Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma
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 ima...
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/14/7244 |
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| author | Simona Marzi Francesca Piludu Ilaria Avanzolini Valerio Muneroni Giuseppe Sanguineti Alessia Farneti Pasqualina D’Urso Maria Benevolo Francesca Rollo Renato Covello Francesco Mazzola Antonello Vidiri |
| author_facet | Simona Marzi Francesca Piludu Ilaria Avanzolini Valerio Muneroni Giuseppe Sanguineti Alessia Farneti Pasqualina D’Urso Maria Benevolo Francesca Rollo Renato Covello Francesco Mazzola Antonello Vidiri |
| author_sort | Simona Marzi |
| collection | DOAJ |
| 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<sub>PT</sub>, 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. |
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| language | English |
| last_indexed | 2024-03-09T12:16:42Z |
| publishDate | 2022-07-01 |
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| series | Applied Sciences |
| spelling | doaj.art-4aecacc6a11d4e6294ac23c3216df4d12023-11-30T22:45:35ZengMDPI AGApplied Sciences2076-34172022-07-011214724410.3390/app12147244Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell CarcinomaSimona Marzi0Francesca Piludu1Ilaria Avanzolini2Valerio Muneroni3Giuseppe Sanguineti4Alessia Farneti5Pasqualina D’Urso6Maria Benevolo7Francesca Rollo8Renato Covello9Francesco Mazzola10Antonello Vidiri11Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyMedical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyDepartment of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyDepartment of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyDepartment of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyPathology Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyPathology Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyPathology Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyOtolaryngology & Head and Neck Surgery, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyRadiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, ItalyBackground: 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<sub>PT</sub>, 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.https://www.mdpi.com/2076-3417/12/14/7244human papillomavirusoropharyngeal squamous cell carcinomamagnetic resonance imagingdiffusion magnetic resonance imagingmachine learningradiomics |
| spellingShingle | Simona Marzi Francesca Piludu Ilaria Avanzolini Valerio Muneroni Giuseppe Sanguineti Alessia Farneti Pasqualina D’Urso Maria Benevolo Francesca Rollo Renato Covello Francesco Mazzola Antonello Vidiri Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma Applied Sciences human papillomavirus oropharyngeal squamous cell carcinoma magnetic resonance imaging diffusion magnetic resonance imaging machine learning radiomics |
| title | Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma |
| title_full | Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma |
| title_fullStr | Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma |
| title_full_unstemmed | Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma |
| title_short | Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma |
| title_sort | multifactorial model based on dwi radiomics to determine hpv status in oropharyngeal squamous cell carcinoma |
| topic | human papillomavirus oropharyngeal squamous cell carcinoma magnetic resonance imaging diffusion magnetic resonance imaging machine learning radiomics |
| url | https://www.mdpi.com/2076-3417/12/14/7244 |
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