An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.

用于结直肠癌肿瘤芽检测和风险分层的集成深度学习模型。

• 批准号: 10564824
• 负责人: Wei Chen
• 金额: $ 54.37万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10564824
• 关键词: Agreement; American; American Cancer Society; Biological; Cancer Etiology; Cell Adhesion; Cell Nucleus; Cell Polarity; Cells; Cessation of life; Clinical; Clinical Management; Color; Colorectal; Colorectal Cancer; Compensation; Computer Assisted; Computer software; Computer-Assisted Image Analysis; Consensus; Consumption; Cytokeratin; Decision Making; Detection; Diagnostic; Disease; Disease-Free Survival; Evaluation; Exclusion; Future; Guidelines; Head and Neck Squamous Cell Carcinoma; Health; Hematoxylin and Eosin Staining Method; Human; Image; Image Analysis; Incidence; International; Interobserver Variability; Large Intestine Carcinoma; Learning; Learning Module; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of esophagus; Manuals; Mathematics; Metastatic Neoplasm to Lymph Nodes; Methods; Microsatellite Instability; Mission; Modality; Modeling; Morphology; Neoplasm Metastasis; Outcome; Pathologic; Pathologist; Pathology; Pathology Report; Patient Care; Patient Selection; Patient-Focused Outcomes; Patients; Performance; Play; Prevalence; Prognostic Factor; Progression-Free Survivals; Protocols documentation; Public Health; Qualifying; Recommendation; Regional Anatomy; Reproducibility; Research; Role; Scientist; Shapes; Slide; Software Tools; Specimen; Squamous cell carcinoma; Stains; Standardization; Statistical Data Interpretation; System; Systems Analysis; Techniques; Time; Training; Tumor stage; United States; United States National Institutes of Health; Variant; Visual; Woman; Work; automated image analysis; cancer risk; cancer type; chemotherapy; cohort; college; colon cancer patients; colorectal cancer risk; colorectal cancer treatment; computer aided detection; deep learning; deep learning algorithm; deep learning model; differential geometry; disorder risk; epithelial to mesenchymal transition; experience; image registration; improved; improved outcome; innovation; lifetime risk; malignant breast neoplasm; malignant stomach neoplasm; men; mortality; neoplastic cell; novel; novel strategies; outcome prediction; patient stratification; personalized cancer therapy; quantitative imaging; risk stratification; symposium; theories; tool; tumor; whole slide imaging

PROJECT ABSTRACT Colorectal cancer (CRC) is the fourth most common cancer, and the second leading cause of cancer death in the United States, with an estimated incidence of 151,030 new cases in 2022. According to the American Cancer Society, the lifetime risk of developing colorectal cancer is 1 in 23 for men and 1 in 25 for women. Tumor budding is a prognostic factor in colorectal cancer with potential to risk stratify patients and possibly guide treatment decisions. It is defined as the presence of a single tumor cell or a cell cluster consisting of fewer than five tumor cells at the invasive tumor front. Unfortunately, tumor budding is not routinely disclosed in pathology reports due to lack of reproducible methods in identifying tumor buds from H&E slides. The prevalence, mortality, and risk of colorectal cancer as well as the potential of tumor budding as a prognostic factor necessitate an accurate, easy- to-use, reproducible system to identify tumor budding. We aim to develop a computer-aided image analysis system to standardize the quantitative criteria used to define tumor budding from H&E slides. In addition to identifying tumor buds, the system will correlate tumor buds with several outcomes (microsatellite instability status, overall survival, progression free survival, and disease free survival). As part of the proposed computer- aided image analysis system, we will first develop a sophisticated method for color deconvolution to compensate for color variations. This will be followed by deformable image registration and deep learning modules to differentiate tumor from non-tumor regions. The study will show that machines can be trained using deep learning to identify different anatomical regions within H&E slides of colorectal patients. From thereon, we will rely on scale-space theory and alpha-shapes to identify tumor buds and hotspots. We will use mathematical morphology and differential geometry to extract visually meaningful imaging features from tumor buds and hotspots. We will explore the potential of these imaging features along with features produced by our unsupervised multiple instance learning in predicting outcomes. The proposed research will help identify the association of tumor budding to colorectal cancer outcomes. The model will be subjected to rigorous statistical analysis for accuracy and reproducibility. The project will result in innovative software tools that facilitate the selection for personalized cancer therapies for colorectal patients.

项目摘要 结直肠癌（CRC）是第四大常见癌症，也是美国癌症死亡的第二大原因。 美国，估计2022年新发病例为151,030例。根据美国癌症 在社会中，男性患结直肠癌的终生风险为1/23，女性为1/25。肿瘤出芽 是结直肠癌预后因素，有可能对患者进行风险分层，并可能指导治疗 决策它被定义为存在单个肿瘤细胞或由少于五个肿瘤细胞组成的细胞簇。 在侵袭性肿瘤前沿的细胞。不幸的是，肿瘤出芽在病理报告中并没有常规披露， 缺乏从H&E切片中鉴定肿瘤芽的可重复方法。患病率、死亡率和风险 结肠直肠癌以及肿瘤出芽作为预后因素的潜力需要准确，简单， 使用可重复的系统来识别肿瘤萌芽。我们的目标是开发一种计算机辅助图像分析 该系统用于标准化用于定义来自H&E载玻片的肿瘤出芽的定量标准。除了 识别肿瘤芽，系统将肿瘤芽与几种结果（微卫星不稳定性）相关联 状态、总生存期、无进展生存期和无疾病生存期）。作为拟议中的计算机的一部分- 辅助图像分析系统，我们将首先制定一个复杂的方法，颜色反卷积补偿 颜色变化。接下来将是可变形图像配准和深度学习模块， 区分肿瘤与非肿瘤区域。这项研究将表明，机器可以使用深度学习进行训练。 以识别结直肠患者的H&E载玻片内的不同解剖区域。从那时起，我们将依靠 尺度空间理论和α形状来识别肿瘤芽和热点。我们将使用数学形态学 和微分几何来从肿瘤芽和热点提取视觉上有意义的成像特征。我们将 探索这些成像特征的潜力，沿着我们的无监督多重成像产生的特征 实例学习在预测结果中的应用这项拟议中的研究将有助于确定肿瘤 结直肠癌的结果。该模型将接受严格的统计分析，以确保准确性 和再现性。该项目将产生创新的软件工具，便于选择个性化的 结肠直肠癌患者的癌症治疗。