Prediction of Incomplete Response of Primary Tumour Based on Clinical and Radiomics Features in Inoperable Head and Neck Cancers after Definitive Treatment

Abstract

Radical treatment of patients diagnosed with inoperable and locally advanced head and neck cancers (LAHNC) is still a challenge for clinicians. Prediction of incomplete response (IR) of primary tumour would be of value to the treatment optimization for patients with LAHNC. Aim of this study was to develop and evaluate models based on clinical and radiomics features for prediction of IR in patients diagnosed with LAHNC and treated with definitive chemoradiation or radiotherapy. Clinical and imaging data of 290 patients were included into this retrospective study. Clinical model was built based on tumour and patient related features. Radiomics features were extracted based on imaging data, consisting of contrast- and non-contrast-enhanced pre-treatment CT images, obtained in process of diagnosis and radiotherapy planning. Performance of clinical and combined models were evaluated with area under the ROC curve (AUROC). Classification performance was evaluated using 5-fold cross validation. Model based on selected clinical features including ECOG performance, tumour stage T3/4, primary site: oral cavity and tumour volume were significantly predictive for IR, with AUROC of 0.78. Combining clinical and radiomics features did not improve model’s performance, achieving AUROC 0.77 and 0.68 for non-contrast enhanced and contrast-enhanced images respectively. The model based on clinical features showed good performance in IR prediction. Combined model performance suggests that real-world imaging data might not yet be ready for use in predictive models.

Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node‐positive head and neck cancer

“…Radiomics and machine learning approaches have found several applications in the HNC field, such as the development of prognostic biomarkers, 6–8 radiomics strategies for predicting tumor response, 9–11 as well as the detection of HPV status in patients with oropharynx cancer 11,12 . Furthermore, normal tissues radiomics information is likely to reflect potential risks for late radiation‐induced toxicities 13,14 .…”

“…4 Its potential applications are extensive, both for the interpretation of data resulting from modern imaging modalities and for the identification of new features undetectable through the human eyes, thus overcoming several limits of conventional imaging and fostering further advances in RT. 4,5 Radiomics and machine learning approaches have found several applications in the HNC field, such as the development of prognostic biomarkers, [6][7][8] radiomics strategies for predicting tumor response, [9][10][11] as well as the detection of HPV status in patients with oropharynx cancer. 11,12 Furthermore, normal tissues radiomics information is likely to reflect potential risks for late radiationinduced toxicities.…”