开发一种低成本、开源、在地制造的自动化组织病理评估工作站及深度学习计算流程

在资源有限的环境中，部署和获取最先进的精准医疗技术仍然是实现公平全球癌症护理的核心挑战。近年来数字病理的发展及其与诊断人工智能算法的潜在结合，为普及个性化医疗提供了契机。然而，目前的数字病理工作站成本高昂，达数千至数十万美元。随着许多低中收入国家癌症发病率的上升，验证和应用低成本自动化诊断工具对帮助医疗提供者管理日益增长的癌症负担至关重要。本研究介绍了一款由开源组件组成的低成本（$230）数字切片采集与分析工作站。我们比较了使用该开源工作站与使用昂贵硬件所采集的病理图像在深度学习模型中的预测性能。验证研究涵盖三类不同数据集和预测模型：头颈部鳞状细胞癌（HPV阳性与阴性）、肺癌（腺癌与鳞癌）和乳腺癌（浸润性导管癌与浸润性小叶癌）。结果显示，与传统图像采集方法相比，低成本的数字切片采集和分析（包括低成本显微镜设备）在乳腺癌、肺癌和HNSCC分类中的模型性能具有可比性。在患者层面，HNSCC HPV状态预测的AUROC为0.84，肺癌亚型预测为1.0，乳腺癌分类为0.80。即使在降低图像质量和采用低功耗计算硬件的情况下，模型性能仍得以维持，表明大幅降低深度学习模型在数字病理中的部署成本成为可能。改善诊断工具的普及，有望缩小高低收入地区在癌症护理中的差距。本项目的资金支持包括NIH/NCIR25-CA240134、NIH/NCIU01-CA243075、NIH/NIDCRR56-DE030958、NIH/NCIR01-CA276652、NIH/NCIK08-CA283261、NIH/NCI-SOAR25CA240134、SU2C（抗癌基金会）范科尼贫血研究基金会-法拉·费弗特基金会头颈癌研究团队，以及欧盟Horizon项目（I3LUNG）。

Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its potential interface with diagnostic artificial intelligence algorithms provides an opportunity to democratize access to personalized medicine. Current digital pathology workstations, however, cost thousands to hundreds of thousands of dollars. As cancer incidence rises in many low- and middle-income countries, the validation and implementation of low-cost automated diagnostic tools will be crucial to helping healthcare providers manage the growing burden of cancer.Here we describe a low-cost ($230) workstation for digital slide capture and computational analysis composed of open-source components. We analyze the predictive performance of deep learning models when they are used to evaluate pathology images captured using this open-source workstation versus images captured using common, significantly more expensive hardware. Validation studies assessed model performance on three distinct datasets and predictive models: head and neck squamous cell carcinoma (HPV positive versus HPV negative), lung cancer (adenocarcinoma versus squamous cell carcinoma), and breast cancer (invasive ductal carcinoma versus invasive lobular carcinoma).When compared to traditional pathology image capture methods, low-cost digital slide capture and analysis with the open-source workstation, including the low-cost microscope device, was associated with model performance of comparable accuracy for breast, lung, and HNSCC classification. At the patient level of analysis, AUROC was 0.84 for HNSCC HPV status prediction, 1.0 for lung cancer subtype prediction, and 0.80 for breast cancer classification.Our ability to maintain model performance despite decreased image quality and low-power computational hardware demonstrates that it is feasible to massively reduce costs associated with deploying deep learning models for digital pathology applications. Improving access to cutting-edge diagnostic tools may provide an avenue for reducing disparities in cancer care between high- and low-income regions.Funding for this project including personnel support was provided via grants from NIH/NCIR25-CA240134, NIH/NCIU01-CA243075, NIH/NIDCRR56-DE030958, NIH/NCIR01-CA276652, NIH/NCIK08-CA283261, NIH/NCI-SOAR25CA240134, SU2C (Stand Up to Cancer) Fanconi Anemia Research Fund - Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant, and the European UnionHorizon Program (I3LUNG).