人工智能预测III期结肠癌预后
创作:Lexi 审核:Lexi 2019年12月16日
  • 该人工智能识别病理切片上被染色的CD3和CD8,并量化肿瘤核(TC)和浸润边界(IM)中淋巴细胞密度和表面积;
  • 分析1018位患者数据,发现更差的无复发生存(RFS)与高IM基质面积与高DGMate相关;
  • 更高的CD3+ TC、CD3+ IM、和CD8+ TC密度与更长RFS相关;
  • 方差分析表明CD3+ TC产生的预后值与经典CD3/CD8免疫评分相似;
  • IM基质面积、DGMate、CD3(被称为DGMuneS)评估患者预后时优于免疫评分,且与Cox多变量分析后的患者预后独立相关。
主编推荐语
Lexi
诊断测试(如免疫评分)可预测结肠癌患者的预后,然而使用人工智能(AI)可通过病理切片检测额外的预后标志物。来自Gut上发表的一项最新研究,创建了一个AI系统用于检测CD3和CD8染色切片上的结肠癌、健康黏膜、基质和免疫细胞。该AI系统可自动量化肿瘤核(TC)和浸润边界(IM)中的淋巴细胞密度和表面积,并检测肿瘤细胞内与患者预后相关的数字参数。该AI分析得到的预后值优于经典免疫评分。该研究表明人工智能可辅助病理学家更好地定义III期结肠癌患者预后,从而改善对患者的护理。
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Gut [IF:19.819]

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

人工智能辅助组织分析与免疫浸润评估相结合预测III期结肠癌结局

10.1136/gutjnl-2019-319292

2019-11-28, Article

Abstract & Authors:展开

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Objective : Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.
Design : We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.
Results : Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated ‘DGMuneS’, outperformed Immunoscore when used in estimating patients’ prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.
Conclusion : These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients’ prognosis.

First Authors:
Cynthia Reichling

Correspondence Authors:
François Ghiringhelli

All Authors:
Cynthia Reichling,Julien Taieb,Valentin Derangere,Quentin Klopfenstein,Karine Le Malicot,Jean-Marc Gornet,Hakim Becheur,Francis Fein,Oana Cojocarasu,Marie Christine Kaminsky,Jean Paul Lagasse,Dominique Luet,Suzanne Nguyen,Pierre-Luc Etienne,Mohamed Gasmi,Andre Vanoli,Hervé Perrier,Pierre Laurent Puig,Jean Francois Emile,Come Lepage,François Ghiringhelli

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