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广东省人民医院[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
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.
基金:
Key-Area Research and Development
Program of Guangdong Province, China (2021B0101420006), the National
Science Fund for Distinguished Young Scholars of China (81925023), National
Science Foundation for Young Scientists of China (82001986, 62002082,
62102103, 82102034), National Natural Science Foundation of China
(82072090, 82071892), China Postdoctoral Science Foundation (2021M690753,
2021M700897), High-level Hospital Construction Project (DFJH201805,
DFJHBF202105), Applied Basic Research Projects of Yunnan Province, China,
Outstanding Youth Foundation (202101AW070001), and Yunnan digitalization,
Development and Application of Biotic Resource (202002AA100007).
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
共同第一作者:
通讯作者:
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
推荐引用方式(GB/T 7714):
Lin Huan,Pan Xipeng,Feng Zhengyun,et al.Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification[J].JOURNAL OF TRANSLATIONAL MEDICINE.2022,20(1):doi:10.1186/s12967-022-03458-9.
APA:
Lin, Huan,Pan, Xipeng,Feng, Zhengyun,Yan, Lixu,Hua, Junjie...&Liu, Zaiyi.(2022).Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification.JOURNAL OF TRANSLATIONAL MEDICINE,20,(1)
MLA:
Lin, Huan,et al."Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification".JOURNAL OF TRANSLATIONAL MEDICINE 20..1(2022)