Radiomics features analysis of PET images in oropharyngeal and hypopharyngeal cancer

Liao, Ken Ying-Kai; Chiu, Chuang-Chien; Chiang, Wan-Chi; Chiou, Yu-Rou; Zhang, Geoffrey; Yang, Shih-Neng; Huang, Tzung-Chi

doi:10.1097/md.0000000000015446

Cited by 21 publications

(18 citation statements)

References 15 publications

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“…We collected information from 60 articles that satisfy our selection criteria and evaluated head and neck cancer [ 28 , 82 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , ...…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…The application of radiomics has led to great strides in tumour diagnosis, treatment response assessment and prognosis [ 12 , 13 ]. In head and neck cancer patients, computed tomography Li-na Song (CT) and positron emission tomography (PET) radiomics signatures can predict not only the HPV (p16) status in oropharyngeal squamous cell carcinoma [ 14 ] but also the hypoxia status [ 15 ], and the data can be used to distinguish oropharyngeal from hypopharyngeal cancer [ 16 ]. Moreover, MRI radiomics signatures have also been recognized as non-invasive, preoperative and independent prognostic factors for head and neck squamous cell carcinoma (HNSCC) and nasopharynx Li-na Song cancer Li-na Song (NPC) in clinical practice [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Song

Chen

et al. 2021

BMC Med Imaging

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Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

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“…The application of radiomics has led to great strides in tumour diagnosis, treatment response assessment and prognosis [12][13] . In head and neck cancer patients, computed tomography (CT) and positron emission tomography (PET) radiomics signatures can predict not only the HPV (p16) status in oropharyngeal squamous cell carcinoma [14] but also the hypoxia status [15] , and the data can be used to distinguish oropharyngeal from hypopharyngeal cancer [16] . Moreover, MRI radiomics signatures have also been recognized as noninvasive, preoperative and independent prognostic factors for head and neck squamous cell carcinoma (HNSCC) and nasopharynx cancer (NPC) in clinical practice [17][18] .…”

Section: Introductionmentioning

confidence: 99%

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Song

Chen

et al. 2020

Preprint

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Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p=0.000) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

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Radiomics features analysis of PET images in oropharyngeal and hypopharyngeal cancer

Cited by 21 publications

References 15 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

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