A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study

• Main Authors: Fan Zhang, Lian-Zhen Zhong, Xun Zhao, Di Dong, Ji-Jin Yao, Si-Yang Wang, Ye Liu, Ding Zhu, Yin Wang, Guo-Jie Wang, Yi-Ming Wang, Dan Li, Jiang Wei, Jie Tian, Hong Shan
• Format: Article
• Language: English
• Published: SAGE Publishing 2020-12-01
• Series: Therapeutic Advances in Medical Oncology
• Online Access: https://doi.org/10.1177/1758835920971416

Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) 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 test ( n  = 44), and external test ( 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 test 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 with the clinical model in the training (C-index: 0.817 versus 0.730, p  < 0.050), internal test (C-index: 0.828 versus 0.602, p  < 0.050) and external test (C-index: 0.834 versus 0.679, p  < 0.050) cohorts. Furthermore, patients were stratified successfully 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. Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.