A deep learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study | |
Zhang, Fan2; Zhong, Lianzhen1; Zhao, Xun1; Dong, Di1; Yao, Jijin2; Wang, Siyang2; Liu, Ye2; Zhu, Ding2; Wang, Yin2; Wang, Guojie2 | |
刊名 | Therapeutic Advances in Medical Oncology |
2020 | |
卷号 | 0期号:0页码:0 |
关键词 | nasopharyngeal carcinoma |
DOI | 10.1177/1758835920971416 |
英文摘要 | Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance images and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). Methods: We recruited 220 NPC patients and divided them into training (n=132), internal testing (n=44), external testing (n=44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological testing cohort). Results: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689-0.779, all p <0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared to the clinical model in the training (C-index: 0.817 vs. 0.730, p <0.050), internal testing (C-index: 0.828 vs. 0.602, p <0.050) and external testing (C-index: 0.834 vs. 0.679, p <0.050) cohorts. Furthermore, patients were successfully stratified into two groups with distinguishable prognosis (log-rank p <0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort (n = 16). Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40689] |
专题 | 自动化研究所_中国科学院分子影像重点实验室 |
通讯作者 | Wei, Jiang; Tian, Jie; Shan, Hong |
作者单位 | 1.4. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China 2.1. Department of head and neck oncology, The cancer center of the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province 519000, P. R. China |
推荐引用方式 GB/T 7714 | Zhang, Fan,Zhong, Lianzhen,Zhao, Xun,et al. A deep learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study[J]. Therapeutic Advances in Medical Oncology,2020,0(0):0. |
APA | Zhang, Fan.,Zhong, Lianzhen.,Zhao, Xun.,Dong, Di.,Yao, Jijin.,...&Shan, Hong.(2020).A deep learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study.Therapeutic Advances in Medical Oncology,0(0),0. |
MLA | Zhang, Fan,et al."A deep learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study".Therapeutic Advances in Medical Oncology 0.0(2020):0. |
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