A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images

Abstract

Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

“…As can be seen in Table 3 , one or more dose features were included in the multidimensional prognostic model. Meanwhile, the selected features were from the filtered images, which is similar to the results obtained in some other studies [ 7 , 10 , 33 – 36 ], indicating that the filtered images are able to reveal information that is difficult to convey in the original images.…”

“…Xu et al developed a PET/CT-based subregional radiomics approach to predict progression-free survival in patients with nasopharyngeal carcinoma, demonstrating the prognostic potential of subregional radiomics in nasopharyngeal carcinoma [ 9 ]. Keek et al proposed a multifactorial prognostic model including CT-based radiomic features, TNM8, tumour volume, clinical and biological variables, and demonstrated that the model could predict overall survival (OS) very accurately in patients with advanced head and neck squamous cell carcinoma [ 10 ]. Liu et al found that combining clinicopathological features with pre-treatment PET/CT or post-treatment PET/CT radiomic features substantially improved the prediction of OS and disease-free survival (DFS) in patients with head and neck squamous cell carcinoma [ 11 ].…”

From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics

“…Deep learning involves training artificial neural networks to learn representative features for outcome prediction from amounts of data, and deep learning-based models have been developed to predict progression-free survival for head and neck squamous cell carcinoma patients using clinical and PET/CT imaging data [ 20 , 21 , 22 ]. Several studies have shown that radiomics in CT has the potential to improve the prediction of the prognosis of H&N cancer [ 23 , 24 , 25 ]. Some studies have also investigated the use of radiomics in both CT and PET for survival analysis for H&N cancer [ 26 , 27 ].…”

Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up

“…Additionally, 18 F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) imaging has been used for several years in this disease for staging as well [1][2][3][4][5][6][7]. Most radiomics studies on these tumors have focused on CT and MRI images, including the development of radiomics signatures that outperforms TNM staging when evaluating overall survival [19,[50][51][52], with external validation in a separate study [21]; they are predictors of the tumor's HPV status [53,54]. However, studies evaluating PET radiomics have demonstrated a potential to predict the risk of local failure [55][56][57][58] and distant metastasis [57,58], progression-free [59,60] and overall survival [57][58][59][60].…”