Radiomics and Pathomics to predict upstaging of DCIS

放射组学和病理组学预测 DCIS 的分期

• 批准号: 10376844
• 负责人: Mehdi Damaghi
• 金额: $ 66.93万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10376844
• 关键词: Acidosis; Adjuvant; Adjuvant Therapy; Antibodies; Area; Attention; Axilla; Basic Science; Biochemical; Biological Markers; Biopsy; Breast; Breast Carcinogenesis; Carcinoma; Cells; Clinical; Clinical Paths; Clinical Trials; Consent; Core Biopsy; Data; Diagnosis; Diagnostic; Disease; Duct (organ) structure; Epigenetic Process; Evolution; Excision; Excision biopsy; Formalin; Foundations; Functional disorder; Genotype; Goals; Habitats; Health; Heritability; Histologic; Hyperplasia; Hypoxia; Image; Immunohistochemistry; Indolent; Intervention Trial; Knowledge; Lesion; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Mammographic screening; Mammography; Metastatic breast cancer; Methods; Milk; Modeling; Multiparametric Analysis; Mutation; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Outcome; Paraffin Embedding; Pathologic; Pathology; Patients; Periodicity; Phenotype; Physiological; Population; Prevalence; Process; Prospective Studies; Prospective cohort; Proteins; Protocols documentation; Radiation; Radio; Radiology Specialty; Receiver Operating Characteristics; Resected; Retrospective Studies; Retrospective cohort; Risk; SLC2A1 gene; Sentinel Lymph Node Biopsy; Staging; Stains; Surgical Pathology; Testing; Time; Tissue Embedding; Tissues; Training; Validation; Variant; Woman; Work; advanced analytics; arm; base; breast malignancies; cancer care; cohort; contrast enhanced; deep learning; disease natural history; disorder risk; hormone therapy; malignant breast neoplasm; neoplastic; optimal treatments; practical application; preclinical study; premalignant; prospective; radiomics; standard of care; statistics; tumor; validation studies

Abstract Ductal carcinomas in situ (DCIS) of the breast are a heterogeneous group of neoplastic lesions that are usually detected by screening mammography. Workup generally includes a percutaneous (core) Biopsy (Bx) for histologic confirmation, followed by multiparametric MRI (mpMRI), followed by breast-conserving excision, and adjuvant radiation. Approximately 20-25% of patients with core Bx-confirmed DCIS are upstaged to invasive carcinoma upon pathology of resected tissue. Foreknowledge of this would dictate a more aggressive surgical intervention, including sentinel node biopsy for axillary staging. Further, another 20-25% of patients are judged to have low-risk disease and current thought is that such women may have better outcomes in an active surveillance setting, and this is being tested in clinical trials. The ultimate goal and the overall impact of this project is to use machine learning to identify biochemical (SA1) or imaging (SA2) biomarkers, as well as their combination (SA3) to discriminate indolent from aggressive DCIS, as determined by upstaging upon excisional biopsy. The major hypothesis to be tested in this work is that hypoxia and expression of hypoxia-related proteins (HRPs) can discriminate aggressive from more indolent DCIS, and that this can be used for decision support. Expression of HRPs is optimally characterized by immunohistochemistry (IHC), and we have deployed methods for multiplexed IHC, as well as methods for advanced analytics using machine learning (pathomics). We have also shown that hypoxic habitats within breast cancers can be identified from mpMRI using machine learning (radiomics). We thus propose to use pathomics of core biopsies and radiomics of mpMRI to determine the presence and extent of hypoxic habitats in DCIS prior to surgery to predict subsequent upstaging after surgical resection. This work will be performed in Aim 1 for pathomics and Aim 2 for radiomics, and Aim 3 will develop combined radio-pathomics predictors. Each aim will contain: (a) retrospective arms for training, tuning, and testing; and (b) prospective internal and external cohorts for rigorous validation. For the retrospective studies, we have identified 604 cases wherein women with DCIS obtained core Bx, mpMRI, and surgery with pathology at Moffitt in the last 10 years. Internal prospective studies will accrue ~6 women/month who have consented to the total Cancer Care® protocol and who have their complete workup at Moffitt. External validation cohorts will be accrued at UCSF and at Advent Health. At the end of this work we will have developed a risk model for DCIS that can be deployed prior to surgery to guide decisions along the spectrum from active surveillance at one end to more extensive surgical intervention at the other. This is expected to lay a foundation for subsequent interventional trials. Additionally, the inclusion of hypoxia as a central hypothesis has high potential to illuminate components of the natural history of this disease.

摘要 乳腺导管原位癌（DCIS）是一组异质性肿瘤性病变，通常是 通过筛查性乳房X光检查发现。检查通常包括经皮（核心）活检（Bx）， 组织学确认，然后进行多参数MRI（mpMRI），然后进行保乳切除术，以及 辅助辐射大约20-25%的核心BX证实的DCIS患者被升级为侵入性 切除组织病理学上的癌。对此的预见将决定采取更积极的手术 介入，包括前哨淋巴结活检腋窝分期。此外，另有20-25%的患者被判定 低风险的疾病，目前的想法是，这些妇女可能有更好的结果，在一个积极的 这是一个监测环境，正在临床试验中进行测试。最终目标和总体影响 该项目是使用机器学习来识别生物化学（SA 1）或成像（SA 2）生物标志物，以及它们的生物标志物。 联合用药（SA 3），以区分无痛性和侵袭性DCIS，如通过切除后分期上调确定的 活检 在这项工作中要检验的主要假设是缺氧和缺氧相关蛋白的表达 （HRPs）可以区分侵略性更懒惰的DCIS，这可以用于决策支持。 HRPs的表达最好通过免疫组织化学（IHC）来表征，我们已经部署了方法 用于多重IHC，以及使用机器学习（病理组学）进行高级分析的方法。我们有 还表明，可以使用机器学习从mpMRI中识别乳腺癌中的缺氧栖息地， （放射组学）。因此，我们建议使用核心活检的病理组学和mpMRI的放射组学来确定 术前DCIS中缺氧环境的存在和程度，以预测术后的后续升级 切除术这项工作将在针对病理组学的目标1和针对放射组学的目标2中进行，目标3将开发 组合的放射病理学预测因子。每个目标将包含：（a）用于训练、调整和 测试;和（B）前瞻性内部和外部队列进行严格验证。对于回顾性研究， 我们已经确定了604例DCIS女性患者，其中接受了核心Bx、mpMRI和手术病理检查 在莫菲特工作了十年内部前瞻性研究将每月招募约6名同意 所有癌症护理方案，并在莫菲特进行完整的检查。外部验证队列将 在加州大学旧金山分校和降临节健康中心 在这项工作结束时，我们将开发一个DCIS的风险模型，可以在手术前部署， 引导决策沿着从一端的积极监测到更广泛的外科干预的范围 另一边。这有望为后续的干预性试验奠定基础。此外，包括 缺氧作为一个中心假设有很高的潜力，以阐明组成部分的自然历史，这 疾病