Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis

Nardone, Valerio; Tini, Paolo; Nioche, Christophe; Biondi, M.; Sebaste, Lucio; Mazzei, Maria Antonietta; Buonamici, F. Banci; Pirtoli, Luigi

doi:10.1016/j.radonc.2016.09.004

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…Some recent studies have demonstrated the potential value of texture features, which are significantly associated with the pathology of tumors and the prognosis of patients. 7,8 Texture features can be extracted from many kinds of medical images and provide quantitative information we need. 9 A great deal of quantitative information is contained in medical images, which is far beyond what is currently used for routine interpretation.…”

Section: Introductionmentioning

confidence: 99%

<p>A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland</p>

Zhang¹,

Li²,

Lv³

et al. 2020

CMAR

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The aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland. Materials and Methods: Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden's index. Results: All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P<0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P>0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden's index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%). Conclusion: Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.

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Section: Introductionmentioning

confidence: 99%

<p>A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland</p>

Zhang¹,

Li²,

Lv³

et al. 2020

CMAR

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“…TA is performed by means of a computer quantification of both gray-level intensity, position of pixels, and its use is being investigated in several fields (16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab

Nardone¹,

Tini

Pastina

et al. 2019

Oncol Lett

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“…Statistical analysis achieves and quantifies both distribution and relationships of the gray-level values of the image. Actually, many studies have considered TA in several areas of cancer imaging, showing a potential application in diagnosis, assessment of response to treatment as well as characterization of tumors (16)(17)(18)(19)(20)(21)(22)(23)(24). In regards of bone physiology, TA has been applied mainly for densitometry, leading to the development of the trabecular bone score (TBS), an analytical tool for gray-level texture measurements calculated on dual X-ray absorptiometry (DXA) of the lumbar spine, thus providing information related to trabecular architecture (25)(26)(27).…”

Section: Respectively the Risk Factors For Rt-induced Ifs That Havementioning

confidence: 99%

3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures

Nardone¹,

Tini²,

Croci³

et al. 2018

Quant. Imaging Med. Surg.

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Background: The aim of our work is to assess the potential role of texture analysis (TA), applied to computed tomography (CT) simulation scans, in relation to the development of insufficiency fractures (IFs) in patients undergoing radiation therapy (RT) for pelvic malignancies. Methods:We analyzed patients undergoing pelvic RT from Jan-2010 to Dec-2016, 31 of whom had developed IFs of the pelvis. We analyzed CT simulation scans using LifeX Software © , and in particular we selected three regions of interest (ROI): L5 body, the sacrum and both the femoral heads. The ROI were automatically contoured using the treatment planning software Raystation © . TA parameters included parameters from the gray-level histogram, indices from sphericity and from the matrix of GLCM (gray level co-occurrence matrix). The IFs patients were matched (1:1 ratio) with control patients who had not developed IFs, and were matched for age, sex, type of tumor, menopausal status, RT dose and use of chemotherapy. Univariate and multivariate analyses (logistic regression) were used for statistical analysis.Results: Significant TA parameters on univariate analysis included both parameters from the histogram distribution, as well from the matrix of GLCM. On logistic regression analysis the significant parameters Conclusions: In our experience, 3D-bone CT TA can be used to stratify the risk of the patients to develop radiation-induced IFs. A prospective study will be conducted to validate these findings.

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Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis

Cited by 11 publications

References 4 publications

<p>A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland</p>

<p>A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland</p>

Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab

3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures

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