使用机器学习和深度学习进行乳腺癌预筛查
Pre-screening breast cancer with machine learning and deep learning
摘要：
我们认为，深度学习可以通过分析患者的人口统计学和人体测量学信息，以及从常规血液样本中获得的生物标志物和从荟萃分析和国际数据库中获得的相对风险来进行癌症的预筛选。我们将特征选择算法应用于116名女性的数据库，其中包括52名健康女性和64名诊断为乳腺癌的女性，以确定癌症的最佳预筛选预测因子。我们利用最佳预测器来执行k倍蒙特卡罗交叉验证实验，将深度学习与传统的机器学习算法进行比较。我们的研究结果表明，具有使用特征选择微调的输入层架构的深度学习模型可以有效区分患有癌症和没有癌症的患者。此外，与机器学习相比，深度学习的预测不确定性最低。这些研究结果表明，应用于癌症预筛查的深度学习算法为基于图像的筛查方法提供了无辐射，非侵入性和负担得起的补充。深度学习算法在癌症预筛查中的应用为识别可能需要基于成像的筛查的个体提供了机会，可以鼓励自我检查，并减少与癌症筛查中的假阳性相关的心理外部性。深度学习算法在筛查和预筛查中的整合将最终导致恶性肿瘤的早期检测，减少与癌症治疗相关的医疗和社会负担。
英文摘要：
We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from meta-analysis and international databases. We applied feature selection algorithms to a database of 116 women, including 52 healthy women and 64 women diagnosed with breast cancer, to identify the best pre-screening predictors of cancer. We utilized the best predictors to perform k-fold Monte Carlo cross-validation experiments that compare deep learning against traditional machine learning algorithms. Our results indicate that a deep learning model with an input-layer architecture that is fine-tuned using feature selection can effectively distinguish between patients with and without cancer. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions. These findings suggest that deep learning algorithms applied to cancer pre-screening offer a radiation-free, non-invasive, and affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer pre-screening offer opportunities to identify individuals who may require imaging-based screening, can encourage self-examination, and decrease the psychological externalities associated with false positives in cancer screening. The integration of deep learning algorithms for both screening and pre-screening will ultimately lead to earlier detection of malignancy, reducing the healthcare and societal burden associated to cancer treatment.
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