PDF(Pubmed)
Abstract：
BACKGROUND: Artificial intelligence (AI)-based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy.
OBJECTIVE: Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy.
METHODS: This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling.
RESULTS: Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01).
CONCLUSIONS: Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist\'s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.
OBJECTIVE: Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy.
METHODS: This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling.
RESULTS: Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01).
CONCLUSIONS: Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist\'s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.
摘要：
背景：基于人工智能（AI）的用于乳房X线照相术的癌症探测器（CAD）开始用于放射科的乳腺癌筛查。重要的是要了解AICAD系统对良性病变的反应，尤其是那些接受过活检的.
目的：我们的目的是证实这样的假设，即先前进行过良性活检和细胞学评估的女性即使保持健康，随后也会呈现增加的AICAD异常评分。
方法：这是一项应用商业AICAD系统的回顾性研究(InsightMMG，版本1.1.4.3；LunitInc）到10,889名女性（中位年龄56，范围40-74岁）的癌症富集乳房X线摄影筛查数据集。AICAD在0.00和1.00之间产生肿瘤怀疑的连续预测评分，其中1.00代表最高怀疑水平。基于预定截止阈值(0.40)定义二进制读取(标记或未标记)。为健康的女性计算了AI评分的标记中位数和比例，那些有良性活检发现的人，和那些被诊断出患有乳腺癌的人。对于有良性活检发现的女性，乳房X线照相术和活检之间的时间间隔用于AI评分的分层.使用亚组分析和回归模型检查年龄增加的影响。
结果：共有10,889名妇女，234在筛查之前或之后有良性活检发现。被标记的健康女性的比例为3.5%，11%,84%的健康女性没有良性活检发现，那些有良性活检发现的人，和患有乳腺癌的女性，分别(P<.001)。对于8307名拥有完整信息的女性，放射科医生1、放射科医生2和AICAD系统标记为8.5%，6.8%,和8.5％的先前有良性活检发现的女性检查。AI评分仅与癌症组女性的年龄增长相关（P=0.01）。
结论：与没有活检的健康女性相比，所检查的AICAD系统显示，根据放射科医生的决定，有或将有良性活检发现的女性比例要大得多。然而,标记率不高于放射科医生。进一步的研究应集中在考虑先前活检信息的AICAD系统的培训上。
目的：我们的目的是证实这样的假设，即先前进行过良性活检和细胞学评估的女性即使保持健康，随后也会呈现增加的AICAD异常评分。
方法：这是一项应用商业AICAD系统的回顾性研究(InsightMMG，版本1.1.4.3；LunitInc）到10,889名女性（中位年龄56，范围40-74岁）的癌症富集乳房X线摄影筛查数据集。AICAD在0.00和1.00之间产生肿瘤怀疑的连续预测评分，其中1.00代表最高怀疑水平。基于预定截止阈值(0.40)定义二进制读取(标记或未标记)。为健康的女性计算了AI评分的标记中位数和比例，那些有良性活检发现的人，和那些被诊断出患有乳腺癌的人。对于有良性活检发现的女性，乳房X线照相术和活检之间的时间间隔用于AI评分的分层.使用亚组分析和回归模型检查年龄增加的影响。
结果：共有10,889名妇女，234在筛查之前或之后有良性活检发现。被标记的健康女性的比例为3.5%，11%,84%的健康女性没有良性活检发现，那些有良性活检发现的人，和患有乳腺癌的女性，分别(P<.001)。对于8307名拥有完整信息的女性，放射科医生1、放射科医生2和AICAD系统标记为8.5%，6.8%,和8.5％的先前有良性活检发现的女性检查。AI评分仅与癌症组女性的年龄增长相关（P=0.01）。
结论：与没有活检的健康女性相比，所检查的AICAD系统显示，根据放射科医生的决定，有或将有良性活检发现的女性比例要大得多。然而,标记率不高于放射科医生。进一步的研究应集中在考虑先前活检信息的AICAD系统的培训上。
