Predicting Neoadjuvant Chemoradiotherapy Response in Locally Advanced Rectal Cancer Using Tumor-Infiltrating Lymphocytes Density
Yao Xu,1,2,* Xiaoying Lou,3,* Yanting Liang,2,4,* Shenyan Zhang,3 Shangqing Yang,5 Qicong Chen,6 Zeyan Xu,1,2 Minning Zhao,2,7 Zhenhui Li,8,* Ke Zhao,2 Zaiyi Liu1,2 1School of Medicine, South China University of Technology, Guangzhou, 510006, People’s Republic of Chin...
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| Autores principales: | , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/2213ea1d606f4c1885efea2c68a532fd |
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| Sumario: | Yao Xu,1,2,* Xiaoying Lou,3,* Yanting Liang,2,4,* Shenyan Zhang,3 Shangqing Yang,5 Qicong Chen,6 Zeyan Xu,1,2 Minning Zhao,2,7 Zhenhui Li,8,* Ke Zhao,2 Zaiyi Liu1,2 1School of Medicine, South China University of Technology, Guangzhou, 510006, People’s Republic of China; 2Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China; 3Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510665, People’s Republic of China; 4Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China; 5School of Life Science and Technology, Xidian University, Xian, 710071, People’s Republic of China; 6Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, People’s Republic of China; 7The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510080, People’s Republic of China; 8Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zaiyi LiuDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, People’s Republic of ChinaEmail liuzaiyi@gdph.org.cnKe ZhaoDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, People’s Republic of ChinaEmail zhaoke@gdph.org.cnPurpose: Accumulating evidence revealed the predictive value of tumor-infiltrating lymphocytes (TILs) for neoadjuvant chemoradiotherapy (nCRT) response in solid tumors. This study quantified TILs density using hematoxylin and eosin (H&E) stained whole-slide images (WSIs) and investigated the predictive value of TILs density on nCRT response in locally advanced rectal cancer (LARC) patients.Patients and Methods: Two hundred and ten patients diagnosed with LARC were enrolled in this study. The density of TILs in the stroma region was quantified by a semi-automatic method in WSIs. Patients were stratified into low-TILs and high-TILs groups using the median value as the threshold. The tumor regression grade (TRG) was used to assess the response to nCRT in tumor resected specimens. Based on TRG, patients were classified into major-responder (TRG 0– 1) and non-responder (TRG 2– 3) groups.Results: The TILs density was significantly correlated with the nCRT response. Specifically, patients with high-TILs tend to have a higher major-responder rate than the low-TILs group (63.8% vs 47.6%, P = 0.026). Univariate analysis showed the TILs density was a predictor for the nCRT response (high vs low, odds ratio [OR] =1.94, 95% confidence interval 1.12– 3.37, P = 0.019), and multivariate analysis further confirmed the correlation (adjusted odds ratio [AOR] = 2.41, 1.28– 4.56, P = 0.007).Conclusion: Patients with a high-TIL density have a higher major-responder rate than the low-TILs group, indicating patients with a strong immune response benefit more from nCRT. This semi-automatic method can facilitate the individualized preoperative prediction of the TRG for LARC patients before nCRT.Keywords: tumor-infiltrating lymphocytes, locally advanced rectal cancer, neoadjuvant chemoradiotherapy response, tumor regression grade, digital pathology |
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