共 50 条
Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer
被引:6
|作者:
Ye, Yunrui
[1
,2
,3
]
Wu, Xiaomei
[4
]
Wang, Huihui
[5
]
Ye, Huifen
[1
]
Zhao, Ke
[2
,3
,8
]
Yao, Su
[6
]
Liu, Zaiyi
[2
]
Zhu, Yaxi
[7
]
Zhang, Qingling
[6
]
Liang, Changhong
[1
,2
,3
,8
]
机构:
[1] Southern Med Univ, Sch Clin Med 2, Guangzhou, Peoples R China
[2] Guangdong Prov Key Lab Artificial Intelligence Med, Guangzhou, Peoples R China
[3] Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Radiol, Guangzhou, Peoples R China
[4] Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Peoples R China
[5] Guangzhou Panyu Cent Hosp, Dept Radiol, Guangzhou, Peoples R China
[6] Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Pathol, 106 Zhongshan Er Rd, Guangzhou 510080, Peoples R China
[7] Sun Yat Sen Univ, Affiliated Hosp 6, Dept Pathol, 26 Erheng Rd, Guangzhou 510655, Peoples R China
[8] Guangdong Prov Peoples Hosp, Guangdong Prov Key Lab Artificial intelligence Med, 106 Zhongshan Er Rd, Guangzhou 510080, Peoples R China
基金:
美国国家科学基金会;
中国国家自然科学基金;
关键词:
Colorectal cancer;
deep learning;
whole-slide images;
tumor growth pattern;
tumor-infiltrating lymphocytes;
PROGNOSTIC CLASSIFICATION;
INFILTRATING LYMPHOCYTES;
BORDER CONFIGURATION;
PROPOSAL;
GROWTH;
CELLS;
D O I:
10.1080/07853890.2023.2215541
中图分类号:
R5 [内科学];
学科分类号:
1002 ;
100201 ;
摘要:
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 (N = 347) and a validation (N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. Results The TGP-I score(3) (trichotomy) was an independent prognostic factor, with higher TGP-I score(3) 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; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84-18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score(3) 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 messages A 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.
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页数:15
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