基于肿瘤浸润淋巴细胞的人工智能风险评分预测鼻咽癌的局部区域无复发生存率

AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma.

作者信息

Wibawa Made Satria, Zhou Jia-Yu, Wang Ruoyu, Huang Ying-Ying, Zhan Zejiang, Chen Xi, Lv Xing, Young Lawrence S, Rajpoot Nasir

机构信息

Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.

State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.

出版信息

Cancers (Basel). 2023 Dec 10;15(24):5789. doi: 10.3390/cancers15245789.

DOI:10.3390/cancers15245789

PMID:38136336

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10742296/

Abstract

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 ( = 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 ( < 0.001). This finding was also found in distant metastasis-free survival ( < 0.001), progression-free survival ( < 0.001), and regional recurrence-free survival ( < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, < 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.

摘要

背景

鼻咽癌（NPC）原发治疗后局部区域复发率为10%至50%。然而，目前鼻咽癌的主要预后标志物，即分期和血浆EB病毒DNA，对局部区域复发并不敏感。

方法

我们从中山大学肿瘤防治中心收集了385张苏木精和伊红（H&E）染色的鼻咽癌切片的全切片图像（WSIs）（n = 367例）。我们开发了一种深度学习算法来检测WSIs中的肿瘤细胞核和淋巴细胞核，然后通过基于密度的聚类将肿瘤浸润淋巴细胞（TILs）量化为12个分数。然后在TILs上训练随机生存森林模型以生成风险评分。

结果

基于Kaplan-Meier分析，所提出的方法能够在局部区域复发的验证集中对低风险和高风险鼻咽癌病例进行分层，结果具有统计学意义（P < 0.001）。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）中也发现了这一结果。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，我们还发现我们的方法对局部区域复发具有很强的预后价值。