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

Abstract

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.

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

“…At present, deep learning has been recognized as an effective tool in the field of medicine, especially in medical image analysis [6,7]. In the recent years, along with the development of microphotography and whole slide scanning technology, the pathological slides can be preserved in the form of digital image, and the computer-aided diagnostic system based on the deep learning methods has been introduced into pathological field [7], demonstrating its advantages in both lesion detection [8,9] and prognostic analysis [10,11].…”

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

“…Therefore, DLR, which combines the advantages of DL and radiomics, has been proposed and widely researched [ 25 , 181 , 182 ]. Although there are four studies that use DLR in NPC [ 30 , 152 , 153 , 154 ], the method is far from being fully developed.…”

“…In [ 154 ], Zhang innovatively combined the clinical features of patients with nasopharyngeal cancer, the radiomic features based on MRIs, and the DCNN model based on pathological images to construct a multi-scale nomogram to predict the failure-free survival of patients with NPC. The nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the internal test (C-index: 0.828 vs. 0.602, p < 0.050) and external test (C-index: 0.834 vs. 0.679, p < 0.050) cohorts.…”

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

“…Limited generalizability as it was a single institution study. Zhang et al (2020) 36 (China) NPC 220 (WSIs, MRI images and clinicopathological data) Deep learning (Prognosis) 1. Prediction: Resnet-18 and DeepSurv To explore the use of magnetic resonance imaging and microscopic whole-slide images to improve the prognosis of model 1.…”

“… ✓ ✓ ✓ Yes No Yang et al (2020) 28 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Yang et al (2020) 67 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Zhang et al (2020) 58 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhang et al (2020) 36 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhao et al (2020) 35 ✓ ✓ ✓ ? ✓ ✓ ✓ Yes No Zhong et al (2020) 29 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No ...…”