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: 10458057
• 负责人: WALTER Tsong LEE
• 金额: $ 18.76万
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458057
• 关键词: Archives; Area; Asian; 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; Resource-limited setting; Resources; Risk; Risk Factors; Role; Sampling; Sensitivity and Specificity; Services; Slide; Specimen; Stains; Stress; System; Tanzania; Testing; Thyroid Gland; Thyroid Nodule; Time; Training; Triage; 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; ultrasound

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学专业知识和服务严重不足， 有限公司病理学对诊断和临床决策尤其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增加。一 甲状腺结节临床评估的基本部分是超声引导下细针穿刺活检 （FNAB）。不幸的是，这种细胞病理学的专业知识是稀缺的LMIC和许多结节手术切除 进行诊断。由于90-95%的结节没有恶性肿瘤，许多患者接受不必要的治疗。 手术带来了相关的风险、财务成本，并对已经有限的资源造成了压力。关键是什么 需要一种方法来提供细胞病理学专业知识，以解释LMIC中的甲状腺FNAB。 该提案的总体目标是将我们的ML方法实施到低资源设置中， FNAB标本的细胞病理学分析准确及时。本R21/33提案侧重于司法协助 利用智能手机进行适应，以实现低收入、中等收入国家（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肿瘤的概率 与经过培训的细胞病理学家相当的FNAB。 我们的目标是：R21：采用智能手机方法捕获细胞病理学图像进行MLA分析。 不同国际机构使用的存档FNAB样本将用于培训和调整司法协助， 这些图像。此外，我们还将培训LMIC的当地人员使用这种新设置进行图像捕获。的 该阶段的结果将是使用具有可比灵敏度的智能手机图像的适应性MLA， 原始MLA的特性。R33：在LMIC临床环境中实施智能手机MLA方法。 将在坦桑尼亚和越南的高等教育中心实施这一方法。 获得的FNAB样本将进行标准护理病理学评估，并使用 MLA协议。将当地细胞病理学家评估与MLA分析进行比较。观察指标 包括载玻片MLA评估、技术载玻片审查和图像采集、与病理学的一致性 在美国和LMIC的MLA和专业知识之间。 预期的结果将导致一种具有成本效益的方法来实施甲状腺FNAB的MLA， 在LMIC。这种方法不仅有助于甲状腺癌的早期诊断， 通过有效地识别那些需要手术的人和那些不需要手术的人，

项目成果

Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning.

• DOI: 10.1016/j.modpat.2023.100129
• 发表时间: 2023-02
• 期刊: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
• 作者: Serge Assaad;D. Dov;Richard Davis;S. Kovalsky;Walter T. Lee;R. Kahmke;Daniel J. Rocke;Jonathan Cohen;Ricardo Henao;L. Carin;D. Range