Automated FISH Imaging and Analysis for Screening Cervical Cancer

用于筛查宫颈癌的自动化 FISH 成像和分析

• 批准号: 7566142
• 负责人: Hong Liu
• 金额: $ 41.38万
• 依托单位: UNIVERSITY OF OKLAHOMA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-04 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7566142
• 关键词: Abnormal Cell; Agreement; Biological Assay; Biological Markers; Cancer Prognosis; Cell Count; Cell Nucleus; Cells; Cellular Morphology; Cervical; Cervical Cancer Screening; Cervix carcinoma; Characteristics; Chromosomes; Chromosomes, Human, Pair 3; Classification; Clinical; Clinical Research; Color; Computer Assisted; Computer-Assisted Diagnosis; Cytology; Databases; Detection; Development; Diagnostic; Exhibits; Fluorescence; Fluorescent in Situ Hybridization; Future; Genetic; Goals; Human Papilloma Virus Vaccine; Image; Image Analysis; Imaging Device; Imaging Techniques; Interobserver Variability; Interphase; Interphase Cell; Label; Laboratory Technicians; Lead; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Manuals; Methods; Microscopic; Monitor; Noise; Normal Cell; Nuclear; Optics; Pap smear; Pathologist; Pilot Projects; Polysomy; Probability; Process; Public Health; Reader; Research; Resolution; Risk; Sampling; Scanning; Scheme; Screening procedure; Sensitivity and Specificity; Signal Transduction; Slide; Specimen; Spottings; Staging; Staining method; Stains; Statistical Data Interpretation; System; Systems Analysis; Techniques; Technology; Testing; Treatment Efficacy; Woman; Workload; base; cancer diagnosis; clinical application; clinical practice; design; detector; follow-up; image registration; improved; innovation; molecular imaging; mortality; new technology; performance tests; prototype; success; tool; tumor; Abnormal Cell; Agreement; Biological Assay; Biological Markers; Cancer Prognosis; Cell Count; Cell Nucleus; Cells; Cellular Morphology; Cervical; Cervical Cancer Screening; Cervix carcinoma; Characteristics; Chromosomes; Chromosomes, Human, Pair 3; Classification; Clinical; Clinical Research; Color; Computer Assisted; Computer-Assisted Diagnosis; Cytology; Databases; Detection; Development; Diagnostic; Exhibits; Fluorescence; Fluorescent in Situ Hybridization; Future; Genetic; Goals; Human Papilloma Virus Vaccine; Image; Image Analysis; Imaging Device; Imaging Techniques; Interobserver Variability; Interphase; Interphase Cell; Label; Laboratory Technicians; Lead; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Manuals; Methods; Microscopic; Monitor; Noise; Normal Cell; Nuclear; Optics; Pap smear; Pathologist; Pilot Projects; Polysomy; Probability; Process; Public Health; Reader; Research; Resolution; Risk; Sampling; Scanning; Scheme; Screening procedure; Sensitivity and Specificity; Signal Transduction; Slide; Specimen; Spottings; Staging; Staining method; Stains; Statistical Data Interpretation; System; Systems Analysis; Techniques; Technology; Testing; Treatment Efficacy; Woman; Workload; base; cancer diagnosis; clinical application; clinical practice; design; detector; follow-up; image registration; improved; innovation; molecular imaging; mortality; new technology; performance tests; prototype; success; tool; tumor

Fluorescent in situ hybridization (FISH) imaging has been proved as a powerful molecular imaging tool to detect cancers, predict cancer prognosis, and monitor therapy efficacy. However, manual FISH analytic method is very tedious and introducing large inter-reader variability. It is difficult or unpractical to use manual FISH analytic method to detect small number of abnormal chromosome cells from the overwhelming normal cells depicted on one specimen (i.e., screening for early cervical cancer using Pap smear). To solve this difficulty, we proposed to develop and test a prototype system of automated FISH imaging analysis. The system includes both an optical imager and a computer-aided detection (CAD) scheme. Comparing to the currently available techniques, our proposed system provides unique advantages. First, it improves imaging efficiency. Second, it allows CAD processing to detect significantly more analyzable or abnormal cells from a testing specimen than the current manual FISH image analytic method. Our hypothesis is that if all cells and FISH signals depicted on one specimen can be automatically imaged and correctly counted, the probability of missing early cancer sign of abnormal or carcinoma cells can be reduced and thus improving sensitivity of cancer diagnosis. To test this hypothesis, we will conduct a number of specific research tasks. First, we will collect and assemble a preliminary image database that includes both cytology and FISH images for each Pap smear examination. Approximately half of cases depict cervical abnormalities that lead to the development of cancer. Second, we will build a prototype fluorescent image scanning system and test its quality and reliability. Third, we will also develop and preliminarily test the accuracy of our CAD schemes in selecting and segmenting analyzable interphase cells from clinical images, identifying and counting independent FISH labeled biomarker signals (spots), and classifying between the normal and abnormal (cancer) cases. After initial integration of our preliminary imaging system and CAD scheme, we will conduct characterization study to test the performance of the technique. This pilot study will help us to preliminarily assess the feasibility of developing and applying this automated FISH image analysis system and method. The success of this project can generate more follow-up clinical studies that are unable to do in the past.

荧光原位杂交（FISH）成像已被证明是一种强大的分子成像工具，可检测癌症，预测癌症的预后和监测治疗功效。但是，手动鱼类分析方法非常乏味，并且引入了较大的阅读器间变异性。很难使用手动鱼类分析方法来检测一个标本上描绘的压倒性正常细胞的少量异常染色体细胞（即使用Pap Smear筛查早期宫颈癌的筛查）。为了解决这一难度，我们建议开发和测试自动化鱼成像分析的原型系统。该系统既包括光学成像器和计算机辅助检测（CAD）方案。与当前可用的技术相比，我们提出的系统提供了独特的优势。首先，它提高了成像效率。其次，与当前的手动鱼类图像分析方法相比，它允许CAD处理从测试样品中检测到更明显的可分析或异常细胞。我们的假设是，如果可以自动成像并正确计数一个标本上描绘的所有细胞和鱼信号，则可以降低缺失异常或癌细胞癌症标志的可能性，从而提高癌症诊断的敏感性。为了检验这一假设，我们将执行许多特定的研究任务。首先，我们将收集并组装一个初步图像数据库，该数据库包括每个PAP涂片检查的细胞学和鱼类图像。大约一半的病例描绘了导致癌症发展的宫颈异常。其次，我们将构建一个原型荧光图像扫描系统并测试其质量和可靠性。第三，我们还将开发和初步测试我们的CAD方案在从临床图像中选择和分割可分析的间相细胞的准确性，从而识别和计算独立的鱼类标记为具有生物标志物信号（SPOTS）的独立鱼类，并在正常和异常（癌症）病例之间进行分类。在初步整合初步成像系统和CAD方案之后，我们将进行表征研究以测试技术的性能。这项试点研究将帮助我们初步评估开发和应用这种自动化鱼类图像分析系统和方法的可行性。该项目的成功可以产生过去无法进行的更多后续临床研究。