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Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma
| Content Provider | MDPI |
|---|---|
| Author | D’urso, Pasqualina Marzi, Simona Piludu, Francesca Avanzolini, Ilaria Muneroni, Valerio Sanguineti, Giuseppe Farneti, Alessia Benevolo, Maria Rollo, Francesca Covello, Renato Mazzola, Francesco Vidiri, Antonello |
| Copyright Year | 2022 |
| Description | Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of $D_{PT}$, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size. |
| Starting Page | 7244 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12147244 |
| Journal | Applied Sciences |
| Issue Number | 14 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-19 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Oncology Human Papillomavirus Oropharyngeal Squamous Cell Carcinoma Magnetic Resonance Imaging Diffusion Magnetic Resonance Imaging Machine Learning Radiomics |
| Content Type | Text |
| Resource Type | Article |
