Comparative Evaluation Of Diffusion Weighted MRI And Ultrasonography In The Detection Of Breast Lesions
PDF

Keywords

Magnetic Resonance Imaging (MRI), Diffusion weighted imaging (DWI), Ultrasonography (USG)

Abstract

Objective: To compare the evaluation of diffusion weighted MRI and ultrasonography in the detection of breast lesions.

Study design: It was a Cross-Sectional Comparative Study.

Place and duration of study: The study was conducted at at Islamabad Diagnostic Center, Faisalabad from July 2024 to December 2024.

Material and Methods: A sample size of 97 patients presenting with breast lesions was selected, excluding those with previous breast implant. All patients underwent MRI using a 1.5 Tesla machine and statistical analysis was performed using SPSS version 25.

Results: This study included 97 patients with breast lesions to compare the diagnostic performance of ultrasonography (US) and diffusion-weighted MRI (DWI). Based on US, 39 patients (40.2%) were classified as BI-RADS 2, 32 (33.0%) as BI-RADS 3, 6 (6.2%) as BI-RADS 4, 8 (8.2%) as BI-RADS 5, and 12 (12.4%) as BI-RADS 6. Among 33 malignant lesions, MRI accurately detected 31, yielding a sensitivity of 93.9%. MRI correctly identified 9 out of 10 malignant lesions in BI-RADS 3 (90.0% accuracy), 3 out of 4 in BI-RADS 4 (75.0% accuracy), and all malignant cases in BI-RADS 5 and 6 (100% accuracy). DWI detected 100% of vascular lesions as hyperintense and 91% of non-vascular lesions as hypointense, demonstrating high specificity for malignancy. MRI outperformed US in identifying malignancies, particularly for vascular lesions, whereas US was more accurate for benign lesion detection. These findings reinforce the complementary role of both modalities in breast lesion characterization.

https://doi.org/10.37939/jnah.v3i01.115
PDF

References

Gupta M, Goyal N. Applied anatomy of breast cancer. InBreast Cancer: Comprehensive Management 2022 Jan 31 (pp. 23-35). Singapore: Springer Nature Singapore.

Mareti E, Vatopoulou A, Spyropoulou GA, Papanastasiou A, Pratilas GC, Liberis A, Hatzipantelis E, Dinas K. Breast disorders in adolescence: a review of the literature. Breast Care. 2021 Apr 19;16(2):149-55.

Sosnowska-Sienkiewicz P, Januszkiewicz-Lewandowska D, Mańkowski P. Benign and malignant breast lesions in children and adolescents-diagnostic and therapeutic approach. Frontiers in Pediatrics. 2024 Oct 23;12:1417050.

Dong S, Wang Z, Shen K, Chen X. Metabolic syndrome and breast cancer: prevalence, treatment response, and prognosis. Frontiers in oncology. 2021 Mar 25;11:629666.

Niu S, Huang J, Li J, Liu X, Wang D, Zhang R, Wang Y, Shen H, Qi M, Xiao Y, Guan M. Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A. BMC cancer. 2020 Dec;20:1-7.

Gnanasekaran VS, Joypaul S, Meenakshi Sundaram P, Chairman DD. Deep learning algorithm for breast masses classification in mammograms. IET Image Processing. 2020 Oct;14(12):2860-8.

Chi X, Zhang L, Xing D, Gong P, Chen Q, Lv Y. Diagnostic value of the enhancement intensity and enhancement pattern of CESM to benign and malignant breast lesions. Medicine. 2020 Sep 11;99(37):e22097.

Lin F, Wang Z, Zhang K, Yang P, Ma H, Shi Y, Liu M, Wang Q, Cui J, Mao N, Xie H. Contrast-enhanced spectral mammography-based radiomics nomogram for identifying benign and malignant breast lesions of sub-1 cm. Frontiers in Oncology. 2020 Oct 30;10:573630.

Hu Q, Whitney HM, Li H, Ji Y, Liu P, Giger ML. Improved classification of benign and malignant breast lesions using deep feature maximum intensity projection MRI in breast cancer diagnosis using dynamic contrast-enhanced MRI. Radiology: Artificial Intelligence. 2021 Feb 24;3(3):e200159.

Ma W, Mao J, Wang T, Huang Y, Zhao ZH. Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis. European journal of radiology. 2021 Aug 1;141:109809.

Messina C, Bignone R, Bruno A, Bruno A, Bruno F, Calandri M, Caruso D, Coppolino P, De Robertis R, Gentili F, Grazzini I. Diffusion-weighted imaging in oncology: an update. Cancers. 2020 Jun 8;12(6):1493.

Ha SM, Chang JM, Lee SH, Kim ES, Kim SY, Kim YS, Cho N, Moon WK. Detection of contralateral breast cancer using diffusion-weighted magnetic resonance imaging in women with newly diagnosed breast cancer: comparison with combined mammography and whole-breast ultrasound. Korean Journal of Radiology. 2021 Jun;22(6):867.

Lee SH, Shin HJ, Moon WK. Diffusion-weighted magnetic resonance imaging of the breast: standardization of image acquisition and interpretation. Korean journal of radiology. 2020 Aug 28;22(1):9.

Hetta, W. (2015). Role of diffusion weighted images combined with breast MRI in improving the detection and differentiation of breast lesions. The Egyptian Journal of Radiology and Nuclear Medicine, 46(1), 259-270.

Gouda W, Yasin R, Yasin MI, Omar S. Automated breast ultrasound in breast cancer screening of mammographically dense breasts: added values. Egyptian Journal of Radiology and Nuclear Medicine. 2024;55(1):86.

Azhdeh S, Kaviani A, Sadighi N, Rahmani M. Accurate estimation of breast tumor size: a comparison between ultrasonography, mammography, magnetic resonance imaging, and associated contributing factors. European Journal of Breast Health. 2020 Dec 24; 17(1):53

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2025 Journal of Nursing and Allied Health