Development of a Multimodal Deep Learning Model for the Generation of Cancer Probability Maps and Imaging Biomarkers for Prostate Cancer using Multiparametric MRI

使用多参数 MRI 开发用于生成前列腺癌癌症概率图和成像生物标志物的多模态深度学习模型

• 批准号: 9723045
• 负责人: Karthik Venkataraman Sarma
• 金额: $ 3.95万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9723045
• 关键词: Address; Age; Algorithms; American; Area; Belief; Biological Markers; Biopsy; Caring; Classification; Clinical; Clinical Data; Complex; Computer-Assisted Diagnosis; Consensus; Data; Data Set; Detection; Development; Disease; Engineering; Evaluation; Future; Generations; Goals; Guidelines; Histopathology; Hospitals; Human; Image; Incidence; Individual; Indolent; Learning; Lesion; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Manuals; Maps; Medical; Medical History; Medical Records; Methodology; Methods; Modality; Modeling; Morbidity - disease rate; National Comprehensive Cancer Network; Newly Diagnosed; Outcome Measure; Pathology; Patients; Physiologic pulse; Population; Probability; Process; Prostate; Prostate Adenocarcinoma; Prostatectomy; Radical Prostatectomy; Recording of previous events; Research; Risk; Risk Factors; Screening for Prostate Cancer; Screening procedure; Sensitivity and Specificity; Serum; Site; Specificity; Specimen; System; Techniques; Testing; Tissues; Training; Transrectal Ultrasound; United States; Visual; Work; autoencoder; base; cancer diagnosis; cancer therapy; convolutional neural network; cost; deep learning; deep learning algorithm; denoising; digital; follow-up; high risk; imaging Segmentation; imaging biomarker; improved; innovation; insight; interest; men; mortality; multimodality; non-linear transformation; novel; predictive modeling; primary outcome; public health relevance; radiologist; rectal; risk stratification; routine screening; screening; screening guidelines; secondary outcome; serum PSA; spatiotemporal; support vector machine; tool

Project Summary/Abstract Background: Prostatic adenocarcinoma is the most common newly diagnosed cancer and second deadliest cancer in American men. There is a large discrepancy between the incidence of the disease and its mortality rate. Thus, the development of screening tools to identify prostate cancer and determine if it is aggressive or indolent is an area of considerable interest. Current methods rely on the use of serum biomarkers and follow-up biopsies for screening. However, there is substantial debate as to the appropriate methodology for screening. The goal of this proposal is the development of: 1) new imaging biomarkers (i.e., “features”) for prostate cancer; and 2) a novel predictive model for the presence of aggressive prostatic adenocarcinoma. These tools will enable more effective use of mp-MRI in prostate cancer screening in the future and thus enable a future improvement in the sensitivity and specificity of screening, reducing the rates of overdiagnosis and underdiagnosis. Aim 1: To implement a deep learning algorithm for clinical prostate mp-MRI sequences, creating a cancer prob- ability map that is predictive of biopsy results. Aim 2: To create a multimodal framework that will combine discovered imaging features with clinical data points from the medical record (e.g., age, risk factors, medical history, biomarkers) to predict the presence and aggressiveness of prostatic adenocarcinoma. Methods: In Aim 1, a deep convolutional neural network (CNN) will be trained on a clinical dataset comprised of patches extracted from pre-prostatectomy mp-MRI sequences from patients with prostate cancer, using his- topathology analysis of whole-mount radical prostatectomy specimens as ground truth. The innovations in this aim will be the development of a CNN that can simultaneously learn from three different imaging sequence types, the use of patches for data augmentation, and the proper alignment of mp-MRI sequences and prostatectomy specimens for machine learning. The result of the work of this aim will be the creation of an algorithm for gen- erating imaging biomarkers (features) and cancer probability maps from mp-MRI data. In Aim 2, a multimodal learning framework that will integrate mp-MRI sequence data with clinical parameters in order to predict the presence of aggressive prostatic adenocarcinoma will be developed. The innovation in this aim will be the devel- opment of a framework that can integrate information from multiple modalities (imaging, serum, history, etc.) in order to generate a high confidence prediction of the presence of aggressive prostate cancer without the use of invasive testing. Long-term Objective: The development of a novel predictive model for the presence of aggressive prostatic adenocarcinoma in prostate mp-MRI data that will enable better future use of this data for the early detection of prostate cancer.

项目总结/摘要 背景：前列腺腺癌是最常见的新诊断的癌症和第二致命的 美国男性的癌症这种疾病的发病率和死亡率之间有很大的差距。 因此，开发筛查工具来识别前列腺癌并确定它是侵袭性的还是惰性的， 是一个相当令人感兴趣的领域。目前的方法依赖于使用血清生物标志物和后续活检 用于筛选。然而，对于适当的筛查方法存在着大量的争论。的目标 该建议是开发：1）新的成像生物标记物（即，2）前列腺癌的特征;以及 侵袭性前列腺腺癌存在的新预测模型。这些工具将使更多 MP-MRI在未来前列腺癌筛查中的有效使用，从而能够在未来改善 筛查的敏感性和特异性，减少过度诊断和诊断不足的比率。 目的1：为临床前列腺mp-MRI序列实现深度学习算法，创建癌症探针， 预测活检结果的能力图。 目标2：创建一个多模态框架，将联合收割机发现的成像特征与临床数据相结合 来自医疗记录的点（例如，年龄、风险因素、病史、生物标志物）来预测存在和 前列腺腺癌的侵袭性。 方法：在目标1中，深度卷积神经网络（CNN）将在临床数据集上进行训练， 使用他的-从前列腺癌患者的前列腺切除术前mp-MRI序列中提取的贴片 作为基础事实的全包埋根治性直肠癌切除术标本的病理学分析。这方面的创新 目标是开发一种CNN，它可以同时从三种不同的成像序列类型中学习， 使用补丁进行数据增强，以及mp-MRI序列和椎间盘切除术的正确对齐 机器学习的样本。这一目标的工作的结果将是创建一个算法，用于生成- 从mp-MRI数据中生成成像生物标志物（特征）和癌症概率图。在目标2中， 将mp-MRI序列数据与临床参数相结合，以预测 将出现侵袭性前列腺腺癌。在这一目标上的创新将是发展- 可以整合来自多种模态（成像、血清、病史等）的信息的框架的开发 为了产生侵袭性前列腺癌存在的高置信度预测而不使用 侵入性测试 长期目标：建立一种新的预测侵袭性前列腺的模型 前列腺腺癌的mp-MRI数据，这将使未来更好地利用这些数据进行早期检测， 前列腺癌