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Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification

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机构: [1]School of Medicine, South China University of Technology, Guangzhou510006, China [2]Department of Radiology, Guangdong Provincial People’sHospital, Guangdong Academy of Medical Sciences, Guangzhou 510080,China [3]Guangdong Provincial Key Laboratory of Artificial Intelligencein Medical Image Analysis and Application, Guangdong Provincial People’sHospital, Guangdong Academy of Medical Sciences, Guangzhou 510080,China [4]Guangdong Cardiovascular Institute, Guangzhou 510080, China [5]School of Computer Science and Information Security, Guilin Universityof Electronic Technology, Guilin 541004, China [6]Department of Pathology,Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China [7]Department of Epidemiology and HealthStatistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, XiangyaSchool of Public Health, Central South University, Changsha 410078, China [8]Department of Pathology, The Third Affiliated Hospital of Kunming MedicalUniversity, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118,China [9]The Second School of Clinical Medicine, Southern Medical University,Guangzhou 510515, China [10]Department of Radiology, Guangzhou FirstPeople’s Hospital, Guangzhou 510180, China [11]First Department of ThoracicSurgery, The Third Affiliated Hospital of Kunming Medical University, YunnanCancer Hospital, Yunnan Cancer Center, Kunming 650118, China [12]Departmentof Radiology, The Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
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关键词: Non-small-cell lung cancer (NSCLC) Whole-slide image Immunohistochemistry (IHC) Tumour immune microenvironment Prognosis prediction

摘要:
Background High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. Methods We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3(+) and CD8(+) T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system "I-score" based on the automated assessed cell density. Results A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33-0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36-0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). Conclusion This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.

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出版当年[2022]版:
大类 | 2 区 医学
小类 | 2 区 医学:研究与实验
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大类 | 2 区 医学
小类 | 2 区 医学:研究与实验
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出版当年[2021]版:
Q1 MEDICINE, RESEARCH & EXPERIMENTAL
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Q1 MEDICINE, RESEARCH & EXPERIMENTAL

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第一作者机构: [1]School of Medicine, South China University of Technology, Guangzhou510006, China [2]Department of Radiology, Guangdong Provincial People’sHospital, Guangdong Academy of Medical Sciences, Guangzhou 510080,China
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通讯机构: [1]School of Medicine, South China University of Technology, Guangzhou510006, China [2]Department of Radiology, Guangdong Provincial People’sHospital, Guangdong Academy of Medical Sciences, Guangzhou 510080,China [3]Guangdong Provincial Key Laboratory of Artificial Intelligencein Medical Image Analysis and Application, Guangdong Provincial People’sHospital, Guangdong Academy of Medical Sciences, Guangzhou 510080,China [4]Guangdong Cardiovascular Institute, Guangzhou 510080, China [12]Departmentof Radiology, The Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
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