ADDED VALUE OF QUANTITATIVE ANALYSIS OF DWI/ADC IN OVARIAN ADNEXAL REPORTING AND DATA SYSTEM MRI

Background: Adnexal masses are among the most frequent gynecological findings, with ultrasound being the primary imaging tool for their initial evaluation. Although ultrasonography reliably classifies many adnexal lesions, up to 31% remain indeterminate, posing diagnostic and management challenges. Magnetic Resonance Imaging (MRI) with the Ovarian-Adnexal Reporting and Data System (O-RADS) has been introduced as a standardized approach to improve lesion characterization, reduce unnecessary interventions, and enhance patient outcomes. Objective: This study aimed to evaluate the diagnostic accuracy of MRI using the O-RADS classification system in characterizing adnexal masses indeterminate on ultrasonography, with histopathology serving as the gold standard. Methods: A prospective, observational study was conducted at the Armed Forces Institute of Radiology and Imaging (AFIRI), Rawalpindi, over six months. A total of 150 female patients aged 18–65 years with indeterminate adnexal masses based on IOTA Simple Rules or similar ultrasound scoring systems were enrolled using consecutive sampling. All patients underwent pelvic MRI with contrast, and lesions were categorized using the O-RADS MRI scoring system. Surgical excision followed by histopathological examination provided the definitive diagnosis. Statistical analysis was performed using SPSS version 21. Sensitivity, specificity, predictive values, and overall diagnostic accuracy were calculated, and Receiver Operating Characteristic (ROC) curve analysis was used to evaluate performance. Results: The mean age of participants was 42.6 ± 10.8 years, with 87 (58.0%) premenopausal and 63 (42.0%) postmenopausal. Pelvic pain was the most common presenting symptom, reported in 87 patients (58.0%). MRI categorized lesions as O-RADS 2 in 26 patients (17.3%), O-RADS 3 in 47 (31.3%), O-RADS 4 in 38 (25.3%), and O-RADS 5 in 39 (26.0%). Histopathology confirmed 42 malignant cases (28.0%) and 108 benign cases (72.0%). Using O-RADS ≥4 as the threshold for malignancy, MRI demonstrated a sensitivity of 88.1%, specificity of 63.0%, positive predictive value of 48.1%, negative predictive value of 93.2%, and an overall diagnostic accuracy of 71.3%. ROC analysis showed an area under the curve (AUC) of 0.86 (95% CI: 0.80–0.92). Conclusion: MRI with O-RADS scoring provided high sensitivity and reliability in excluding malignancy among sonographically indeterminate adnexal masses. Its application improved diagnostic confidence, minimized unnecessary surgical procedures, and guided more accurate clinical decision-making, making it a valuable adjunct to ultrasound in routine practice.

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Journal
Insights-Journal of Health and Rehabilitation
Published
2025-10-14
DOI
https://doi.org/10.71000/a68mfz74
Primary Topic
Smart Systems and Machine Learning
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article
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article

ADDED VALUE OF QUANTITATIVE ANALYSIS OF DWI/ADC IN OVARIAN ADNEXAL REPORTING AND DATA SYSTEM MRI

Ali Zamir Khan, Sarah Nathaniel, Ayesha Khan, Rabia Haq et al.
Insights-Journal of Health and Rehabilitation
Smart Systems and Machine Learning
article

ADDED VALUE OF QUANTITATIVE ANALYSIS OF DWI/ADC IN OVARIAN ADNEXAL REPORTING AND DATA SYSTEM MRI

Ali Zamir Khan, Sarah Nathaniel, Ayesha Khan, Rabia Haq, Rabia khan, Farid Ul Haq
article en

Abstract

Background: Adnexal masses are among the most frequent gynecological findings, with ultrasound being the primary imaging tool for their initial evaluation. Although ultrasonography reliably classifies many adnexal lesions, up to 31% remain indeterminate, posing diagnostic and management challenges. Magnetic Resonance Imaging (MRI) with the Ovarian-Adnexal Reporting and Data System (O-RADS) has been introduced as a standardized approach to improve lesion characterization, reduce unnecessary interventions, and enhance patient outcomes. Objective: This study aimed to evaluate the diagnostic accuracy of MRI using the O-RADS classification system in characterizing adnexal masses indeterminate on ultrasonography, with histopathology serving as the gold standard. Methods: A prospective, observational study was conducted at the Armed Forces Institute of Radiology and Imaging (AFIRI), Rawalpindi, over six months. A total of 150 female patients aged 18–65 years with indeterminate adnexal masses based on IOTA Simple Rules or similar ultrasound scoring systems were enrolled using consecutive sampling. All patients underwent pelvic MRI with contrast, and lesions were categorized using the O-RADS MRI scoring system. Surgical excision followed by histopathological examination provided the definitive diagnosis. Statistical analysis was performed using SPSS version 21. Sensitivity, specificity, predictive values, and overall diagnostic accuracy were calculated, and Receiver Operating Characteristic (ROC) curve analysis was used to evaluate performance. Results: The mean age of participants was 42.6 ± 10.8 years, with 87 (58.0%) premenopausal and 63 (42.0%) postmenopausal. Pelvic pain was the most common presenting symptom, reported in 87 patients (58.0%). MRI categorized lesions as O-RADS 2 in 26 patients (17.3%), O-RADS 3 in 47 (31.3%), O-RADS 4 in 38 (25.3%), and O-RADS 5 in 39 (26.0%). Histopathology confirmed 42 malignant cases (28.0%) and 108 benign cases (72.0%). Using O-RADS ≥4 as the threshold for malignancy, MRI demonstrated a sensitivity of 88.1%, specificity of 63.0%, positive predictive value of 48.1%, negative predictive value of 93.2%, and an overall diagnostic accuracy of 71.3%. ROC analysis showed an area under the curve (AUC) of 0.86 (95% CI: 0.80–0.92). Conclusion: MRI with O-RADS scoring provided high sensitivity and reliability in excluding malignancy among sonographically indeterminate adnexal masses. Its application improved diagnostic confidence, minimized unnecessary surgical procedures, and guided more accurate clinical decision-making, making it a valuable adjunct to ultrasound in routine practice.

Insights-Journal of Health and RehabilitationVol. 3(5 (Health and Rehabilitation))
Rawalpindi Medical University (PK), Fatima Jinnah Medical University (PK), Benazir Bhutto Hospital (PK), Dow University of Health Sciences (PK), Ghazi University (PK)
Openalex Percentile: Top 65%
Smart Systems and Machine Learning
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