Risk stratifying indeterminate pulmonary nodules with jointly learned features from longitudinal radiologic and clinical big data

利用纵向放射学和临床大数据共同学习的特征对不确定的肺结节进行风险分层

• 批准号: 10678264
• 负责人: Thomas Zhihe Li
• 金额: $ 3.28万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678264
• 关键词: Advanced Malignant Neoplasm; Algorithms; Anxiety; Aptitude; Archives; Area; Artificial Intelligence; Award; Benign; Big Data; Biometry; Cessation of life; Characteristics; Classification; Clinical; Clinical Informatics; Clinical Management; Clinical/Radiologic; Data; Decision Making; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic Procedure; Disease; Early Diagnosis; Engineering; Environment; Epidemic; Evaluation; Exhibits; Fellowship; Future; Goals; Growth; Health Care Costs; Healthcare Systems; Histologic; Histology; Histopathology; Image; Image Analysis; Incidence; Indolent; Institution; Intervention; Joints; Laboratories; Language; Learning; Lung; Lung Adenocarcinoma; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Measures; Medical; Medical History; Medical Imaging; Mentors; Metastatic Neoplasm to the Lung; Methods; Modality; Modeling; Modernization; Morbidity - disease rate; Nodule; Oncology; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Performance; Phenotype; Physicians; Positioning Attribute; Predictive Value; Probability; Prospective cohort; Public Health; Radiation; Radiologic Finding; Radiology Specialty; Recording of previous events; Records; Research; Resources; Retrospective cohort; Risk; Risk Reduction; Scientist; Smoking; Smoking History; Standardization; Subgroup; Techniques; Time; Training; Universities; Vision; Work; X-Ray Computed Tomography; anxiety reduction; artificial intelligence method; biomedical informatics; cancer imaging; cancer subtypes; career; chest computed tomography; clinical phenotype; clinical predictive model; clinically actionable; cohort; cost efficient; deep learning; design; electronic health data; health record; high dimensionality; high risk; improved; innovation; learning strategy; lens; low dose computed tomography; lung cancer screening; mortality; multimodal data; multimodality; noninvasive diagnosis; novel; novel strategies; personalized approach; precision oncology; predictive modeling; prospective; radiomics; risk stratification; screening; segregation; serial imaging; standard of care; success; symposium; tumor

PROJECT SUMMARY Indeterminate pulmonary nodules (IPNs) are highly prevalent radiologic findings that represent a substantial burden to patients and the national health care system because of the diagnostic challenge they present. There is a dire need to accurately stratify IPNs into low and high malignancy risk subgroups which are associated with clinical management pathways that are standardized and well validated. Clinical prediction models have the potential to do so in a scalable, cost-efficient, automated, and noninvasive manner, but advances in predictive accuracy must be made before they can make a substantial impact in medical practice. An unexplored direction in this area is integrating repeated measures of computed tomography (CT) studies and clinically-collected information within the same prediction model. This joint learning strategy has advantage of potentially modeling how dynamic radiologic changes like nodule growth rate vary with the trajectory of clinical variables such as smoking patterns and laboratory abnormalities. This perspective motivates the hypothesis that integrating information from longitudinal imaging and longitudinal clinical records will improve personalized IPN risk stratification and lung cancer subclassification From a clinician’s lens, this finding would not be surprising given the many time-varying modalities that are involved in diagnosis and decision making. This project leverages artificial intelligence (AI) and radiomic methods to analyze three retrospective cohorts with the possible addition of a large prospective cohort. The proposed work in Aim 1 will extend upon existing deep learning techniques to train a joint learning model on longitudinal images and clinical records to estimate the malignancy probability across time in patients with IPNs in a combined cohort exceeding 2000 subjects. This novel strategy will be evaluated against single-modality models and convention models that are used in practice. The evaluation will compare the models’ performance in stratifying IPNs into the low and high risk subgroups as a measure of clinical utility. Aim 2 asks if longitudinal change in radiomic features can distinguish between indolent and aggressive lung adenocarcinoma, other lung cancer subtypes, and pulmonary metastases. The proposed study will be the first to comprehensively characterize longitudinal radiomics across lung cancer subtypes and has the potential to identify novel longitudinal radiomic features that will aid early IPN evaluation and noninvasive lung cancer subclassification in patients with repeated imaging. In summary, the proposed research asks if clever integration of longitudinal information across different modalities can be leveraged to advance IPN risk stratification and lung cancer subclassification. This fellowship will be conducted at Vanderbilt University in a highly collaborative training environment with mentors in medical imaging AI, pulmonary oncology, biomedical informatics, radiology, and biostatistics. The proposed research and training plans are synergistically designed to ultimately prepare the candidate for a physician scientist career at the intersection of engineering innovation and precision oncology.

