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Automated Breast Ultrasound for Evaluating Response to Neoadjuvant Therapy: A Comparison with Magnetic Resonance Imaging.

Authors :
Telegrafo, Michele
Stucci, Stefania Luigia
Gurrado, Angela
Catacchio, Claudia
Cofone, Federico
Maruccia, Michele
Stabile Ianora, Amato Antonio
Moschetta, Marco
Source :
Journal of Personalized Medicine; Sep2024, Vol. 14 Issue 9, p930, 9p
Publication Year :
2024

Abstract

Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total of 52 consecutive patients were included in this study. ABUS and MRI sensitivity (SE), specificity (SP), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV) were calculated and represented using Area Under ROC Curve (ROC) analysis, searching for any significant difference (p < 0.05). The McNemar test was used searching for any significant difference in terms of sensitivity by comparing the ABUS and MRI results. The inter-observer agreement between the readers in evaluating the response to NAC for both MRI and ABUS was calculated using Cohen's kappa k coefficient. Results: A total of 35 cases of complete response and 17 cases of persistent disease were found. MRI showed SE, SP, DA, PPV, and NPV values of 100%, 88%, 92%, 81%, and 100%, respectively, with an AUC value of 0.943 (p < 0.0001). ABUS showed SE, SP, DA, PPV, and NPV values of 88%, 94%, 92%, 89%, and 94%, respectively, with an AUC of 0.913 (p < 0.0001). The McNemar test revealed no significant difference (p = 0.1250). The inter-observer agreement between the two readers in evaluating the response to NAC for MRI and ABUS was, respectively, 0.88 and 0.89. Conclusions: Automatic breast ultrasound represents a new accurate, tri-dimensional and operator-independent tool for evaluating patients referred to NAC. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754426
Volume :
14
Issue :
9
Database :
Complementary Index
Journal :
Journal of Personalized Medicine
Publication Type :
Academic Journal
Accession number :
180009857
Full Text :
https://doi.org/10.3390/jpm14090930