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.

翻译：我们提出，深度学习可通过分析患者的人口统计学和人体测量学信息、常规血液样本中的生物标志物以及来自荟萃分析和国际数据库的相对风险，用于癌症预筛查。我们对包含116名女性（其中52名健康女性，64名确诊乳腺癌患者）的数据库应用特征选择算法，以识别最佳的癌症预筛查预测因子。利用这些最优预测因子，我们进行了K折蒙特卡洛交叉验证实验，将深度学习与传统机器学习算法进行对比。结果表明，采用特征选择微调输入层架构的深度学习模型能有效区分癌症患者与非癌症患者。此外，与传统机器学习相比，深度学习的预测不确定性最低。这些发现表明，将深度学习算法应用于癌症预筛查，可为基于影像的筛查方法提供一种无辐射、非侵入性且经济实惠的补充方案。在癌症预筛查中部署深度学习算法，有助于识别需要影像学筛查的个体，促进自我检查，并降低癌症筛查中假阳性结果引发的心理外部性。将深度学习算法整合用于筛查和预筛查，最终将实现恶性肿瘤的早期发现，减轻癌症治疗带来的医疗体系与社会负担。