Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images

通过诊断前 CT 图像的人工智能分析预测胰腺导管腺癌 (PDAC)

• 批准号: 10475648
• 负责人: Debiao Li
• 金额: $ 100.05万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10475648
• 关键词: Abdominal Pain; Accounting; American; Artificial Intelligence; Biological; Biological Markers; Biopsy; Cancer Etiology; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Collection; Data; Data Set; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Early Diagnosis; Early treatment; Emergency department visit; Enrollment; Epidemiology; Eye; Future; Gender; Goals; Head; Image; Image Analysis; Incidence; Individual; Laboratories; Low Prevalence; Machine Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Manuals; Medical center; Modeling; Morphology; Operative Surgical Procedures; Pancreas; Pancreatic Ductal Adenocarcinoma; Pancreatic duct; Patients; Reader; Resectable; Risk; Scanning; Screening procedure; Shapes; Statistical Data Interpretation; Survival Rate; Symptoms; Tail of pancreas; Techniques; Testing; Texture; Time; Training; Training Technics; United States; Validation; Visit; Woman; X-Ray Computed Tomography; abdominal CT; artificial intelligence algorithm; automated segmentation; base; clinically significant; comorbidity; deep learning; experience; follow-up; high risk; human error; imaging study; improved; large datasets; men; mortality; pancreas imaging; predictive modeling; radiologist; radiomics; risk prediction; risk stratification; tumor

The objective of the proposed project is to develop a Pancreatic Ductal Adenocarcinoma (PDAC) prediction model to identify individuals who have high risk for PDAC in the next 3 years through Artificial Intelligence (AI) analysis of pre-diagnostic CT images and non-imaging factors. PDAC is the fourth leading cause of cancer- related deaths in both men and women in the United States despite its low incidence rate. The 5-year survival rate for all stages of PDAC is 10% but can be as high as 50% with early-stage diagnosis. Therefore, identification of individuals at high risk for PDAC has high clinical significance as follow-up imaging examinations or biopsy may assist in early detection and allow surgical intervention while the tumors are still resectable. However, PDAC prediction is difficult due to the lack of reliable screening tools, the absence of sensitive and specific symptoms and biomarkers, and low prevalence. Abdominal pain is the single most common reason that Americans visit the emergency room (ER), where an abdominal Computed Tomography (CT) scan is usually performed. Even though most scans don’t show any signs of cancer visible to the naked eyes of radiologists, some subjects eventually develop PDAC in the next few years. These pre-diagnostic CT images provide critical morphological information associated with biological changes at the pre-cancer or early cancer stage, which can be extracted using AI to predict PDAC risk. Therefore, the objective of the proposed project is to uncover unique features in pre-diagnostic images using AI and develop PDAC prediction model based on these features. Non-imaging factors such as demographic, epidemiologic, and anthropometric factors, clinical comorbidities, and laboratory tests will be included in the model to improve the prediction accuracy. The primary hypotheses are a) AI allows extraction of unique image features in pre-diagnostic CT images associated with pre-cancer or early cancer biological changes that are invisible to naked eyes and b) the combination of pre-diagnostic image features and non- imaging factors improves the accuracy of PDAC risk stratification and prediction over that using conventional non-imaging factors alone. To verify these hypotheses, we will retrospectively evaluate CT pancreatic images obtained up to 3 years prior to PDAC diagnosis that were deemed non-cancerous by radiologists. A group of subjects who underwent similar imaging studies for non-gastrointestinal disorders and were age/gender matched with pre-diagnostic imaging will serve as healthy controls. Accurately stratifying high risk individuals may allow for early detection of PDAC in the future. A major challenge of the project is the scarcity of the appropriate imaging data because of the low prevalence of PDAC and stringent enrollment criteria. Eight major medical centers will participate in collection of 1,064 cases. The end point of this project is the development, training, and validation of an AI-based PDAC prediction model, which will identify individuals who are at high risk for developing PDAC within the next 3 years.

拟议项目的目标是开发胰腺导管腺癌 (PDAC) 预测 通过人工智能 (AI) 识别未来 3 年内 PDAC 高风险人群的模型 诊断前 CT 图像和非影像因素的分析是癌症的第四大原因。 尽管其 5 年生存率较低，但美国男性和女性均出现相关死亡。 所有阶段的 PDAC 的发生率均为 10%，但早期诊断时的发生率可高达 50%。 识别 PDAC 高风险个体作为后续影像学检查具有很高的临床意义 检查或活检可能有助于早期发现，并在肿瘤仍然存在时进行手术干预 然而，由于缺乏可靠的筛查工具、缺乏 敏感且特异的症状和生物标志物，且患病率低。 腹痛是美国人去急诊室 (ER) 的最常见原因， 尽管大多数扫描不会显示，但通常会进行腹部计算机断层扫描 (CT)。 放射科医生肉眼可见的任何癌症迹象，一些受试者最终会在体内发展为 PDAC 这些预诊断 CT 图像提供了与以下疾病相关的关键形态学信息。 癌前或癌症早期阶段的生物学变化，可以使用 AI 提取来预测 PDAC 因此，该项目的目标是发现预诊断图像中的独特特征。 利用人工智能并基于这些特征等非成像因素开发PDAC预测模型。 人口统计学、流行病学和人体测量因素、临床合并症和实验室测试将 包含在模型中以提高预测准确性的主要假设是 a) AI 允许提取。 与癌前或早期癌症生物学相关的预诊断 CT 图像中的独特图像特征 b) 诊断前图像特征与非肉眼不可见的变化相结合 与使用传统方法相比，成像因素提高了 PDAC 风险分层和预测的准确性 为了验证这些假设，我们将回顾性评估 CT 胰腺图像。 在 PDAC 诊断前 3 年内获得的、被放射科医生认为是非癌性的。 接受过类似非胃肠道疾病影像学检查且年龄/性别的受试者 与预诊断成像相结合将作为健康对照，准确地对高危人群进行分层。 可能会在未来实现 PDAC 的早期检测 该项目的一个主要挑战是 PDAC 的稀缺性。 由于 PDAC 患病率低且入组标准严格，因此需要适当的影像学数据。 医疗中心将参与收集 1,064 个病例 该项目的终点是开发， 训练和验证基于人工智能的 PDAC 预测模型，该模型将识别处于高水平的个体 未来3年内发生PDAC的风险。