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.

拟议项目的目标是开发胰腺导管腺癌(PDAC)的预测 通过人工智能(AI)识别未来3年内PDAC高危人群的模型 诊断前CT影像与非影像因素的分析。PDAC是导致癌症的第四大原因- 尽管美国的发病率很低，但男性和女性的相关死亡人数都很少。五年的生存时间 PDAC所有阶段的发病率为10%，但早期诊断的发病率可高达50%。因此， 随着影像随访，识别PDAC高危个体具有很高的临床意义 检查或活组织检查可能有助于早期发现，并允许在肿瘤仍然存在的情况下进行手术治疗 可切除的。然而，PDAC的预测是困难的，因为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)最常见的原因，在那里 通常要进行腹部计算机断层扫描。即使大多数扫描都没有显示 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 接下来的几年。这些诊断前的CT图像提供了与 癌前或癌症早期的生物学变化，可使用人工智能提取以预测PDAC 风险。因此，拟议项目的目标是发现诊断前图像的独特特征 利用人工智能技术，开发基于这些特征的PDAC预测模型。非成像因素，如 人口学、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)人工智能允许提取 与癌前或早期癌生物学相关的诊断前CT图像中的独特图像特征 肉眼看不到的变化以及b)诊断前图像特征和非诊断图像特征的组合 与常规方法相比，影像因素提高了PDAC风险分层和预测的准确性 仅有非成像因素。为了验证这些假设，我们将回顾评估CT胰腺图像 在PDAC诊断之前最多3年获得，被放射科医生认为是非癌症的。一群 接受过类似的非胃肠道疾病的影像研究的受试者，年龄/性别 与诊断前影像相匹配的患者将作为健康对照。准确划分高危人群 可能允许在未来及早发现PDAC。该项目的一个主要挑战是缺乏 合适的影像数据，因为PDAC的患病率很低，而且招生标准严格。八大 医疗中心将参与1064例病例的收集。这个项目的终点是开发， 培训，并验证基于人工智能的PDAC预测模型，该模型将识别处于兴奋状态的个人 在未来3年内开发PDAC的风险。

项目成果

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

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

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