Background: Nipple discharge is the third most frequent complaint of women attending rapid diagnostic breast clinics. Nipple smear cytology remains the single most used diagnostic method for investigating fluid content. This study aimed to conduct a systematic review and meta-analysis of the diagnostic accuracy of nipple discharge fluid assessment.
Methods: The study incorporated searches for studies interrogating the diagnostic data of nipple discharge fluid cytology compared with the histopathology gold standard. Data from studies published from 1956 to 2019 were analyzed. The analysis included 8648 cytology samples of women with a presenting complaint of nipple discharge. Both hierarchical and bivariate models for diagnostic meta-analysis were used to attain overall pooled sensitivity and specificity.
Results: Of 837 studies retrieved, 45 fulfilled the criteria for inclusion. The diagnostic accuracy of the meta-analysis examining nipple discharge fluid had a sensitivity of 75 % (95 % confidence interval [CI], 0.74-0.77) and a specificity of 87 % (95 % CI, 0.86-0.87) for benign breast disease. For breast cancer, it had a sensitivity of 62 % (95 % CI, 0.53-0.71) and a specificity 71 % (95 % CI, 0.57-0.81). Furthermore, patients presenting with blood-stained discharge yielded an overall malignancy rate of 58 % (95 % CI, 0.54-0.60) with a positive predictive value (PPV) of 27 % (95 % CI, 0.17-0.36).
Conclusions: Pooled data from studies encompassing nipple discharge fluid assessment suggest that nipple smear cytology is of limited diagnostic accuracy. The authors recommend that a tailored approach to diagnosis be required given the variable sensitivities of currently available tests.
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http://dx.doi.org/10.1245/s10434-021-11070-2 | DOI Listing |
Quant Imaging Med Surg
December 2024
Department of Radiology, Shenzhen People's Hospital, Shenzhen, China.
Background: The classification of Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions in mammography is complicated by subjective interpretations and unclear criteria, which can lead to potential misclassifications and unnecessary biopsies. Thus, more accurate assessment methods need to be developed. This study aimed to improve the classification prediction of BI-RADS 4A positive lesions in mammography by combining deep learning (DL) technology with relevant clinical factors.
View Article and Find Full Text PDFAm J Dermatopathol
November 2024
Department of Pathology and ARUP Laboratories, University of Utah, Salt Lake City, UT.
J Clin Ultrasound
December 2024
Department of Pathology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, China.
Giant cell tumor of soft tissue (GCT-ST) is an extremely rare phenomenon in the breast. Herein, a case involving a 75-year-old female with a painless lump and bloody discharge from the nipple of her left breast is reported. A diagnosis of malignant tumor was arrived at by observing the location of the tumor, interior echo, margins, vascular distribution, hardness, and microvascular density on preoperative multimodal ultrasonography.
View Article and Find Full Text PDFPak J Med Sci
November 2024
Chunzhi Li, Department of Radiology, China Academy of Chinese Medical Sciences, Xiyuan Hospital, Beijing 100091, China.
Front Oncol
October 2024
Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
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