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人工智能辅助分析结直肠癌浸润边缘的肿瘤免疫相互作用。

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer.

机构信息

The Second School of Clinical Medicine, Southern Medical University, Guangzhou, P.R. China.

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, P.R. China.

出版信息

Ann Med. 2023 Dec;55(1):2215541. doi: 10.1080/07853890.2023.2215541.

Abstract

BACKGROUND

In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification.

MATERIALS AND METHODS

The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3 T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery ( = 347) and a validation ( = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival.

RESULTS

The TGP-I score (trichotomy) was an independent prognostic factor, with higher TGP-I score associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22-5.90;  < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84-18.20;  = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters.

CONCLUSIONS

This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I-III CRC patients.Key messagesA new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin.TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival.TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.

摘要

背景

在结直肠癌(CRC)中,肿瘤侵袭和肿瘤侵袭边缘(IM)的免疫分析都与患者的预后显著相关,但传统上这两者是分开报告的。我们提出了一种新的评分系统,即 TGP-I 评分,用于评估肿瘤生长模式(TGP)和肿瘤浸润淋巴细胞在 IM 处的关联和相互作用,并预测其对 CRC 患者分层的预后有效性。

材料和方法

在苏木精和伊红染色的全玻片图像中评估 TGP 的类型。使用深度学习方法自动量化免疫组化染色载玻片上 IM 处的 CD3 T 细胞密度。一个发现( = 347)和一个验证( = 132)队列用于评估 TGP-I 评分对总生存的预后价值。

结果

TGP-I 评分(三分法)是一个独立的预后因素,较高的 TGP-I 评分与发现队列(高 vs. 低的未调整危险比 [HR] 3.62,95%置信区间 [CI] 2.22-5.90; < 0.001)和验证队列(高 vs. 低的未调整 HR 5.79,95%CI 1.84-18.20; = 0.003)的预后不良相关。分析了每个参数对预测生存的相对贡献。与肿瘤-淋巴结-转移分期(31.2%对 32.9%)相比,TGP-I 评分具有同等重要性,并且比其他临床参数更强。

结论

这种自动化工作流程和提出的 TGP-I 评分可以进一步提供准确的预后分层,并有可能为支持 I-III 期 CRC 患者的临床决策提供帮助。

关键信息

提出了一种新的评分系统,即 TGP-I 评分,用于评估肿瘤侵袭边缘处的 TGP 和 TIL 之间的关联和相互作用。TGP-I 评分可作为 CRC 患者预后的独立预测因子,评分较高与生存较差相关。TGP-I 评分与肿瘤-淋巴结-转移分期的重要性相当,且比其他临床参数更强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10d6/10210840/407947a5c922/IANN_A_2215541_F0001_C.jpg

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