CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma

Mungai, Francesco; Verrone, Giovanni Battista; Pietragalla, Michele; Berti, Valentina; Addeo, Gloria; Desideri, Isacco; Bonasera, Luigi; Miele, Vittorio

doi:10.1007/s11547-019-01028-6

Cited by 42 publications

(35 citation statements)

References 27 publications

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“…Mann‐Whitney U test (non‐normality) was used to compare the value of each textural parameter for differentiation of the three groups (L versus I; L versus C and I versus C). Multiple testing correction using Benjamini and Hochberg 18,19 method was performed to calculate the false discovery rate at 0.05. The repeatability of the method was assessed with the revaluation (segmentation and extraction of texture parameters) of a quarter of the sample again, using the intraclass correlation coefficient (ICC).…”

Section: Methodsmentioning

confidence: 99%

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Gonçalves

Araújo

Nussi

et al. 2020

Journal of Periodontology

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Background The aim of this study was to apply texture analysis (TA) to cone‐beam computed tomography (CBCT) scans of patients with grade C periodontitis for detection of non‐visible changes in the image. Methods TA was performed on CBCT scans of 34 patients with grade C periodontitis. Axial sections of CBCT were divided into three groups as follows: Group L (lesion) in which there is a furcal lesion with periodontal bone loss; Group I (intermediate) in which the border of the furcal lesion has normal characteristics; and Group C (control) in which the area is healthy. Eleven texture parameters were extracted from the region of interest. Mann‐Whitney U test was used to assess the differences in the texture between the three groups as follows: L versus I; L versus C, and I versus C. Results Statistically significant differences (P <0.05) were observed in almost all parameters in the intergroup analyses (i.e., L versus I and L versus C). However, statistical differences were smaller in groups I versus C in which only entropy of sum, entropy of difference, mean of sum, and variance of difference were statistically different (P < 0.05). Conclusion TA can potentially provide prognostic information to improve the diagnostic accuracy in the grading of the tissue around the furcal lesion, thus potentially accelerating the treatment decision‐making process.

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Section: Methodsmentioning

confidence: 99%

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Gonçalves

Araújo

Nussi

et al. 2020

Journal of Periodontology

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“…Spearman's correlation coefficient was used to analyse associations between the investigated parameters. Mann-Whitney's test was used to test between-groups and P -values were adjusted by using the Benjamini-Hochberg false discovery rate (FDR) method [ 23 , 24 ]. The significance level adopted was 5%.…”

Section: Methodsmentioning

confidence: 99%

Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

Rosa

Bergamini

Palmieri

et al. 2020

Heliyon

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Objective This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis. Methods Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005). Results The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation. Conclusion Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions. Clinical significance Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.

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“…The distinct oncogenesis of HPV-positive OPSCC results in characteristic histopathology 4 , 5 , perfusion, and diffusion parameters, which are related to the angiogenesis and cellularity of the tumour. Several studies have reported diagnosis of the HPV status in patients with OPSCC using preoperative computed tomography (CT) or magnetic resonance (MR) imaging 6 – 8 .…”

Section: Introductionmentioning

confidence: 99%

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status

Suh

Lee

Choi

et al. 2020

Sci Rep

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We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

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CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma

Cited by 42 publications

References 27 publications

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status

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