UNASSIGNED: Mammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT).
UNASSIGNED: We used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles.
UNASSIGNED: The average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40-49, 50-59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40-49, 50-59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40-59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36.
UNASSIGNED: The study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40-59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.

OBJECTIVE: This study aimed to explore the positive rate of non-visualized sentinel lymph nodes (non-vSLN) [1] in breast cancer (BC) patients and the discrepancy of non-vSLN among different molecular subtypes, in order to further evaluate the clinical risk of non-vSLNs.
METHODS: A total of 627 patients were retrospectively analyzed. These patients were pathologically confirmed with invasive breast cancer and underwent sentinel lymph node biopsy (SLNB). Various factors were compared using chi-square test. The positive rate of SLNs between non-vSLNs and visible sentinel lymph nodes (vSLNs) were compared. Moreover, factors that influenced the prognosis, such as ER, PR, HER-2, histological grade and lymph node metastasis were compared between these two groups.
RESULTS: Among the 627 patients who underwent SLNB, 196 patients had non-vSLNs, accounting for 31.26% (196/627) and 113 patients had positive SLNs, accounting for 18.02% (113/627). Furthermore, 40.71% (46/113) of patients with positive SLNs had non-vSLN, and 17.39% (8/46) of patients with non-vSLN had HER-2+BC. In contrast, 35.82% (24/67) of patients with vSLNs had HER-2+BC. Moreover, 23.91% (11/46) of patients with non-vSLN and 5.97% (4/67) of patients with vSLNs had triple-negative breast cancer (TNBC). The metastasis rate was 41.30% (19/46) in the non-vSLN group and 43.28% (29/67) in the vSLN group. The difference in the rate of positive SLNs between the non-vSLN and the vSLN groups was statistically significant (P< 0.05), in which the positive rate of SLNs in the non-vSLN group was remarkably higher than that in the vSLN group. The differences in the proportion of HER-2+BC and TNBC between the non-vSLN and the vSLN groups were statistically significant (P< 0.05), in which HER-2+BCwas evidently higher in the vSLN group than in the non-vSLN group. Meanwhile, TNBC was markedly higher in non-vSLN group than in the vSLN group. Furthermore, differences in Luminal A subtype, Luminal B subtype and non-SLN metastasis between these two groups was not statistically significant. In addition, the difference in non-SLN metastasis rate was not statistically significant among breast cancers of different molecular subtypes and between the non-vSLN and the vSLN groups.
CONCLUSIONS: Breast cancer patients with positive non-vSLNs are more likely to have a TNBC subtype relative to patients with positive vSLN. Breast cancer patients with non-vSLN have higher positive rate of SLNs. The non-SLN metastasis rate in positive SLN patients was not correlated to the molecular subtype of breast cancer.