Pritzker School of Medicine, University of Chicago, Chicago, IL, USA.
Geisinger Cancer Institute, Danville, PA, USA.
EBioMedicine. 2024 Sep;107:105276. doi: 10.1016/j.ebiom.2024.105276. Epub 2024 Aug 27.
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).
在资源匮乏的环境中提供公平的全球癌症护理，部署和获取最先进的精准医学技术仍然是一个基本挑战。近年来，数字病理学的扩展及其与诊断人工智能算法的潜在接口为普及个性化医疗提供了机会。然而，目前的数字病理学工作站成本高达数千至数十万美元。随着许多低收入和中等收入国家癌症发病率的上升，验证和实施低成本自动化诊断工具对于帮助医疗保健提供者应对不断增长的癌症负担至关重要。
在这里，我们描述了一个低成本（230 美元）的数字幻灯片捕获和计算分析工作站，由开源组件组成。我们分析了使用这种开源工作站捕获的病理学图像与使用常见的、成本高得多的硬件捕获的图像的深度学习模型的预测性能。验证研究评估了三种不同数据集和预测模型上的模型性能：头颈部鳞状细胞癌（HPV 阳性与 HPV 阴性）、肺癌（腺癌与鳞状细胞癌）和乳腺癌（浸润性导管癌与浸润性小叶癌）。
与传统的病理学图像采集方法相比，使用开源工作站进行低成本数字幻灯片采集和分析，包括低成本显微镜设备，与用于乳腺癌、肺癌和 HNSCC 分类的模型性能具有相当的准确性。在患者水平的分析中，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。
尽管图像质量下降和低功率计算硬件，但我们仍能保持模型性能，这表明可以大规模降低部署深度学习模型用于数字病理学应用的相关成本。改善对尖端诊断工具的获取可能为减少高收入和低收入地区之间的癌症护理差距提供途径。
本项目的人员支持资金由 NIH/NCIR25-CA240134、NIH/NCIU01-CA243075、NIH/NIDCRR56-DE030958、NIH/NCIR01-CA276652、NIH/NCI-K08-CA283261、NIH/NCI-SOAR25CA240134、SU2C（Stand Up to Cancer）Fanconi Anemia Research Fund - Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant 和欧盟地平线计划（I3LUNG）提供。
