Prediction of treatment response and outcome in locally advanced rectal cancer using radiomics and deep learning: an example case to demonstrate a general purpose deep-learning-based processing pipeline for image classification.

使用放射组学和深度学习预测局部晚期直肠癌的治疗反应和结果：展示用于图像分类的通用基于深度学习的处理流程的示例。

• 批准号: 428149221
• 负责人: Professorin Dr. Ulrike I. Attenberger
• 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 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/428149221?language=en
• 关键词: Prediction; treatment; response; outcome; locally

Over the last decade, rectal cancer has become the number 3 most lethal disease in Europe with a 5-year survival rate of only 68% in Germany. Incidence rates are still increasing and remain above 60%. Even though diagnostic and treatment opportunities have improved in recent years, rectal cancer remains a heterogeneous disease in terms of treatment response and outcome.Thus far, only clinical and magnetic resonance imaging (MRI) based criteria are used for guiding treatment decisions, once the diagnosis has been confirmed. Although MRI has evolved to become the standard diagnostic approach in the local staging of rectal cancer, it does not provide information on intratumor heterogeneity or molecular subtypes. Consequently, novel imaging biomarkers are urgently needed in order to better characterize rectal cancer subtypes, aiming at an improved prediction of treatment response and patient outcome.Texture analysis, radiomics and deep learning strategies are increasingly used to address these challenges and to improve patient care. However, patient cohorts in many radiomics studies were relatively small, studies often lacked a validation cohort and imaging data were obtained within one single institution or different centers with similar MRI scanners, thus not allowing for assessing the generalizability of the trained models. Modern radiomics techniques therefore are increasingly shifted towards recent developments in deep learning.The purpose of this study is to develop a radiomics- and deep learning-based imaging signature of rectal cancer, which is able to decode different tumor phenotypes and to non-invasively assess / predict therapeutic response in correlation to histopathology as well as genomics / clinomics. This should lead to a comprehensive characterization of tumor heterogeneity and tumor biology by imaging criteria, which will then allow for individually tailored treatment strategies in the future. The entire framework will be developed based on available open source methodology and will be made available for future use and research, thus enabling future translation into routine clinical practice.Available methodology from the field of radiomics, artificial intelligence, and computer vision will be applied on a prospectively acquired, well-structured multi-center dataset from the CAO-ARO-AIO-12 study, which is already available and accessible for the purpose of the present study. This dataset includes radiation-planning CT data, pre- and post-treatment multiparametric MRI data, histopathological information as well as clinical and genomic data. A team of four experienced PIs from the fields of oncologic imaging, radiomics, MRI physics, radiation therapy, and informatics will work as an interdisciplinary team and share their knowledge-gain with other members of the priority program consortium in an open-source fashion.

在过去的十年中，直肠癌已成为欧洲第三大致命疾病，在德国的5年生存率仅为68%。发病率仍在上升，保持在60%以上。尽管近年来诊断和治疗机会有所改善，但直肠癌在治疗反应和结果方面仍然是一种异质性疾病。到目前为止，只有临床和磁共振成像（MRI）为基础的标准用于指导治疗决策，一旦诊断得到证实。虽然MRI已经发展成为直肠癌局部分期的标准诊断方法，但它不能提供肿瘤内异质性或分子亚型的信息。因此，迫切需要新的成像生物标志物来更好地表征直肠癌亚型，旨在改善治疗反应和患者结局的预测。纹理分析，放射组学和深度学习策略越来越多地用于解决这些挑战并改善患者护理。然而，许多放射组学研究中的患者队列相对较小，研究通常缺乏验证队列，并且成像数据是在一个单一机构或具有类似MRI扫描仪的不同中心内获得的，因此无法评估训练模型的普遍性。因此，现代放射组学技术越来越多地转向深度学习的最新发展。本研究的目的是开发基于放射组学和深度学习的直肠癌成像特征，该特征能够解码不同的肿瘤表型，并非侵入性地评估/预测与组织病理学以及基因组学/临床组学相关的治疗反应。这将导致通过成像标准对肿瘤异质性和肿瘤生物学进行全面表征，从而允许未来个性化的治疗策略。整个框架将基于可用的开源方法开发，并将用于未来的使用和研究，从而使未来能够转化为常规临床实践。放射组学，人工智能和计算机视觉领域的可用方法将应用于CAO-ARO-AIO-12研究中前瞻性获取的结构良好的多中心数据集，这份文件为本研究报告的目的已经可以获得。该数据集包括放射计划CT数据、治疗前和治疗后多参数MRI数据、组织病理学信息以及临床和基因组数据。来自肿瘤成像，放射组学，MRI物理学，放射治疗和信息学领域的四名经验丰富的PI团队将作为一个跨学科团队工作，并以开源方式与优先计划联盟的其他成员分享他们的知识。