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 乳腺导管原位癌 (DCIS) 是一组异质性肿瘤病变，通常是 通过筛查乳房 X 光检查检查通常包括经皮（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，以及 大约 20-25% 的核心 Bx 确诊的 DCIS 患者会被升级为侵袭性导管原位癌。 预见到这一点将决定更积极的手术。 干预措施，包括前哨淋巴结活检以进行腋窝分期，另外还有 20-25% 的患者进行了判断。 患有低风险疾病，目前的想法是，这些女性在积极的活动中可能会获得更好的结果 监测设置，正在临床试验中进行测试，其最终目标及其总体影响。 项目是利用机器学习来识别生化（SA1）或成像（SA2）生物标志物，以及它们的 组合（SA3）来区分惰性 DCIS 和侵袭性 DCIS，通过切除后的升期来确定 活检。 这项工作要测试的主要假设是缺氧和缺氧相关蛋白的表达 (HRP) 可以区分攻击性 DCIS 和惰性 DCIS，这可以用于决策支持。 HRP 的表达通过免疫组织化学 (IHC) 进行最佳表征，我们已经部署了方法 我们拥有用于多重 IHC 的方法，以及使用机器学习（病理组学）进行高级分析的方法。 还表明，可以使用机器学习从 mpMRI 中识别乳腺癌内的缺氧栖息地 （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 手术前导管原位癌中缺氧环境的存在和程度，以预测手术后的后续升期 这项工作将在病理组学的目标 1 和放射组学的目标 2 中进行，目标 3 将进行。 每个目标将包含：（a）用于训练、调整和的回顾性手段。 测试；和（b）前瞻性内部和外部队列进行严格验证。 我们已经确定了 604 例 DCIS 女性接受了核心 Bx、mpMRI 和手术病理检查的病例 在过去 10 年中，莫菲特的内部前瞻性研究每月将有约 6 名女性同意。 完整的癌症护理®方案以及在莫菲特进行完整检查的人将。 在 UCSF 和 Advent Health 累积。 在这项工作结束时，我们将开发出一个 DCIS 风险模型，可以在手术前部署 从一端的主动监测到更广泛的手术干预，指导决策 另一方面，这有望为后续的介入试验奠定基础。 缺氧作为一个中心假说具有很大的潜力来阐明这一自然历史的组成部分 疾病。

项目成果

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。

• 发表时间: 2023-01
• 影响因子: 19.7
• 作者: Niell; Bethany L
• 通讯作者: Bethany L

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。

• 发表时间: 2023-11
• 作者: Expert Panel on Breast Imaging;Chetlen, Alison;Niell, Bethany L;Brown, Ann;Baskies, Arnold M;Battaglia, Tracy;Chen, Andrew;Jochelson, Maxine S;Klein, Katherine A;Malak, Sharp F;Mehta, Tejas S;Sinha, Indranil;Tuscano, Daymen S;Ulaner, Gary A
• 通讯作者: Ulaner, Gary A