Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer
Crohn’s-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and furthe...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022004445 |
| _version_ | 1797978161656365056 |
|---|---|
| author | Yao Xu Shangqing Yang Yaxi Zhu Su Yao Yajun Li Huifen Ye Yunrui Ye Zhenhui Li Lin Wu Ke Zhao Liyu Huang Zaiyi Liu |
| author_facet | Yao Xu Shangqing Yang Yaxi Zhu Su Yao Yajun Li Huifen Ye Yunrui Ye Zhenhui Li Lin Wu Ke Zhao Liyu Huang Zaiyi Liu |
| author_sort | Yao Xu |
| collection | DOAJ |
| description | Crohn’s-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score were obtained according to the related density. And the CLR-I score was calculated by summing two scores. The development (Hospital 1, N = 370) and validation (Hospital 2 & 3, N = 144) cohorts were used to evaluate the prognostic value of the CLR-I score. The C-index and integrated area under the curve were used to assess the discrimination ability. A higher CLR-I score was associated with a better prognosis, which was identified by multivariable analysis in the development (hazard ratio for score 3 vs score 0 = 0.22, 95% confidence interval 0.12–0.40, P < 0.001) and validation cohort (0.21, 0.05–0.78, P = 0.020). The AI-based CLR-I score outperforms the single predictor in predicting OS which is objective and more prone to be deployed in clinical practice. |
| first_indexed | 2024-04-11T05:18:32Z |
| format | Article |
| id | doaj.art-89a8b9a3393e42f0b060affdecbc095b |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:18:32Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-89a8b9a3393e42f0b060affdecbc095b2022-12-24T04:54:38ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012055865594Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancerYao Xu0Shangqing Yang1Yaxi Zhu2Su Yao3Yajun Li4Huifen Ye5Yunrui Ye6Zhenhui Li7Lin Wu8Ke Zhao9Liyu Huang10Zaiyi Liu11Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; 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; School of Medicine, South China University of Technology, Guangzhou 510006, ChinaSchool of Life Science and Technology, Xidian University, Xian 710071, ChinaDepartment of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, ChinaDepartment of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; 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, ChinaThe Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, ChinaThe Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; 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; Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, ChinaDepartment of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, ChinaDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; 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; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Corresponding authors at: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China (K. Zhao and Z. Liu). School of Life Science and Technology, Xidian University, 2 Taibai Nanlu Road, Xian, 710071, China (L. Huang).School of Life Science and Technology, Xidian University, Xian 710071, China; Corresponding authors at: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China (K. Zhao and Z. Liu). School of Life Science and Technology, Xidian University, 2 Taibai Nanlu Road, Xian, 710071, China (L. Huang).Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; 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; Corresponding authors at: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China (K. Zhao and Z. Liu). School of Life Science and Technology, Xidian University, 2 Taibai Nanlu Road, Xian, 710071, China (L. Huang).Crohn’s-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score were obtained according to the related density. And the CLR-I score was calculated by summing two scores. The development (Hospital 1, N = 370) and validation (Hospital 2 & 3, N = 144) cohorts were used to evaluate the prognostic value of the CLR-I score. The C-index and integrated area under the curve were used to assess the discrimination ability. A higher CLR-I score was associated with a better prognosis, which was identified by multivariable analysis in the development (hazard ratio for score 3 vs score 0 = 0.22, 95% confidence interval 0.12–0.40, P < 0.001) and validation cohort (0.21, 0.05–0.78, P = 0.020). The AI-based CLR-I score outperforms the single predictor in predicting OS which is objective and more prone to be deployed in clinical practice.http://www.sciencedirect.com/science/article/pii/S2001037022004445Crohn's-like lymphoid reactionTumor-infiltrating lymphocytesArtificial intelligenceColorectal cancerWhole-slide images |
| spellingShingle | Yao Xu Shangqing Yang Yaxi Zhu Su Yao Yajun Li Huifen Ye Yunrui Ye Zhenhui Li Lin Wu Ke Zhao Liyu Huang Zaiyi Liu Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer Computational and Structural Biotechnology Journal Crohn's-like lymphoid reaction Tumor-infiltrating lymphocytes Artificial intelligence Colorectal cancer Whole-slide images |
| title | Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer |
| title_full | Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer |
| title_fullStr | Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer |
| title_full_unstemmed | Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer |
| title_short | Artificial intelligence for quantifying Crohn’s-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer |
| title_sort | artificial intelligence for quantifying crohn s like lymphoid reaction and tumor infiltrating lymphocytes in colorectal cancer |
| topic | Crohn's-like lymphoid reaction Tumor-infiltrating lymphocytes Artificial intelligence Colorectal cancer Whole-slide images |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022004445 |
| work_keys_str_mv | AT yaoxu artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT shangqingyang artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT yaxizhu artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT suyao artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT yajunli artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT huifenye artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT yunruiye artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT zhenhuili artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT linwu artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT kezhao artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT liyuhuang artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer AT zaiyiliu artificialintelligenceforquantifyingcrohnslikelymphoidreactionandtumorinfiltratinglymphocytesincolorectalcancer |