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: 10693185
• 负责人: Debiao Li
• 金额: $ 93.44万
• 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/10693185
• 关键词: Abdominal Pain; Accounting; Age; 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; Techniques; Testing; Texture; Time; Training; Training Technics; United States; Validation; Visit; Woman; X-Ray Computed Tomography; abdominal CT; artificial intelligence algorithm; automated segmentation; clinically significant; comorbidity; deep learning; experience; follow-up; high risk; high risk population; human error; imaging study; improved; large datasets; men; mortality; pancreas imaging; predictive modeling; premalignant; 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.

该项目的目的是发展胰腺导管腺癌（PDAC）的预测 通过人工智能（AI）识别未来3年PDAC高风险个体的模型 分析诊断前CT图像和非影像学因素。PDAC是癌症的第四大病因- 在美国，尽管发病率很低，但男性和女性的相关死亡率都很高。5年生存 PDAC所有阶段的诊断率为10%，但早期诊断可高达50%。因此，我们认为， 识别PDAC高风险个体具有很高的临床意义， 检查或活组织检查可有助于早期发现，并允许在肿瘤仍然存在时进行手术干预。 可切除的然而，由于缺乏可靠的筛查工具，缺乏 敏感和特异的症状和生物标志物，以及低患病率。 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摄影（CT）扫描。尽管大多数扫描并没有显示 尽管放射科医生肉眼可见的任何癌症迹象，但一些受试者最终在肿瘤中发展为PDAC。 未来几年这些诊断前CT图像提供了与以下相关的关键形态学信息： 癌症前期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，该项目的目标是揭示诊断前图像的独特功能 并基于这些特征建立PDAC预测模型。非成像因素，如 人口统计学、流行病学和人体测量学因素、临床合并症和实验室检查将 包括在模型中以提高预测精度。主要假设是a）AI允许提取 与癌前或早期癌症生物学特征相关的诊断前CT图像中的独特图像特征 肉眼不可见的变化，以及B）诊断前图像特征和非诊断性图像特征的组合。 影像学因素提高了PDAC危险分层和预测的准确性， 非成像因素单独。为了验证这些假设，我们将回顾性评估CT胰腺图像 在PDAC诊断前最多3年获得，被放射科医生认为是非癌性的。一群 接受过类似非胃肠道疾病成像研究且年龄/性别 与诊断前成像相匹配的将作为健康对照。对高危人群进行准确分层 可以允许在未来早期检测PDAC。该项目的一个主要挑战是缺乏 由于PDAC的低患病率和严格的入组标准，需要适当的成像数据。八大 医疗中心将参与收集一千零六十四个病例。这个项目的终点是发展， 训练和验证基于AI的PDAC预测模型，该模型将识别处于高风险的个体。 在未来三年内开发PDAC的风险。

项目成果

Segmentation of Pancreatic Subregions in Computed Tomography Images.

计算机断层扫描图像中胰腺子区域的分割。

• DOI: 10.3390/jimaging8070195
• 发表时间: 2022-07-12
• 期刊: Journal of imaging
• 影响因子: 3.2

Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images.

• DOI: 10.1117/1.jmi.9.2.024002
• 发表时间: 2022-03
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Qureshi TA;Lynch C;Azab L;Xie Y;Gaddam S;Pandol SJ;Li D
• 通讯作者: Li D

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

• DOI: 10.3233/cbm-210273
• 发表时间: 2022
• 期刊: CANCER BIOMARKERS
• 影响因子: 3.1
• 作者: Qureshi, Touseef Ahmad;Gaddam, Srinivas;Wachsman, Ashley Max;Wang, Lixia;Azab, Linda;Asadpour, Vahid;Chen, Wansu;Xie, Yibin;Wu, Bechien;Pandol, Stephen Jacob;Li, Debiao
• 通讯作者: Li, Debiao

Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.

• DOI: 10.21037/cco-21-117
• 发表时间: 2022-03
• 期刊: CHINESE CLINICAL ONCOLOGY
• 影响因子: 2.8
• 作者: Qureshi, Touseef Ahmad;Javed, Sehrish;Sarmadi, Tabasom;Pandol, Stephen Jacob;Li, Debiao
• 通讯作者: Li, Debiao