determine the ability of DWI and ADC in the discrimination between malignant and benign ovarian masses. Results: Of the 58 masses, 33 have been benign, and 25 have been malignant. There was a decrease correlation between signal thing on T2W and ADC values in malignant as compared to benign masses. The DWI and T1 + GAD values in malignant tumors have been substantially higher than the ones in benign masses (P value < 0.0001). Additionally, our consequences suggest that a T1 cutoff value (1 × 10⁻≥ mm²/s) would possibly the quality factor to help discriminate between benign and malignant lesions. Conclusions: The mixture of DWI imaging with T1 + GAD values can beautify the diagnostic overall performance in discrimination among benign and malignant ovarian masses by increasing specificity.
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spelling doaj-art-92cb8830d3a94e5eb8e06a14f59b4e5a2025-08-20T03:07:35ZengWolters Kluwer Medknow PublicationsAdvanced Biomedical Research2277-91752025-05-01141505010.4103/abr.abr_138_24Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian LesionsAli Reza Eftekhari MoghadamMilad JalilianForouzan AbsalanFarhad HeidariBackground: Ovarian cancer is a common female malignancy frequently identified at advanced stages. Diffusion-weighted imaging (DWI) provides valuable information on structural traits of tissue and is used as an imaging biomarker in OST cancer prognosis. Post-processing of three-dimensional apparent diffusion coefficient (ADC) maps has proven useful in evaluating variable tumors, although its position in ovarian cancer prognosis is until now not well defined. Consequently, our foremost objective was to assess the sensitivity and efficiency of DWI (T1 and T2) and ADC maps in malignant and benign ovarian lesions prognosis. Matherials and Methods: A total of 58 patients with undetermined ovarian masses in ultrasound were referred to MRI for more accurate diagnosis. The signals of DWI (qualitative) and ADC values (quantitative DWI) of the lesion components were analyzed separately. Student’s t-test and receiver operating characteristic (ROC) curves were used to determine the ability of DWI and ADC in the discrimination between malignant and benign ovarian masses. Results: Of the 58 masses, 33 have been benign, and 25 have been malignant. There was a decrease correlation between signal thing on T2W and ADC values in malignant as compared to benign masses. The DWI and T1 + GAD values in malignant tumors have been substantially higher than the ones in benign masses (P value < 0.0001). Additionally, our consequences suggest that a T1 cutoff value (1 × 10⁻≥ mm²/s) would possibly the quality factor to help discriminate between benign and malignant lesions. Conclusions: The mixture of DWI imaging with T1 + GAD values can beautify the diagnostic overall performance in discrimination among benign and malignant ovarian masses by increasing specificity.https://journals.lww.com/10.4103/abr.abr_138_24diagnostic imagingdiffusion magnetic resonance imagingovarian cancer
spellingShingle Ali Reza Eftekhari Moghadam
Milad Jalilian
Forouzan Absalan
Farhad Heidari
Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
Advanced Biomedical Research
diagnostic imaging
diffusion magnetic resonance imaging
ovarian cancer
title Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
title_full Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
title_fullStr Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
title_full_unstemmed Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
title_short Diffusion-Weighted Imaging-Based Differentiating between Benign and Malignant Ovarian Lesions
title_sort diffusion weighted imaging based differentiating between benign and malignant ovarian lesions
topic diagnostic imaging
diffusion magnetic resonance imaging
ovarian cancer
url https://journals.lww.com/10.4103/abr.abr_138_24
work_keys_str_mv AT alirezaeftekharimoghadam diffusionweightedimagingbaseddifferentiatingbetweenbenignandmalignantovarianlesions
AT miladjalilian diffusionweightedimagingbaseddifferentiatingbetweenbenignandmalignantovarianlesions
AT forouzanabsalan diffusionweightedimagingbaseddifferentiatingbetweenbenignandmalignantovarianlesions
AT farhadheidari diffusionweightedimagingbaseddifferentiatingbetweenbenignandmalignantovarianlesions