Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

• Published in: Cancers Vol. 16; no. 12; p. 2195
• Main Authors: Philip, Mahima Merin, Watts, Jessica, McKiddie, Fergus, Welch, Andy, Nath, Mintu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.06.2024; MDPI
• Subjects: Algorithms; Cancer therapies; Cell culture; Datasets; Disease management; Head & neck cancer; Head and neck carcinoma; Immunotherapy; Machine learning; Medical imaging; Medical prognosis; Metastases; Metastasis; Mortality; Patients; Positron emission tomography; Prognosis; Radiomics; Software; Squamous cell carcinoma; Survival analysis; Tomography; Tumors; radiomics; machine learning; positron emission tomography (PET); prognosis; head and neck squamous cell carcinoma (HNSCC)
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers16122195

High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models-penalized Cox model, random forest, gradient boosted model and support vector machine-to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.