Adapting a machine learning algorithm to predict thyroid cytopathologyin LMIC

采用机器学习算法来预测 LMIC 中的甲状腺细胞病理学

• 批准号: 10269391
• 负责人: WALTER Tsong LEE
• 金额: $ 20.2万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269391
• 关键词: Archives; Area; Asians; Biopsy Specimen; Cellular Phone; Cessation of life; Clinical; Clinical Trials; Clinical assessments; Country; Cytopathology; Data; Diagnosis; Disease; Early Diagnosis; Equipment; Financial cost; Fine needle aspiration biopsy; Funding; Future; Goals; Health; Human Resources; Image; Improve Access; Incidence; Institution; International; Iodine; Laboratories; Machine Learning; Malignant Neoplasms; Malignant neoplasm of thyroid; Methods; Microscope; Nodule; Operative Surgical Procedures; Outcome; Outcome Measure; Pathologist; Pathology; Patient Care; Patients; Phase; Play; Population; Preparation; Probability; Procedures; Protocols documentation; Reporting; Research; Resolution; Resources; Risk; Risk Factors; Role; Sampling; Sensitivity and Specificity; Services; Slide; Specimen; Stains; Stress; System; Tanzania; Testing; Thyroid Gland; Thyroid Nodule; Time; Training; Triage; Ultrasonography; Unnecessary Surgery; Vietnam; base; clinical care; clinical decision-making; cost; cost effective; digital; handheld mobile device; improved; interest; low and middle-income countries; machine learning algorithm; multidisciplinary; prospective; public health relevance; standard of care

Abstract Pathology expertise and services in low and middle income countries (LMIC) are severely inadequate and limited. One area in which pathology is especially critical for diagnosis and clinical decision making is thyroid related disease and cancers. The incidences of thyroid nodules and cancers have increased worldwide. A fundamental part of the clinical assessment for thyroid nodules is ultrasound-guided fine needle aspiration biopsy (FNAB). Unfortunately, this cytopathology expertise is scarce in LMIC and many nodules are surgically removed for diagnosis. Since 90-95% of these nodules are without malignancy, many patients undergo unnecessary surgery with the associated risks, financial costs, and stressing already limited resources. What is critically needed is a means to provide cytopathology expertise to interpret thyroid FNAB in LMIC. The overall goal of this proposal is to implement our ML approach into low resource settings to provide accurate and timely cytopathology analysis of FNAB specimens. This R21/33 proposal focuses on MLA adaptation using smartphones for LMIC (R21) and subsequent capacity building and implementation (R33) through well-established LMIC research partnerships in tertiary centers in Tanzania and Vietnam. We hypothesize that our MLA can use smartphone captured images to assess probability of malignancy in thyroid FNAB that is comparable to trained cytopathologists. Our Aims are: R21: Adaptation of a smartphone approach to capture cytopathology images for MLA analysis. Archived FNAB samples used at different international institutions will be used to train and adapt the MLA with these images. Furthermore, we would train local personnel in LMIC to image capture using this new setup. The results from this phase will be an adapted MLA using smartphone images that has comparable sensitivity and specificity to the original MLA. R33: Implementation of the smartphone MLA approach into LMIC clinical settings. A prospective implementation of this approach in tertiary centers in Tanzania and Vietnam will be conducted. FNAB samples obtained would undergo standard of care pathology assessment as well as be analyzed with the MLA protocol. Local cytopathologist assessment will be compared with the MLA analysis. Outcome measures include MLA assessment of slides, technical slide review and image capture, concordance with pathology between MLA and expertise both in US and LMIC. The expected outcome will lead to a cost-effective method to implement MLA for thyroid FNAB that can be used in LMIC. This approach will not only assist in early diagnosis of thyroid cancers but also improve the utilization of limited resources through effectively identifying those that need surgery from those that do not.

摘要 低收入和中等收入国家的病理专业知识和服务严重不足， 有限的。病理学对诊断和临床决策特别重要的一个领域是甲状腺。 相关疾病和癌症。甲状腺结节和癌症的发病率在世界范围内有所增加。一个 超声引导细针抽吸活检是甲状腺结节临床评估的基础部分。 (FNAB)。不幸的是，这种细胞病理学专业知识在LMIC中很少，许多结节都是通过手术切除的。 用于诊断。由于这些结节中90%-95%没有恶性，许多患者接受了不必要的治疗 手术伴随着相关的风险、财务成本，并强调已经有限的资源。最重要的是 需要一种手段来提供细胞病理学专业知识来解释LMIC中的甲状腺Fnab。 该提案的总体目标是将我们的ML方法实现到低资源环境中，以提供 准确、及时地对FNAB标本进行细胞病理学分析。此R21/33建议书侧重于MLA 使用智能手机适应LMIC(R21)以及随后的能力建设和实施(R33) 通过在坦桑尼亚和越南的高等教育中心建立良好的LMIC研究伙伴关系。我们 假设我们的MLA可以使用智能手机捕获的图像来评估甲状腺恶性病变的可能性 FNAB可与训练有素的细胞病理学家相媲美。 我们的目标是：R21：采用智能手机方法捕获用于MLA分析的细胞病理图像。 不同国际机构使用的存档FNAB样本将用于培训和调整MLA 这些图像。此外，我们将培训当地的LMIC人员使用这一新设置进行图像捕获。这个 此阶段的结果将是使用智能手机图像进行调整的MLA，该图像具有类似的灵敏度和 原始MLA的特异性。R33：将智能手机MLA方法应用于LMIC临床环境。 将在坦桑尼亚和越南的三级中心实施这一方法。 获得的fnab样本将接受标准的护理病理评估，并通过 MLA协议。当地的细胞病理学评估将与MLA分析进行比较。结果衡量标准 包括对幻灯片MLA评估、技术幻灯片审查和图像捕获、与病理学的一致性 在MLA和美国和LMIC的专业知识之间。 预期的结果将导致一种成本效益高的方法来实施可用于甲状腺Fnab的MLA 在LMIC。这种方法不仅有助于甲状腺癌的早期诊断，而且提高了利用率。 通过有效地将需要手术的人与不需要手术的人区分开来，有效地利用有限的资源。