Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden.
Eur Radiol. 2022 May;32(5):3131-3141. doi: 10.1007/s00330-021-08306-w. Epub 2021 Oct 15.
In this proof of concept study, a deep learning-based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined.
Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients.
A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7-62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53-0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%.
Our study describes an AI platform using baseline DM to predict BC patients' responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted.
• We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.
在这项概念验证研究中，我们检验了一种基于深度学习的方法，用于自动分析数字乳腺 X 线摄影（DM），作为辅助评估乳腺癌（BC）新辅助化疗（NACT）治疗反应的工具。
本研究纳入了 2005 年至 2019 年间接受 NACT 治疗的 453 例患者的基线 DM。使用上述基线 DM 开发了一种深度学习系统，以预测 NACT 完成后手术标本的病理完全缓解（pCR）。将一个图像补丁，一个围绕检测到的肿瘤提取，另一个从参考图像的相应位置提取，输入到分类网络中。为了训练和验证，我们使用了来自 400 例患者的 1485 张图像，最终将模型应用于由 53 例患者组成的测试集。
共有 95 例（21%）患者达到 pCR。患者的中位年龄为 52.5 岁（四分位距 43.7-62.1），255 例（56%）为绝经前。人工智能（AI）模型预测的 pCR 曲线下面积为 0.71（95%置信区间 0.53-0.90；p=0.035）。固定特异性为 90%时，敏感性为 46%。
我们的研究描述了一种使用基线 DM 预测 BC 患者对 NACT 反应的 AI 平台。初步 AI 性能表明，它具有辅助临床决策的潜力。为了继续探索 AI 在预测 BC 患者对 NACT 反应中的临床应用价值，需要进一步研究，包括改进方法和增加样本量。
我们旨在回答以下问题：在开始新辅助化疗之前，人工智能（AI）能否应用于数字乳腺 X 线摄影（DM）来预测乳腺癌肿瘤的反应？
DM 包含 AI 可以利用的信息，用于预测乳腺癌新辅助化疗后的病理完全缓解（pCR）反应。
通过开发一种专注于 DM 相关部分的 AI 系统，可以很好地实现自动 pCR 预测，从而有可能辅助临床决策。
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