Next-generation image analytics for chemotherapy and survival prediction in pancreatic ductal adenocarcinoma

用于胰腺导管腺癌化疗和生存预测的下一代图像分析

• 批准号: 428215948
• 负责人: Professor Dr. Rickmer Früdd Braren
• 金额: --
• 依托单位: Institut für diagnostische und interventionelle Radiologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/428215948?language=en
• 关键词: Next; generation; image; analytics; chemotherapy

The objective of the project is to establish a robust, quality-controlled image processing pipeline and to generate, validate and test predictive models of chemotherapy response and survival in patients with PDAC from standardized routine clinical computed tomography imaging.Research AimsFollowing the objectives, we aim specifically: (1) to establish an image pre-processing and segmentation pipeline including a seed-based region growing algorithm capable of automatically segmenting pancreatic cancer lesions which can be deployed to client-side devices for usage by radiologists with limited experience in pancreatic cancer;(2) to generate a radiomics-based model predictive of short- and long-term response to current intensified chemotherapy regimens based on baseline and early follow-up imaging data; (3) to create a multivariate disease-free/ overall survival model including the derived radiomic signatures alongside clinical data from the NEOLAP trial to assess the incremental value of a radiomics approach over traditional risk assessment tools.Expected ImpactWe expect our research:(1) to improve our understanding of PDAC biology by identifying distinct radiomic features of treatment-relevant tumor subtypes;(2) to identify radiomic signatures predictive of specific therapy response and individual survival in PDAC;(3) to provide non-expert radiologist and clinical partners with tools for decision support regarding patient stratification (i.e. personalized medicine) and therapy monitoring;(4) to facilitate research in the field of pancreatic cancer and precision oncology, providing advanced image-analysis algorithms for the prediction of patient outcome.

该项目的目标是建立一个强大的，质量控制的图像处理管道，并生成，验证和测试预测模型的化疗反应和生存的PDAC患者从标准化的常规临床CT imaging.Research AimsFollowing the objectives，我们的目标具体如下：（1） 建立图像预处理和分割流水线，包括能够自动分割胰腺癌病变的基于种子的区域生长算法，其可以部署到客户端设备，以供胰腺癌经验有限的放射科医生使用;（2） 基于基线和早期随访成像数据，生成基于放射学的模型，预测对当前强化化疗方案的短期和长期反应;（3） 创建一个多变量无病/总生存模型，包括来自NEOLAP试验的临床数据以及衍生的放射组学特征，以评估放射组学方法相对于传统风险评估工具的增量价值。预期影响我们期望我们的研究：（1） 通过识别治疗相关肿瘤亚型的不同放射组学特征，提高我们对PDAC生物学的理解;（2） 鉴定预测PDAC中特异性治疗反应和个体存活的放射组学特征;（3） 为非专业放射科医生和临床合作伙伴提供有关患者分层（即个性化医疗）和治疗监测的决策支持工具;（4） 促进胰腺癌和精确肿瘤学领域的研究，提供先进的图像分析算法来预测患者的预后。