Development of a 3D Imaging Diagnostic Tool for the Improved Characterization of Metastatic Melanoma

开发 3D 成像诊断工具以改善转移性黑色素瘤的表征

• 批准号: 10241692
• 负责人: Thomas Steven Villani
• 金额: $ 84.42万
• 依托单位: VISIKOL, INC.
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241692
• 关键词: 3-Dimensional; Address; Adjuvant; Adjuvant Therapy; American Joint Committee on Cancer; Archives; Axillary lymph node group; Biological Assay; Biopsy Specimen; Caring; Cessation of life; Classification; Clinical; Clinical Laboratory Improvement Amendments; Clinical Research; Clinical Treatment; Computer software; Detection; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Distant; Documentation; Economics; Evaluation; Excision; Guidelines; Histologic; Histopathology; Human Resources; Image; In Situ; Incidence; Label; Laboratories; Malignant Neoplasms; Metastatic Melanoma; Monitor; National Comprehensive Cancer Network; Neoplasm Metastasis; Nivolumab; Nonmetastatic; Operative Surgical Procedures; Outcome; Pathologist; Pathology; Patient-Focused Outcomes; Patients; Phase; Pilot Projects; Population; Primary Neoplasm; Probability; Procedures; Process; Proliferating; Protocols documentation; Quality-Adjusted Life Years; Recurrence; Reporting; Reproducibility; Sampling; Sensitivity and Specificity; Sentinel Lymph Node; Sentinel Lymph Node Biopsy; Skin Cancer; Specificity; Specimen; Statistical Models; Surveys; Testing; Three-Dimensional Imaging; Tissue imaging; Tissues; Translating; Treatment Protocols; Validation; Work; assay development; base; cancer cell; chemotherapy; clinical effect; density; diagnostic assay; diagnostic platform; digital; digital pathology; economic value; experience; follow-up; imager; imaging approach; improved; improved outcome; interest; lymph nodes; malignant breast neoplasm; melanocyte; melanoma; neoplastic cell; software development; tissue processing; tool

Summary The objective of this Phase II project is to improve outcomes and survival for patients with melanoma by reducing the number of false negative sentinel lymph node biopsies wherein metastatic disease is missed through conventional tissue processing and histological characterization. It is estimated that 4,200 patients are misdiagnosed in this manner in the US per year which results in more conservative treatment regimens for these patients than is required to effectively treat their disease. The outcome of patients not receiving the proper treatment regimen is that they experience a three-year reduction in ten-year survival which on a quality adjusted life year basis translates into a $1.2 billion economic loss. To address this problem, we have developed a three-dimensional tissue imaging and digital analysis approach which allows for the complete characterization of sentinel lymph node biopsy tissues and the identification of isolated tumor cells and micro- metastases that are commonly missed with traditional histopathology. We have demonstrated through our preliminary studies that using this approach we can identify these cancer cells in tissues that were previously characterized as node negative and that we can differentiate true negative from false negative samples. The focus of this project is to take this approach and transform it into a CLIA diagnostic assay that can be offered as a laboratory developed test to patients with negative sentinel lymph node biopsies at the conclusion of this Phase II work. Initially, the test will be offered following traditional tissue evaluation but eventually could be used as a primary diagnostic approach for all melanoma sentinel lymph node biopsy tissues. The development of this assay will be completed through conducting a retrospective clinical study in partnership with Cedars-Sinai with archived negative sentinel lymph node biopsy tissue blocks where the approach will be used to characterize these samples in their entirety. Through this study, we will demonstrate the accuracy, specificity and sensitivity of the test and will be able to quantify the extent to which it reduces the incidence of false negatives in the characterization of sentinel lymph node biopsies. Through the execution of this clinical study, we will build a statistical model that will threshold samples based upon their underlying three- dimensional features (e.g. total number of cancer cells, cancer cell aggregate volume, density of cancer cells) and classify them as ‘no metastases present’ (i.e. true negative) or ‘metastases present’ (i.e. true positive). Furthermore, the software we develop will for positive samples describe where a section should be taken from the sample for confirmation by a Visikol pathologist using traditional histopathology such that the report from the assay fits into the traditional classification paradigm. Lastly, we will transform our digital pathology software analysis approach into a 21 CFR part 11 compliant application which will be required at the conclusion of the project to start offering the assay as a CLIA test. To support this approach and to allow for the launch of a laboratory developed test, we will also build the validation documentation package and associated tests required to demonstrate the range, specificity, reproducibility, accuracy and precision of the assay. Therefore, at the conclusion of this project we will have built and validated an assay which can start to be offered to patients following the build-out of a CLIA lab and staffing with clinical personnel.

概括 该阶段项目的目的是改善黑色素瘤患者的结局和生存率 减少遗漏转移性疾病的假阴性前哨淋巴结活检的数量 通过常规的组织加工和组织学表征。据估计有4200名患者是 在美国每年以这种方式误诊，导致更保守的治疗方案 这些患者比有效治疗其疾病所需要的。未接受的患者的结果 适当的治疗方案是，他们的生存期降低了三年，这是一种质量 调整后的生活年度基础转化为12亿美元的经济损失。为了解决这个问题，我们有 开发了三维组织成像和数字分析方法，该方法允许完整 哨兵淋巴结活检组织的表征以及分离的肿瘤细胞和微观的鉴定 传统组织病理学通常会错过的转移。我们已经通过我们的 初步研究，使用这种方法，我们可以在组织中识别这些癌细胞 以节点为阴性为特征，我们可以将真实负面样本与假阴性样本区分开。 该项目的重点是采用这种方法，并将其转换为CLIA诊断测定法 作为实验室开发的测试，可在结论中为哨兵淋巴结活检的患者进行测试 这二阶段的工作。最初，在传统的组织评估后将提供测试，但有时可能 用作所有黑色素瘤淋巴结活检组织的主要诊断方法。这 该评估的开发将通过进行回顾性临床研究来完成 与雪松西奈具有存档的阴性前哨淋巴结活检组织块，该组织将是该方法 用于整体表征这些样品。通过这项研究，我们将证明准确性 测试的特异性和灵敏度，并将能够量化其减少事件的程度 哨兵淋巴结活检表征的假阴性。通过执行此临床 研究，我们将建立一个统计模型，该模型将根据其基本的三个 维度特征（例如癌细胞的总数，癌细胞骨料的体积，癌细胞的密度） 并将它们归类为“不存在的转移”（即真为负）或“存在的转移”（即真正的积极）。 此外，我们为正样本开发的软件将描述应从何处取出一个部分 Visikol病理学家使用传统的组织病理学确认样本，以便 该测定法符合传统的分类范式。最后，我们将改变我们的数字病理软件 分析方法对21 CFR第11部分符合性应用程序，将在结论结束时需要 项目开始作为CLIA测试提供分析。支持这种方法并允许推出 实验室开发了测试，我们还将构建验证文档软件包和相关测试 需要证明测定的范围，特异性，可重复性，准确性和精度。所以， 在该项目的结论结束时，我们将构建和验证一个可以开始提供给 建立CLIA实验室并用临床人员配备后，患者。