Rapid Breast Cancer Diagnosis in Low and Middle Income Countries

低收入和中等收入国家（LMIC）面临的最大癌症挑战之一是缺乏获得 准确和负担得起的细胞和分子诊断，这是必不可少的，使知情 治疗决定，特别是乳腺癌。随着低成本超声的使用增加， 使用细针（细针抽吸物，FNA）对可疑乳腺病变进行轻松采样成为可能。 然而，在许多LMIC环境中，此类标本的后处理通常是不可能的。为了克服这些障碍， 诊断，Aikili-来自A.I.和Akili（斯瓦希里语的情报）-寻求使同一天， 使用低成本的自动化系统在护理点诊断乳腺癌。Aikili系统是 高度先进的独立诊断平台，能够自动化癌症诊断和受体亚单位， 输入接近实时（< 1小时），成本低（集成硬件<800美元，每次测试5 -10美元）。建筑 在我们对人体样本进行初步开发和成功的临床验证后， Aikili应用的目标是推进Aikili技术，以显著提高其在资源有限环境中的可用性。 具体而言，我们建议i）通过结合定制设计的一次性产品来升级Aikili技术 用于现场样品处理和用于自动分析的深度学习算法的盒（Aim 1），以及ii） 通过在肯尼亚进行的验证研究，评估升级后的系统在LMIC工作流程中的性能（n = 30)（目标2）。当我们能够证明现场优化的Aikili 系统准确可靠地检测乳腺癌和人类FNA中的受体状态， 接受黄金标准。第一阶段的成功完成将导致第二阶段的应用， 制造和更大的多中心临床验证研究。这个平台可能会改变治疗模式 为全球乳腺癌患者提供治疗，并使化疗和抗雌激素在 供应有限。

One of the biggest cancer challenges in low- and middle-income countries (LMICs) is the lack of access to accurate and affordable cellular and molecular diagnostics, which are essential for making informed therapeutic decisions, in particular for breast cancer. With the increased use of low cost ultrasound, it has become possible to readily sample suspicious breast lesions with fine needles (fine needle aspirates, FNA). However, the workup of such specimens is often impossible in many LMIC settings. To address these barriers to diagnosis, Aikili—derived from A.I. and Akili (intelligence in Kiswahili)—seeks to enable the same-day diagnosis of breast cancer at the point-of-care using a low-cost, automated system. The Aikili system is a highly advanced stand-alone diagnostic platform capable of automated cancer diagnosis and receptor sub- typing in near real-time (< 1 hour), at a low cost (<$800 for integrated hardware and $5-10 per test). Building upon our initial development and successful clinical validation of human samples, the goal of this Phase I application is to advance the Aikili technology to significantly improve its usability in resource-limited settings. Specifically, we propose to i) upgrade Aikili technology by incorporating a custom-designed disposable cartridge for onsite sample processing and deep learning algorithms for automatic analysis (Aim 1), and ii) evaluate the performance of the upgraded system in LMIC workflows through a validation study in Kenya (n = 30) (Aim 2). We will consider the Phase I project successful when we can show that the field-optimized Aikili system accurately and reliably detects breast cancer and receptor status in human FNAs compared to accepted gold standards. Successful completion of Phase I would lead to a Phase II application for scale-up of manufacturing and a larger, multi-site clinical validation study. This platform may alter therapeutic paradigms for breast cancer patients in globally and enable appropriate use of chemotherapies and anti-estrogens in limited supply.