Amir Tali, Coffey Kristen, Reiner Jeffrey S, Sevilimedu Varadan, Mango Victoria L
Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ultrasonography. 2025 Mar;44(2):145-152. doi: 10.14366/usg.24206. Epub 2025 Jan 21.
This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results.
This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019-December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results.
The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%).
When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.
本研究旨在评估我院将人工智能（AI）决策支持（DS）作为临床工作流程的一部分，对在2019年冠状病毒病（COVID-19）大流行期间随访延迟的乳腺影像报告和数据系统（BI-RADS）3类超声病变患者进行分流，并与后续影像和/或病理随访结果进行对比的经验。
这项回顾性研究纳入了2019年8月至2019年12月间BI-RADS分类为3类（即可能为良性）的乳腺超声评估患者，这些患者在COVID-19大流行期间随访延迟，并且其乳腺超声作为这些患者分流临床工作流程的一部分，使用Koios DS乳腺AI进行重新评估。将Koios DS的输出结果与通过至少2年影像随访的消退/稳定情况或病理结果定义的乳腺癌存在与否的真实结果进行比较。
该研究纳入了161名女性（平均年龄52岁），共有221个BI-RADS 3类超声病变。在这221个病变中，有2个确诊癌症（恶性率0.9%）。Koios DS将112/221个病变（50.7%）评估为良性，42/221个病变（19.0%）评估为可能良性，64/221个病变（29.0%）评估为可疑，3/221个病变（1.4%）评估为可能恶性。Koios DS的敏感性为100%（2/2；95%置信区间[CI]，16%至100%），特异性为70%（154/219；95%CI，64%至76%），阴性预测值为100%（154/154；95%CI，98%至100%），假阳性率为30%（65/219；95%CI，24%至36%）。
当许多随访预约延迟时，例如自然灾害或资源有限的情况下，乳腺超声AI DS有助于对可能为良性乳腺超声的患者进行分流。
