术中标本乳腺X线摄影术是乳腺癌手术中一个有价值的工具，能够立即评估切除肿瘤后的边缘情况。然而，标本乳腺X线摄影术在检测微小边缘阳性情况方面的准确性较低。我们希望开发一个人工智能模型，利用标本乳腺X线摄影术来预测切除的乳腺肿瘤的病理边缘状态。我们从2017年到2020年收集了一个包含了与病理边缘状态匹配的标本乳腺X线摄影图像的数据集。该数据集随机分为训练集、验证集和测试集。我们开发了在放射学图像上预训练和在非医学图像上预训练的标本乳腺X线摄影模型，并进行了比较。我们使用敏感性、特异性和受试者工作特征曲线下面积（AUROC）评估模型的性能。数据集包括821个图像，其中53%的图像具有阳性边缘。在测试了四种模型架构后，对于三种模型，预先在放射学图像上训练的模型的表现优于非医学模型。表现最好的模型，InceptionV3，其敏感性为84%，特异性为42%，AUROC为0.71。在侵袭性癌症、乳房密度较低和非白人种族的患者中，模型的表现更好。本研究开发并进行了内部验证，能够预测标本乳腺X线摄影术下部分切除的病理边缘状态的人工智能模型。这些模型的准确性可以与已发表的关于外科医生和放射科医生对标本乳腺X线摄影术的解读的文献进行比较。随着进一步的发展，这些模型可能更精确地指导切除的范围，从而改善美容效果并减少重新手术。© 2023. 外科肿瘤学会。

Intraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop an artificial intelligence model to predict the pathologic margin status of resected breast tumors using specimen mammography.A dataset of specimen mammography images matched with pathologic margin status was collected from our institution from 2017 to 2020. The dataset was randomly split into training, validation, and test sets. Specimen mammography models pretrained on radiologic images were developed and compared with models pretrained on nonmedical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).The dataset included 821 images, and 53% had positive margins. For three out of four model architectures tested, models pretrained on radiologic images outperformed nonmedical models. The highest performing model, InceptionV3, showed sensitivity of 84%, specificity of 42%, and AUROC of 0.71. Model performance was better among patients with invasive cancers, less dense breasts, and non-white race.This study developed and internally validated artificial intelligence models that predict pathologic margins status for partial mastectomy from specimen mammograms. The models' accuracy compares favorably with published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could more precisely guide the extent of resection, potentially improving cosmesis and reducing reoperations.© 2023. Society of Surgical Oncology.