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.

拟议项目的目的是开发胰腺导管腺癌（PDAC）预测 通过人工智能（AI）来识别未来三年中PDAC风险高的个人 分析诊断前CT图像和非成像因子。 PDAC是癌症的第四个主要原因 - 美国目的地的男性和女性的相关死亡人数低。 5年生存 PDAC的所有阶段的比率为10％，但在早期诊断方面可能高达50％。所以， 鉴定PDAC高风险的个体作为随访成像具有很高的临床意义 检查或活检可能有助于早期检测，并在肿瘤仍在 可切除。但是，由于缺乏可靠的筛选工具，很难进行PDAC预测 敏感和特定的符号和生物标志物，以及较低的患病率。 腹痛是美国人去急诊室（ER）的最常见原因 通常进行腹部计算机断层扫描（CT）扫描。即使大多数扫描都没有显示 放射科医生肉眼可见的癌症迹象，一些受试者最终在 接下来的几年。这些诊断前CT图像提供了与 前癌症或早期癌症阶段的生物学变化，可以使用AI提取以预测PDAC 风险。因此，拟议项目的目的是在诊断前图像中发现独特的特征 使用AI并根据这些功能开发PDAC预测模型。非成像因素，例如 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是 包括在模型中提高预测准确性。主要假设是a）AI允许提取 诊断前CT图像中与前癌症或早期癌症生物学相关的独特图像特征 裸眼看不见的变化和b）诊断前图像特征和非 - 成像因子提高了使用常规的PDAC风险分层和预测的准确性 仅非成像因素。为了验证这些假设，我们将回顾性评估CT胰腺图像 在PDAC诊断之前，最多可获得3年，这些诊断被放射科医生认为是非癌性的。一组 接受类似的非胃肠道疾病且年龄/性别的类似成像研究的受试者 与诊断前成像匹配将用作健康对照。准确地分层高风险个人 将来可能允许早期发现PDAC。该项目的主要挑战是 适当的成像数据由于PDAC的患病率较低和严格的入学标准。八个专业 医疗中心将参加1,064例案件的收集。该项目的终点是开发， 培训和验证基于AI的PDAC预测模型，该模型将确定较高的个人 在未来3年内开发PDAC的风险。