Deep learning in cervical cancer radiogenomics

项目总结/摘要 该提案的总体目标是优化放射组学和基因组数据的使用，以开发生物标志物 它可以做出临床预测，从而改变癌症患者的管理。虽然这种预测的必要性 生物标志物在癌症类型中是显而易见的，我们将我们的建议集中在特别普遍和破坏性的 复发性局部晚期宫颈癌（LACC）。子宫颈癌仍然是第三大 女性常见的癌症诊断和局部晚期疾病的治疗失败率为30-50%， 放化疗迫切需要识别有治疗失败风险的患者， 个性化治疗，包括改良的放化疗方案，早期升级治疗， 试验入组。为了开发LACC复发的放射基因组学生物标志物，该建议涉及三个方面： 突出的方法学需求：癌症亚型基因表达数据的有限可用性，噪声和 冗余的成像特征数据，以及缺乏疾病信息，可解释的组学整合， 针对自己的具体目标。Aim 1将使用生成对抗网络（GAN）来增强小型 所有高危HPV亚型的基因表达数据集。Aim 2将使用 深度卷积自动编码器（CAE）。AIM 3将通过一个结构化的 方程建模（SEM）方法，将HPV特异性致癌机制作为潜在变量。 总之，我们期望这些目标的实现将创造一个优化的复发生物标志物，它将- 执行其他预测模式以及标准护理随访成像。超越具体 应用于HPV驱动的恶性肿瘤，我们的建议将产生新的工具和方法，以整合任何 高维度放射基因组学数据与假设驱动的研究结果，以提高癌症预测。

PROJECT SUMMARY/ABSTRACT The overall goal of this proposal is to optimize the use of radiomic and genomic data to develop biomarkers which make clinical predictions that change cancer patient management. While the need for such predictive biomarkers is evident across cancer types, we focus our proposal on the particularly prevalent and damaging condition of recurrent, locally-advanced cervical cancer (LACC). Cervical cancer remains the third most common cancer diagnosis of women, and treatment failure for locally-advanced disease is 30-50% following chemoradiation therapy. There is a pressing need to identify patients at risk for treatment failure to allow for personalized treatment including modified chemoradiation regimens, early escalation of therapy, and clinical trial enrollment. To develop radiogenomic biomarkers for LACC recurrence, this proposal addresses three outstanding methodological needs: limited availability of gene expression data for cancer subtypes, noisy and redundant imaging feature data, and lack of disease-informed, interpretable -omics integration, each addressed in its own specific aim. Aim 1 will use generative adversarial networks (GAN) to augment the small gene expression datasets for all high-risk HPV subtypes. Aim 2 will optimize imaging feature selection using a deep convolutional autoencoder (CAE). Aim 3 will integrate radiogenomic features through a structural equation modeling (SEM) approach incorporating HPV-specific oncogenic mechanisms as latent variables. Together, we expect fulfillment of these aims will create an optimized recurrence biomarker which will out- perform other prediction modalities as well as standard-of-care follow-up imaging. Beyond the specific application to HPV-driven malignancies, our proposal will generate novel tools and methods to integrate any high-dimensional radiogenomic data with hypothesis-driven research findings to improve cancer prediction.