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Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

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收录情况： ◇ SCIE

作者：
Pan Xipeng[1,2,3,4] * ; Lin Huan[1,3,5] * ; Han Chu[1,3] * ; Feng Zhengyun[4] * ; Wang Yumeng[4] ; Lin Jiatai[1,3] ; Qiu Bingjiang[1,2,3] ; Yan Lixu[6] ; Li Bingbing[7] ; Xu Zeyan[1,3,5] ; Wang Zhizhen[4] ; Zhao Ke[1,2,3] ; Liu Zhenbing[4] ; Liang Changhong[1,3] ; Chen Xin[8] ; Li Zhenhui[2,3,9] ; Cui Yanfen[1,2,3,10] ; Lu Cheng[1,3] ; Liu Zaiyi[1,3] ;
机构： [1]Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China. [2]Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China. [3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. [4]School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China. [5]School of Medicine, South China University of Technology, Guangzhou 510006, China. [6]Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. [7]Department of Pathology, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), 49 Dagong Road, Ganzhou 341000, China. [8]Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China. [9]Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China. [10]Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China.
出处：
DOI： 10.1016/j.isci.2022.105605
ISSN：

摘要：
A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.© 2022 The Authors.

基金：
This work was funded by the Key Area Research and Development Program of Guangdong Province, China (No. 2021B0101420006); National Science Fund for Distinguished Young Scholars of China (No. 81925023); National Science Foundation for Young Scientists of China (Nos. 62002082, 62102103, 82001986); National Natural Science Foundation of China (Nos. 82272084, 82272075, 82072090, 61866009); China Postdoctoral Science Foundation (Nos. 2021M690753, 2021M700897); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105); Guangxi Natural Science Foundation (Nos. 2020GXNSFBA238014, 2020GXNSFAA297061); Guangxi Key Research and Development Project (No. AB21220037); Yunnan digitalization, development and application of biotic resource (No. 202002AA100007); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (No. 202101AW070001); Yunnan Fundamental Research Projects (No. 202201AT070010); Innovation Team of Kunming Medical University (No. CXTD202110); Science and technology Projects in Guangzhou (Nos. 202201020001, 202201010513); and Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345).
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外文
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出版当年[2022]版：
大类 | 2 区 综合性期刊
小类 | 2 区 综合性期刊
最新[2023]版：
大类 | 2 区 综合性期刊
小类 | 2 区 综合性期刊
JCR分区：
出版当年[2021]版：
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

第一作者：
第一作者机构： [1]Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China. [2]Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China. [3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. [4]School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
共同第一作者：
通讯作者：
通讯机构： [1]Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China. [2]Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China. [3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. [9]Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China. [10]Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China.
推荐引用方式(GB/T 7714)：
Pan Xipeng,Lin Huan,Han Chu,et al.Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma[J].ISCIENCE.2022,25(12):doi:10.1016/j.isci.2022.105605.
APA：
Pan Xipeng,Lin Huan,Han Chu,Feng Zhengyun,Wang Yumeng...&Liu Zaiyi.(2022).Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma.ISCIENCE,25,(12)
MLA：
Pan Xipeng,et al."Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma".ISCIENCE 25..12(2022)

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