项目概要 不确定性肺结节 (IPN) 是非常普遍的放射学表现，代表着大量的肺部结节。 由于诊断挑战，给患者和国家医疗保健系统带来负担。 迫切需要将 IPN 准确地分为低和高恶性肿瘤风险亚组，这些亚组是 与标准化且经过充分验证的临床管理路径相关。 模型有潜力以可扩展、经济高效、自动化和非侵入性的方式做到这一点，但是 必须在预测准确性方面取得进步，才能对医疗实践产生重大影响。 该领域一个尚未探索的方向是整合计算机断层扫描 (CT) 研究的重复测量 和临床收集的信息在同一预测模型中，这种联合学习策略具有优势。 潜在地模拟动态放射学变化（如结节生长率）如何随肿瘤的轨迹变化 这种观点激发了吸烟模式和实验室异常等临床变量。 假设整合来自纵向成像和纵向临床记录的信息将 改善个性化 IPN 风险分层和肺癌亚分类 从临床医生的角度来看， 鉴于诊断和治疗中涉及许多随时间变化的方式，这一发现并不令人惊讶。 该项目利用人工智能 (AI) 和放射组学方法来分析三个方面。 目标 1 中拟议的工作将包括回顾性队列和可能添加的大型前瞻性队列。 扩展现有的深度学习技术来训练纵向图像的联合学习模型 临床记录，用于估计组合队列中 IPN 患者随时间变化的恶性肿瘤概率 超过 2000 个受试者将根据单一模态模型和惯例进行评估。 评估将比较模型在将 IPN 分层方面的表现。 目标 2 询问放射组学是否存在纵向变化。 特征可以区分惰性肺腺癌和侵袭性肺腺癌以及其他肺癌亚型， 拟议的研究将是第一个全面描述纵向特征的研究。 跨肺癌亚型的放射组学，并有可能识别新的纵向放射组学特征 这将有助于对反复发作的患者进行早期 IPN 评估和非侵袭性肺癌亚分类 总之，拟议的研究询问是否能够巧妙地整合跨领域的纵向信息。 可以利用不同的方式来推进 IPN 风险分层和肺癌亚分类。 奖学金将在范德比尔特大学与导师高度协作的培训环境中进行 医学成像人工智能、肺肿瘤学、生物医学信息学、放射学和生物统计学。 研究和培训计划协同设计，最终为候选人做好成为医生的准备 工程创新和精准肿瘤学交叉点的科学家职业生涯。

项目成果

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation.

通过强大的自动肺叶分割来量化肺部筛查计算机断层扫描中的肺气肿。

• 发表时间: 2023-07
• 作者: Li, Thomas Z;Hin Lee, Ho;Xu, Kaiwen;Gao, Riqiang;Dawant, Benoit M;Maldonado, Fabien;Sandler, Kim L;Landman, Bennett A
• 通讯作者: Landman, Bennett A

Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer.

为有肺癌风险的社区群体整理回顾性多模式和纵向数据。

• 发表时间: 2024-03-07
• 作者: Li, Thomas Z;Xu, Kaiwen;Chada, Neil C;Chen, Heidi;Knight, Michael;Antic, Sanja;Sandler, Kim L;Maldonado, Fabien;Landman, Bennett A;Lasko, Thomas A
• 通讯作者: Lasko, Thomas A