Classifying malignant pulmonary nodules using biophysics-enhanced artificial intelligence

总结： 肺癌是美国癌症死亡的最常见原因，估计有14万人死亡 2020年虽然已经证明肺部筛查可将死亡率降低20%，但准确的分类 由于高假阳性率，恶性肿瘤的诊断仍然是未满足的需求。当前分类 借助于计算机断层摄影（CT）筛查的方法基于统计预测模型，并且 最近的人工智能。提高恶性肿瘤的分类准确率将降低成本 和死亡风险，并指导临床决策。在这里，我们提出了一个新的框架， 通过使用基于生物药理学的信息丰富输入信息，提高当前模型的预测能力 计算模型所提出的计算模型生成正交信息，其基于 物理定律，因此人工智能本质上无法学习。我们建议加强生物物理 因为许多研究已经证明，肿瘤进展受到肿瘤生长因子的强烈影响， 它们生长的物理微环境。 我们的总体目标是提出肺中的生理机械力， 正交生物标志物的现有输入变量的最先进的方法，以改善预测 肺结节的恶性风险。我们的中心假设是，将基于生物药理学的计算 模型与现有的统计模型和人工智能方法将提高其预测能力 恶性肺结节的分类。我们的假设是基于已发表的作品和我们的初步研究。 数据表明，肺中的机械应力与肿瘤发生率和生长密切相关。通过 耦合基于生物药理学的计算模型，我们计划评估改进的分类性能， 逻辑回归模型（Aim 1），作为一种高度可解释的模型，以及深度卷积神经网络（Aim 2）作为一个高度预测的模型。基于生物物理学的计算模型与人工智能的耦合 预测工具将在不增加患者的经济和健康负担的情况下改善功率的预测。这 本文提出的肺癌分类的新方法在诊断和预后方面具有潜在的益处， 其他病理性肺部疾病，例如慢性阻塞性肺病（COPD）、纤维化、机械性 通气以及肺部的细菌和病毒感染。

SUMMARY: Lung cancer is the most common cause of cancer death in the United States with an estimated 140,000 deaths in 2020. While it has been demonstrated that lung screening reduces the mortality by 20%, accurate classification of malignant tumors remains an unmet need due to high rate of false positive cases. Current classification approaches by means of computed tomography (CT) screenings are based on statistical predictive models, and more recently artificial intelligence. Improving the classification accuracy of malignant tumors will reduce costs and the risk of mortality and guide clinical decision making. Here, we propose a novel framework to further improve the predictive power of current models by enriching the input information with biophysics-based computational models. The proposed computational model generates orthogonal information, which is based on laws of physics, and hence intrinsically unlearnable by artificial intelligence. We propose augmenting biophysical information since numerous studies have demonstrated that the tumor progression is strongly affected by the physical microenvironment in which they grow. Our overall objective here is to propose the physiological mechanical forces in lung as an informative and orthogonal biomarker to the existing input variables in state-of-the-art approaches to improve the prediction of malignancy risk in pulmonary nodules. Our central hypothesis is that coupling a biophysics-based computational model with the existing statistical models and artificial intelligence approaches will improve their prediction power in classifying malignant pulmonary nodules. Our hypothesis is based on published works and our preliminary data that the mechanical stresses in the lung are strongly correlated with both tumor incidence and growth. By coupling biophysics-based computational model, we plan to evaluate the improved classification performance in logistic regression model (Aim 1), as a highly interpretable model, and deep convolutional neural network (Aim 2), as a highly predictive model. Coupling biophysics-based computational model to artificial intelligence predictive tools will improve the prediction of power at no added financial and health burden to the patient. This novel approach, proposed here on lung cancer classification, has potential diagnostic and prognostic benefits in other pathological lung conditions such as chronic obstructive pulmonary disease (COPD), fibrosis, mechanical ventilation, and bacterial and viral infection of the lung.