Background: Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein–Barr virus DNA, are not sensitive to locoregional recurrence. Methods: We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n= 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score. Results: Based on Kaplan–Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p< 0.001). This finding was also found in distant metastasis-free survival (p< 0.001), progression-free survival (p< 0.001), and regional recurrence-free survival (p< 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13–2.19,p< 0.05) and multivariate analysis (HR:1.59, CI: 1.11–2.28,p< 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence. Conclusion: The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.
背景：鼻咽癌（NPC）在初次治疗后，局部区域复发率约为10%至50%。然而，目前鼻咽癌的主要预后标志物——分期和血浆EB病毒DNA——对局部区域复发的敏感性不足。方法：我们收集了来自中山大学肿瘤防治中心的367例鼻咽癌患者的385张苏木精-伊红（H&E）染色切片全视野数字图像（WSIs）。我们开发了一种深度学习算法，用于检测WSIs中的肿瘤细胞核和淋巴细胞核，随后通过基于密度的聚类方法，将肿瘤浸润淋巴细胞（TILs）量化为12个评分。然后，我们使用随机生存森林模型对TILs进行训练，生成风险评分。结果：基于Kaplan-Meier分析，所提出的方法在局部区域复发的验证集中能够显著区分低风险和高风险鼻咽癌病例（p<0.001）。这一发现在无远处转移生存期（p<0.001）、无进展生存期（p<0.001）和无区域复发生存期（p<0.05）中也得到了验证。此外，在单变量分析（HR：1.58，CI：1.13–2.19，p<0.05）和多变量分析（HR：1.59，CI：1.11–2.28，p<0.05）中，我们的方法也显示出对局部区域复发具有显著的预后价值。结论：所提出的新型数字标志物有望用于辅助鼻咽癌的治疗决策。
肿瘤(癌症)患者之家