Background With the expansion of the use of the neoadjuvant chemotherapy(NAC) in locally advanced breast cancer (LABC), both dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET CT) are promising methods for assessment of the tumor response during chemotherapy. We aimed to evaluate the diagnostic accuracy of DCE-MRI of breast &18 F-FDG PETCT regarding the assessment of early response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer patients (LABC) and pathologic complete response (pCR) prediction. Results A total of forty LABC patients who had NAC were included in the study. Before and during NAC, PET/CT and DCE-MRI were used. Various morphological and functional criteria were compared and linked with post-operative pathology for both. The MRI sensitivity and specificity in assessing NAC response in conjunction with pathological data were 100% (p = 0.001) and 12.5% (p = 0.18) respectively. The equivalent readings for PET/CT were 94.1% (p = 0.001) and 25% (p = 0.18), respectively, although the estimated total accuracy for both MRI and PETCT was the same measuring 94.1% (p = 0.001) and 25% (p = 0.18) (72%). PETCT had a higher overall accuracy than MRI in assessing the response of axillary lymph nodes (ALN) to NAC (64% and 56%, respectively). Longest diameter of lesion, ADC value, and maximal enhancement in baseline MRI, SUVmax and SUV mean in baseline PETCT were all significant predictors of rCR. Conclusion During NAC in the primary breast mass and ALN, DCE-MRI demonstrated a better sensitivity in predicting pCR in LABC patients. Although both MRI and PETCT were equally accurate in detecting pCR of LABC patients to NAC, PETCT was more accurate in detecting pathological response of ALN to NAC.
Background Breast asymmetries are prevalent findings in mammograms and are commonly caused by variations in normal breast tissue. However, they may imply significant underlying causes in some cases. Such cases necessitate further assessment by adding further mammography views, targeted ultrasound, and investigations to exclude underlying pathology. Objectives To investigate the role of artificial intelligence (AI) compared to contrast-enhanced spectral mammography (CESM) in the assessment of breast asymmetries and their performance as diagnostic modality among different types of breast asymmetries as well as the additive value of AI software to mammography in these cases. Methods Sixty-four female patients were diagnosed with breast asymmetries by standard mammography (MMG) on both craniocaudal (CC) and mediolateral oblique (MLO). Digital breast tomosynthesis (DBT) may have been added. After evaluating the breast asymmetry by MMG and complementary breast ultrasound (US), both CESM and AI were performed for all cases and analyzed, then the interpreted results were compared accordingly either by histopathology from suspected lesions scored as BI-RADS 4 or 5 or through further close follow-up by single-view mammography in benign cases scored as BI-RADS 2 or 3. Results The sensitivity and specificity of CESM in the assessment of breast asymmetry in correlation with pathological data/follow-up results were 100% and 60% (p < 0.001). The corresponding values for AI were 70.83% and 75%; however, the estimated overall accuracy for both CESM and AI was close to each other measuring 75% and 73.44%, respectively (p < 0.001). The diagnostic accuracy of CESM to detect malignant causes of breast asymmetry was 100%; however, the detection of benign causes of breast asymmetry was 40%. The corresponding values for AI were 70.83% and 25%, respectively, with significant p-value (p < 0.001). Conclusions The CESM was more sensitive; however, the AI was more specific in the assessment of different breast asymmetries. Although the diagnostic accuracy of both is close to each other. Therefore, AI-aided reading can replace CESM in most cases, especially for those contraindicated to do CESM. AI also can reduce the radiation exposure hazards of a second dose of radiation for CESM and its financial cost as well. AI-aided reading in breast screening programs can reduce the recall of patients, unnecessary biopsies, and short-interval follow-up exams.
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