PDF(Pubmed)
Abstract：
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.
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.
摘要：
■乳房X线照相术是乳腺癌筛查的首选方式。然而,一些乳腺癌病例仅通过超声检查确诊,在乳房X线照相术上没有发现或出现良性发现(在此称为不可视).因此,这项研究旨在根据使用人工智能(AI)根据患者年龄和压缩乳房厚度(CBT)估算的乳腺含量比,确定表明存在不可视可能性的因素.
■我们使用我们先前开发的AI来估算乳腺含量比，并对26,232个对照和150个不可视的进行定量分析。首先,我们根据平均估计的乳腺含量比评估了对照组和非可见者之间的差异趋势,以确保按年龄和CBT进行分析的重要性.接下来,我们评估了乳腺含量比率≥50%组会影响对照组和非可见组之间差异的可能性,以明确确定表明非可见组可能性的因素.对于估计的乳腺含量比率，将图像分为两组，阈值为50％，在对照组和非可见者之间进行逻辑回归分析。
■当整体样本时，非可见者的平均估计乳腺含量比率显着高于对照组，患者年龄≥40岁,CBT≥40mm(p<0.05).总体样本的对照和不可见的平均估计乳腺含量比率的差异为7.54％，40-49岁、50-59岁和≥60岁患者的差异为6.20%，7.48%,4.78%，分别,CBT为40-49、50-59和≥60mm的差异为6.67%，9.71%,和16.13%，分别。在评估乳腺含量比率≥50%组时，我们还发现，当对照被用作总体样本的基线时，非可见性呈正相关，在40-59岁的患者中，以及CBT≥40mm的患者（p<0.05）。相应的比值比≥2.20，最大值为4.36。
■研究结果强调，年龄在40-59岁的患者或CBT≥40mm的患者中，乳腺含量的估计比例≥50％可能是不可视的指示因素。
■我们使用我们先前开发的AI来估算乳腺含量比，并对26,232个对照和150个不可视的进行定量分析。首先,我们根据平均估计的乳腺含量比评估了对照组和非可见者之间的差异趋势,以确保按年龄和CBT进行分析的重要性.接下来,我们评估了乳腺含量比率≥50%组会影响对照组和非可见组之间差异的可能性,以明确确定表明非可见组可能性的因素.对于估计的乳腺含量比率，将图像分为两组，阈值为50％，在对照组和非可见者之间进行逻辑回归分析。
■当整体样本时，非可见者的平均估计乳腺含量比率显着高于对照组，患者年龄≥40岁,CBT≥40mm(p<0.05).总体样本的对照和不可见的平均估计乳腺含量比率的差异为7.54％，40-49岁、50-59岁和≥60岁患者的差异为6.20%，7.48%,4.78%，分别,CBT为40-49、50-59和≥60mm的差异为6.67%，9.71%,和16.13%，分别。在评估乳腺含量比率≥50%组时，我们还发现，当对照被用作总体样本的基线时，非可见性呈正相关，在40-59岁的患者中，以及CBT≥40mm的患者（p<0.05）。相应的比值比≥2.20，最大值为4.36。
■研究结果强调，年龄在40-59岁的患者或CBT≥40mm的患者中，乳腺含量的估计比例≥50％可能是不可视的指示因素。
