Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer
Abstract Background We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. Methods A total of 1010 colorectal cancer patient...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-022-03666-3 |
| _version_ | 1817979690012377088 |
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| author | Jing Yang Huifen Ye Xinjuan Fan Yajun Li Xiaomei Wu Minning Zhao Qingru Hu Yunrui Ye Lin Wu Zhenhui Li Xueli Zhang Changhong Liang Yingyi Wang Yao Xu Qian Li Su Yao Dingyun You Ke Zhao Zaiyi Liu |
| author_facet | Jing Yang Huifen Ye Xinjuan Fan Yajun Li Xiaomei Wu Minning Zhao Qingru Hu Yunrui Ye Lin Wu Zhenhui Li Xueli Zhang Changhong Liang Yingyi Wang Yao Xu Qian Li Su Yao Dingyun You Ke Zhao Zaiyi Liu |
| author_sort | Jing Yang |
| collection | DOAJ |
| description | Abstract Background We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. Methods A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival. Result Patients were classified into 4-level score groups (score 1–4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15–0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15–0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018). Conclusion The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine. |
| first_indexed | 2024-04-13T22:45:46Z |
| format | Article |
| id | doaj.art-9eaef0dff80d46938691394fa9c36528 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-04-13T22:45:46Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-9eaef0dff80d46938691394fa9c365282022-12-22T02:26:24ZengBMCJournal of Translational Medicine1479-58762022-10-0120111110.1186/s12967-022-03666-3Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancerJing Yang0Huifen Ye1Xinjuan Fan2Yajun Li3Xiaomei Wu4Minning Zhao5Qingru Hu6Yunrui Ye7Lin Wu8Zhenhui Li9Xueli Zhang10Changhong Liang11Yingyi Wang12Yao Xu13Qian Li14Su Yao15Dingyun You16Ke Zhao17Zaiyi Liu18Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalDepartment of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen UniversityGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalDepartment of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen UniversityThe Second School of Clinical Medicine, Southern Medical UniversityThe Second School of Clinical Medicine, Southern Medical UniversityThe Second School of Clinical Medicine, Southern Medical UniversityDepartment of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer CenterGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalDepartment of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalDepartment of Radiology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated With Jinan UniversityGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalDepartment of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesSchool of Public Health, Kunming Medical UniversityGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalGuangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s HospitalAbstract Background We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. Methods A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival. Result Patients were classified into 4-level score groups (score 1–4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15–0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15–0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018). Conclusion The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.https://doi.org/10.1186/s12967-022-03666-3Deep learningWhole-slide imagesDeep-immune scoreColorectal cancerDigital pathology |
| spellingShingle | Jing Yang Huifen Ye Xinjuan Fan Yajun Li Xiaomei Wu Minning Zhao Qingru Hu Yunrui Ye Lin Wu Zhenhui Li Xueli Zhang Changhong Liang Yingyi Wang Yao Xu Qian Li Su Yao Dingyun You Ke Zhao Zaiyi Liu Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer Journal of Translational Medicine Deep learning Whole-slide images Deep-immune score Colorectal cancer Digital pathology |
| title | Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| title_full | Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| title_fullStr | Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| title_full_unstemmed | Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| title_short | Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| title_sort | artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer |
| topic | Deep learning Whole-slide images Deep-immune score Colorectal cancer Digital pathology |
| url | https://doi.org/10.1186/s12967-022-03666-3 |
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