Evaluation of novel radiomics and machine learning-derived CT-biomarkers in patient stratification and therapy response assessment in pancreatic ductal adenocarcinoma

新型放射组学和机器学习衍生的 CT 生物标志物在胰腺导管腺癌患者分层和治疗反应评估中的评估

• 批准号: 508329183
• 负责人: Dr. Felix Harder
• 金额: --
• 依托单位: Institut für diagnostische und interventionelle Radiologie
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2022
• 资助国家: 德国
• 起止时间: 2021-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/508329183?language=en
• 关键词: Evaluation; novel; radiomics; machine; learning

Pancreatic ductal adenocarcinoma (PDAC) ranges among the deadliest cancers, reflected by a devastating 5-year survival rate of only 9%. Moreover, the incidence of PDAC is rising and it is projected to take over second place among cancer-related deaths in the United States by the year 2030. A particular hallmark of PDAC is the high degree of genomic, transcriptomic and metabolic heterogeneity.The grim prognosis and the characteristic intra- and intertumoral heterogeneity underline the necessity for a more precise patient stratification and therapy response assessment to improve survival in PDAC. However, routine cross-sectional imaging methods are of limited value with regard to therapy response assessment and patient stratification in PDAC. Artificial intelligence based algorithms could help to overcome this issue and pave the path towards individualized precision oncology. However, previous studies on this field in PDAC most importantly lack of sufficient cohort sizes, standardization and availability of histopathologic features. Herein, we aim to develop a comprehensive stratification model for patients with primary resectable PDAC, combining state of the art radiomics and high-end deep learning procedures in the so far published largest multi-institutional and multi-national patient cohort. Moreover, we aim to unravel the potential of novel comprehensive imaging-derived biomarkers, including radiogenomic feature maps, for prediction of response assessment in patients with advanced PDAC included in the prospective COMPASS trial. This trial includes > 330 patients with advanced PDAC. All patients received extensive histopathological and genomic work-up (e.g. whole genomic sequencing and RNA sequencing). We will match state-of-the art machine learning and radiomics approaches with mutational and transcriptional features in order to develop novel imaging-derived biomarkers and radiogenomic feature maps for advanced PDAC. With our proposed research projects, we aim to develop advanced non-invasive imaging-derived biomarkers for a better patient stratification and therapy response assessment in patients with primary resectable and advanced PDAC, respectively.

胰腺导管腺癌(PDAC)是最致命的癌症之一，其5年生存率仅为9%。此外，PDAC的发病率正在上升，预计到2030年，它将超过美国癌症相关死亡人数的第二位。PDAC的一个特殊标志是基因组、转录和代谢的高度异质性。严峻的预后和肿瘤内和肿瘤间的特性异质性强调了更精确的患者分层和治疗反应评估的必要性，以提高PDAC的生存率。然而，常规的横断面成像方法在PDAC的治疗反应评估和患者分层方面的价值有限。基于人工智能的算法可以帮助克服这个问题，并为个性化精确肿瘤学铺平道路。然而，以前对PDAC这一领域的研究最重要的是缺乏足够的队列大小、标准化和组织病理学特征的可用性。在这里，我们的目标是为初次可切除的PDAC患者开发一个全面的分层模型，在迄今公布的最大的多机构和多国家患者队列中结合最先进的放射组学和高端深度学习程序。此外，我们的目标是揭示新的综合成像衍生生物标记物的潜力，包括放射基因组学特征图，用于预测纳入前瞻性COMPASS试验的晚期PDAC患者的反应评估。这项试验包括330名晚期PDAC患者。所有患者都接受了广泛的组织病理学和基因组检查(例如全基因组测序和RNA测序)。我们将把最先进的机器学习和放射组学方法与突变和转录特征相匹配，以便为先进的PDAC开发新的成像衍生生物标志物和放射基因组特征图。通过我们提出的研究项目，我们的目标是开发先进的非侵入性成像衍生生物标记物，分别用于更好地对初次可切除患者和晚期PDAC患者进行患者分层和治疗反应评估。