Prognostic significance of cyclin D1 expression pattern in HPV-negative oral and oropharyngeal carcinoma: A deep-learning approach

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Ke Yang, Guixin Zhu, Yanan Sun, Yaying Hu, Yinan Lv, Yiwei Li, Juncheng Pan, Fu Chen, Yi Zhou, Jiali Zhang
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引用次数: 0

Abstract

Background

We aimed to establish image recognition and survival prediction models using a novel scoring system of cyclin D1 expression pattern in patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.

Methods

The clinicopathological data of 610 patients with human papillomavirus-negative oral/oropharyngeal squamous cell carcinoma were analyzed retrospectively. Cox univariate and multivariate risk regression analyses were performed to compare cyclin D1 expression pattern scoring with the traditional scoring method—cyclin D1 expression level scoring—in relation to patients' overall and progression-free survival. An image recognition model employing the cyclin D1 expression pattern scoring system was established by YOLOv5 algorithms. From this model, two independent survival prediction models were established using the DeepHit and DeepSurv algorithms.

Results

Cyclin D1 had three expression patterns in oral and oropharyngeal squamous cell carcinoma cancer nests. Superior to cyclin D1 expression level scoring, cyclin D1 expression pattern scoring was significantly correlated with the prognosis of patients with oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). Moreover, it was an independent prognostic risk factor in both oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). The cyclin D1 expression pattern-derived image recognition model showed an average test set accuracy of 78.48% ± 4.31%. In the overall survival prediction models, the average concordance indices of the test sets established by DeepSurv and DeepHit were 0.71 ± 0.02 and 0.70 ± 0.01, respectively.

Conclusion

Combined with the image recognition model of the cyclin D1 expression pattern, the survival prediction model had a relatively good prediction effect on the overall survival prognosis of patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.

细胞周期蛋白D1表达模式在hpv阴性口腔和口咽癌中的预后意义:一种深度学习方法
本研究旨在利用一种新的人类乳头瘤病毒阴性口腔或口咽鳞状细胞癌患者细胞周期蛋白D1表达模式评分系统建立图像识别和生存预测模型。方法回顾性分析610例人乳头瘤病毒阴性口腔/口咽鳞状细胞癌的临床病理资料。采用Cox单因素和多因素风险回归分析,比较cyclin D1表达模式评分与传统评分方法- cyclin D1表达水平评分与患者总生存期和无进展生存期的关系。采用YOLOv5算法建立cyclin D1表达模式评分系统的图像识别模型。在此基础上,分别利用deepphit和DeepSurv算法建立了两个独立的生存预测模型。结果Cyclin D1在口腔和口咽鳞状细胞癌癌巢中有三种表达模式。与cyclin D1表达模式评分相比,cyclin D1表达模式评分与口腔鳞状细胞癌(p < 0.0001)和口咽鳞状细胞癌患者的预后有显著相关性(p < 0.05)。此外,它在口腔鳞状细胞癌(p < 0.0001)和口咽鳞状细胞癌(p < 0.05)中都是独立的预后危险因素。基于cyclin D1表达模式的图像识别模型的平均测试集准确率为78.48%±4.31%。在总生存预测模型中,DeepSurv和DeepHit建立的测试集的平均一致性指数分别为0.71±0.02和0.70±0.01。结论结合cyclin D1表达谱的图像识别模型,生存预测模型对人乳头瘤病毒阴性口腔或口咽鳞状细胞癌患者的总体生存预后有较好的预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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