Background: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. The median overall survival time for patients who develop metastasis is approximately one year. In this study, we aim to leverage deep learning (DL) techniques to analyze digital cytopathology images and directly predict the 48 month survival status on a patient level. Methods: Fine-needle aspiration biopsy (FNAB) of the tumor was performed in each patient diagnosed with UM. The cell aspirate was smeared on a glass slide and stained with H&E. Each slide then underwent whole-slide scanning. Within each whole-slide image, regions of interest (ROIs) with UM cells were automatically extracted. Each ROI was converted into super pixels, and the super pixels were automatically detected, segmented and annotated as “tumor cell” or “background” using DL. Cell-level features were extracted from the segmented tumor cells. The cell-level features were aggregated into slide-level features which were learned by a fully connected layer in an artificial neural network, and the patient survival status was predicted directly from the slide-level features. The data were partitioned at the patient level (78% training and 22% testing). Our DL model was trained to perform the binary prediction of yes-versus-no survival by Month 48. The ground truth for patient survival was established via a retrospective chart review. Results: A total of 74 patients were included in this study (43% female; mean age at the time of diagnosis: 61.8 ± 11.6 years), and 207,260 unique ROIs were generated for model training and testing. By Month 48 after diagnosis, 18 patients (24%) died from UM metastasis. Our hold-out test set contained 16 patients, where 6 patients had passed away and 10 patients were alive at Month 48. When using a sensitivity threshold of 80% in predicting UM-specific death by Month 48, our model achieved an overall accuracy of 75%. Within the subgroup of patients who died by Month 48, our model achieved a prediction accuracy of 83%. Of note, one patient in our test set was a clinical surprise, namely death by Month 48 despite having a GEP class 1A tumor, which typically portends a good prognosis. Our model correctly predicted this clinical surprise as well. Conclusions: Our DL model was able to predict the Month 48 survival status directly from digital cytopathology images obtained from FNABs of UM tumors with reasonably robust performance. This approach, if validated prospectively, could serve as an alternative survival prediction tool for patients with UM to whom GEP is not available.
背景：葡萄膜黑色素瘤（UM）是成人中最常见的原发性眼内恶性肿瘤。发生转移的患者中位总生存期约为一年。本研究旨在利用深度学习技术分析数字细胞病理学图像，直接在患者层面预测48个月生存状态。方法：对每位确诊UM的患者进行肿瘤细针穿刺活检。细胞抽吸物涂布于载玻片并进行H&E染色，随后进行全玻片扫描。在全玻片图像中自动提取含UM细胞的感兴趣区域，将每个区域转换为超像素，并运用深度学习技术自动检测、分割超像素，标注为"肿瘤细胞"或"背景"。从分割后的肿瘤细胞中提取细胞级特征，将其聚合为玻片级特征，通过人工神经网络的全连接层进行学习，直接从玻片级特征预测患者生存状态。数据按患者层面划分（78%训练集，22%测试集）。训练深度学习模型进行48个月生存状态的二元预测，患者生存的真实情况通过回顾性病历审查确定。结果：本研究共纳入74例患者（女性占43%；诊断时平均年龄61.8±11.6岁），生成207,260个独立感兴趣区域用于模型训练与测试。诊断后48个月内，18例患者（24%）死于UM转移。留出测试集包含16例患者，其中6例在48个月内死亡，10例存活。在预测48个月内UM特异性死亡时采用80%敏感度阈值，模型总体准确率达75%。在48个月内死亡的患者亚组中，模型预测准确率达83%。值得注意的是，测试集中一例患者出现临床意外情况——虽携带通常预示良好预后的GEP 1A型肿瘤，却在48个月内死亡，而本模型正确预测了这一临床意外。结论：本研究构建的深度学习模型能够直接从UM肿瘤细针穿刺活检获得的数字细胞病理学图像中预测48个月生存状态，且性能较为稳健。该方法若经前瞻性验证，可为无法进行基因表达谱检测的UM患者提供替代性生存预测工具。
肿瘤(癌症)患者之家