Rapid Breast Cancer Diagnosis in Low and Middle Income Countries

低收入和中等收入国家面临的最大癌症挑战之一是缺乏 准确且经济实惠的细胞和分子诊断，这对于使 治疗决策，特别是对乳腺癌的治疗。随着低成本超声波使用的增加，它已经 使用fiNe针(fiNe针抽吸物，FNA)可以很容易地对可疑的乳房病变进行采样。 然而，在许多LMIC环境中，这种试件的制作通常是不可能的。要解决这些障碍 在诊断方面，源自人工智能和阿基里语(斯瓦希里语中的智能)的艾基里语寻求使同一天 使用低成本的自动化系统在护理点诊断乳腺癌。爱奇里系统是一种 高度先进的独立诊断平台，能够自动诊断癌症和受体亚单位 以较低的成本(集成硬件800美元，每次测试5-10美元)近乎实时地(1小时)打字。建房 在我们对人体样本进行初步开发和成功的临床验证后，这一阶段的目标是 应用程序是将Aikii技术推进到fi，从而显著提高其在资源有限的环境中的可用性。 具体来说，我们建议i)通过加入定制设计的一次性产品来升级Aikii技术 用于现场样品处理的墨盒和用于自动分析的深度学习算法(目标1)和2 通过肯尼亚的验证研究，评估升级后的系统在LMIC WorkflOWS中的性能(n= 30)(目标2)。我们将认为第一阶段的项目是成功的，当我们可以证明fi领域优化的艾基里 系统准确可靠地检测乳腺癌和人类FNAs中的受体状态 公认的黄金标准。第一阶段的成功完成将导致第二阶段的扩大申请 制造和一项更大的、多地点的临床验证研究。这一平台可能会改变治疗模式 对于全球乳腺癌患者，并允许适当使用化疗和抗雌激素 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.