Multiregional imaging phenotypes and molecular correlates of aggressive versus indolent breast cancer

侵袭性乳腺癌与惰性乳腺癌的多区域成像表型和分子相关性

基本信息

批准号：
10594058
负责人：
Ruijiang Li
金额：
$ 43.8万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-02-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10594058
关键词：
Adjuvant Therapy Anatomy Biological Biological Assay Biological Markers Breast Breast Cancer Patient Breast Cancer Risk Factor Cancer Etiology Characteristics Clinical Clinical Markers Clinical Trials Complement Data Diagnosis Diagnostic Disease Evaluation Exhibits Failure Functional disorder Gene Expression Gene Expression Profile Genes Genomics Goals Image Image Guided Biopsy Individual Indolent Institution Invaded MRI Scans Magnetic Resonance Imaging Methods Modeling Molecular Morbidity - disease rate Morphology Outcome Pathologic Pathway interactions Patients Phenotype Physiological Physiology Prognosis Prognostic Factor Prognostic Marker Protocols documentation Radiogenomics Randomized Recurrence Recurrent Malignant Neoplasm Regression Analysis Reproducibility Research Retrospective cohort Risk Role Staging Techniques Testing Texture The Cancer Genome Atlas The Cancer Imaging Archive Tissues Uncertainty United States Validation Variant Woman breast cancer progression burden of illness cancer recurrence cancer survival cancer type chemotherapy clinical translation cohort contrast enhanced genomic biomarker high risk imaging biomarker improved individualized medicine interest malignant breast neoplasm molecular phenotype mortality neglect oncotype overtreatment patient stratification precision oncology prognostic prognostic signature prognostic value prognostication prospective quantitative imaging risk minimization risk stratification treatment response tumor tumor heterogeneity

项目摘要

ABSTRACT The goal of this research is to develop and validate prognostic imaging biomarkers for breast cancer. A major challenge in the management of breast cancer is distinguishing patients with indolent disease from those with aggressive lethal disease at diagnosis. Currently, there are no reliable biomarkers to distinguish these groups on an individual level. Consequently, all patients with breast cancer receive adjuvant therapies, but not all benefit equally. This one-size-fits-all approach causes overtreatment, leading to morbidity and mortality. The need for reliable biomarkers is highlighted by the randomized TAILORx trial, which identified a small group of low-risk breast cancer patients who had very low rates of recurrence without chemotherapy, based on the 21-gene Oncotype Dx assay. Unfortunately, a majority (67%) of patients fell in the intermediate-risk range according to the genomic assay, and uncertainty still remains regarding the need for chemotherapy among these patients. Clearly, better biomarkers are needed to improve prognostication and patient stratification in breast cancer. Built on extensive preliminary data, we hypothesize that imaging characteristics reflect underlying tumor pathophysiology, and that image-based phenotyping of both tumor and parenchyma will provide much improved accuracy for recurrence prediction. To test this hypothesis, we propose to: (1) develop and improve methods to explicitly quantify multiregional MRI phenotypes including those of intratumoral subregion and parenchyma, and systematically assess their reproducibility; (2) develop a prognostic imaging signature using a large retrospective cohort of >1000 patients curated by the Stanford Oncoshare Project, and validate it in the prospective multi-center I-SPY 1 cohort; (3) construct a radiogenomic signature to perform additional testing of its prognostic value in 13 public gene expression cohorts of >5000 breast cancer patients. To further improve prognostication, we will build a multifactorial model that integrates imaging with clinical and genomic markers. This research will advance the quantitative imaging field by moving beyond traditional gross-tumor features and incorporating additional parenchymal and intratumoral imaging characteristics. If successful, it will provide much needed, rigorously validated imaging biomarkers for breast cancer, which can be further tested for clinical utility in prospective trials. Ultimately, such biomarkers can be used to stratify patients and guide individualized therapy, by allowing clinicians to avoid overtreatment of indolent disease and intensify treatment in women with aggressive disease.

抽象的这项研究的目标是开发和验证乳腺癌的预后成像生物标志物。乳腺癌治疗的一个主要挑战是区分惰性疾病患者来自那些在诊断时患有侵袭性致命疾病的人。目前尚无可靠的生物标志物在个人层面上区分这些群体。因此，所有乳腺癌患者都接受辅助疗法，但并非所有疗法都同样受益。这种一刀切的方法会导致过度治疗，导致发病率和死亡率。随机研究强调了对可靠生物标志物的需求 TAILORx 试验，该试验确定了一小群低风险乳腺癌患者，这些患者的乳腺癌风险非常低基于 21 基因 Oncotype Dx 检测的不化疗复发率。很遗憾，根据基因组检测，大多数（67%）患者属于中等风险范围，并且这些患者是否需要化疗仍存在不确定性。显然，更好需要生物标志物来改善乳腺癌的预后和患者分层。建立在广泛的初步数据，我们假设成像特征反映了潜在的肿瘤病理生理学，以及肿瘤和实质的基于图像的表型分析将提供大大提高了复发预测的准确性。为了检验这个假设，我们建议：（1）开发和改进方法来明确量化多区域 MRI 表型，包括瘤内分区和实质，并系统评估其再现性； (2) 开发一个使用由超过 1000 名患者组成的大型回顾性队列进行预后成像特征斯坦福 Oncoshare 项目，并在前瞻性多中心 I-SPY 1 队列中进行验证； (3) 构建放射基因组特征，以在 13 名公众中对其预后价值进行额外测试 > 5000 名乳腺癌患者的基因表达队列。为了进一步改善预测，我们将建立一个将成像与临床和基因组标记相结合的多因素模型。这项研究将超越传统的大体肿瘤特征，推动定量成像领域的发展结合额外的实质和肿瘤内成像特征。如果成功的话将会为乳腺癌提供急需的、经过严格验证的成像生物标志物，在前瞻性试验中进一步测试了临床实用性。最终，此类生物标志物可用于分层患者并指导个体化治疗，使临床医生避免对惰性患者的过度治疗疾病并加强对患有侵袭性疾病的女性的治疗。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.

通过多任务深度学习从胃癌 CT 图像预测腹膜复发和无病生存：一项回顾性研究。

DOI：
10.1016/s2589-7500(22)00040-1
发表时间：
2022-05-01
期刊：
The Lancet. Digital health
影响因子：
0
作者：
Yuming Jiang;Zhicheng Zhang;Q. Yuan;W. Wang;Hongyu Wang;Tuan;Weicai Huang;Jingjing Xie
通讯作者：
Jingjing Xie