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: 10652253
• 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/10652253
• 关键词: Acidosis; Adjuvant; Adjuvant Therapy; Antibodies; Area; Attention; Axilla; Basic Science; Biochemical; Biological Markers; Biopsy; Breast; Breast Carcinogenesis; Breast conservation; 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; Periodicals; 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; breast malignancies; cancer care; cohort; contrast enhanced; deep learning; disease natural history; disorder risk; hormone therapy; machine learning model; malignant breast neoplasm; model building; 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.

抽象的 乳腺的原位导管癌是通常是肿瘤病变的一组 通过筛查乳房摄影检测。工作通常包括经皮（核心）活检（BX） 组织学确认，然后是多参数MRI（mpmri），然后是乳房持乳房的惊喜， 辅助辐射。大约20-25％的核心BX确认DCI患者被淘汰为侵入性 癌对切除组织的病理。预知这将决定一个更具侵略性的手术 干预措施，包括用于腋窝分期的前哨节点活检。此外，另外20-25％的患者被评判 患有低风险的疾病和当前的想法是，这样的女性可能在活跃的情况下取得更好的结果 监视设置，并且正在临床试验中进行测试。最终目标和总体影响 项目是使用机器学习来识别生化（SA1）或成像（SA2）生物标志物及其 组合（SA3）以抗侵略性的DCI区分，通过升级时确定 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 （HRP）可以将积极的侵略性与更懒惰的DCI区分开，并且可以将其用于决策支持。 HRP的表达以免疫组织化学（IHC）为特征，并且我们已经部署了方法 用于多路复用IHC以及使用机器学习（病原体）的高级分析方法。我们有 还表明，可以使用机器学习从MPMRI鉴定乳腺癌中的低氧栖息地 （放射线学）。因此，我们建议使用核心活检和MPMRI的放射素学的病原体来确定 手术前DCIS中缺氧栖息地的存在和程度，以预测手术后随后的升级 切除。这项工作将在AIM 1中用于病原体和AIM 2用于放射素学，AIM 3将发展 联合无线病预测指标。每个目标都将包含：（a）回顾性训练，调整和 测试； （b）严格验证的前瞻性内部和外部人群。对于回顾性研究， 我们已经确定了604例DCI的女性获得了核心BX，MPMRI和病理手术 在过去的十年中在莫菲特。内部前瞻性研究将累积约6名妇女/月的女性 Total CancerCare®方案以及在Moffitt进行完整的工作。外部验证队列将 在UCSF和Advent Health积累。 在这项工作结束时，我们将开发DCI的风险模型，可以在手术前部署到 从一端到更广泛的手术干预措施的主动监视沿频谱的指导决策 在另一个。预计这将为随后的介入试验奠定基础。另外，包含 缺氧作为中心假设具有阐明这种自然历史的组成部分的高潜力 疾病。

项目成果

ACR Appropriateness Criteria® Breast Implant Evaluation: 2023 Update.

ACR 适当性标准® 乳房植入物评估：2023 年更新。

• DOI: 10.1016/j.jacr.2023.08.019
• 发表时间: 2023
• 期刊: Journal of the American College of Radiology : JACR
• 作者: ExpertPanelonBreastImaging;Chetlen,Alison;Niell,BethanyL;Brown,Ann;Baskies,ArnoldM;Battaglia,Tracy;Chen,Andrew;Jochelson,MaxineS;Klein,KatherineA;Malak,SharpF;Mehta,TejasS;Sinha,Indranil;Tuscano,DaymenS;Ulaner,GaryA

Background Parenchymal Enhancement at Breast MRI: More Is Not Better.

乳房 MRI 背景实质增强：并非越多越好。

• DOI: 10.1148/radiol.221901
• 发表时间: 2023
• 期刊: Radiology
• 影响因子: 19.7
• 作者: Niell,BethanyL