TH-C-217BCD-02: Ultrasound Texture Analysis of Radiation-Induced Parotid-Gland Injury in Post-Radiotherapy Head-And-Neck Patients: Feasibility Study

2012 ◽  
Vol 39 (6Part30) ◽  
pp. 4003-4003
Author(s):  
X Yang ◽  
S Tridandapani ◽  
J Beitler ◽  
D Yu ◽  
S Henry ◽  
...  
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Texture Analysis ◽  
Feasibility Study ◽  
Radiation Induced

scholarly journals Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: An in vivo study of late toxicity

2012 ◽  
Vol 39 (9) ◽  
pp. 5732-5739 ◽  
Author(s):  
Xiaofeng Yang ◽  
Srini Tridandapani ◽  
Jonathan J. Beitler ◽  
David S. Yu ◽  
Emi J. Yoshida ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Parotid Gland ◽  
Head And Neck ◽  
Texture Analysis ◽  
Neck Cancer ◽  
Late Toxicity ◽  
In Vivo Study ◽  
Cancer Radiotherapy ◽  
Radiation Induced

Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

2022 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
Junqian Zhang ◽  
Yingming Sun ◽  
Hongen Liao ◽  
Jian Zhu ◽  
Yuan Zhang
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Helical Tomotherapy ◽  
Radiation Treatment ◽  
Automatic Segmentation ◽  
Parotid Glands ◽  
Deep Convolutional Neural Networks ◽  
Megavoltage Computed Tomography ◽  
Radiation Induced ◽  
Segmentation Models

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT

2018 ◽  
Vol 123 (6) ◽  
pp. 415-423 ◽  
Author(s):  
Valerio Nardone ◽  
Paolo Tini ◽  
Christophe Nioche ◽  
Maria Antonietta Mazzei ◽  
Tommaso Carfagno ◽  
...  
Keyword(s):  
Head And Neck ◽  
Texture Analysis ◽  
Radiation Induced

scholarly journals Ultrasonic Nakagami-parameter characterization of parotid-gland injury following head-and-neck radiotherapy: A feasibility study of late toxicity

2014 ◽  
Vol 41 (2) ◽  
pp. 022903 ◽  
Author(s):  
Xiaofeng Yang ◽  
Srini Tridandapani ◽  
Jonathan J. Beitler ◽  
David S. Yu ◽  
Ning Wu ◽  
...  
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Feasibility Study ◽  
Late Toxicity ◽  
Head And Neck Radiotherapy

scholarly journals Ultrasound Histogram Assessment of Parotid Gland Injury Following Head-and-Neck Radiotherapy: A Feasibility Study

2012 ◽  
Vol 38 (9) ◽  
pp. 1514-1521 ◽  
Author(s):  
Xiaofeng Yang ◽  
Srini Tridandapani ◽  
Jonathan J. Beitler ◽  
David S. Yu ◽  
Emi J. Yoshida ◽  
...  
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Feasibility Study ◽  
Head And Neck Radiotherapy

scholarly journals Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?

2021 ◽  
Author(s):  
Federica Vernuccio ◽  
Federica Arnone ◽  
Roberto Cannella ◽  
Barbara Verro ◽  
Albert Comelli ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Parotid Gland ◽  
Head And Neck ◽  
Texture Analysis ◽  
Pleomorphic Adenoma ◽  
Diagnostic Performance ◽  
Model Based ◽  
Parotid Gland Tumors ◽  
Mri Sequences ◽  
Sensitivity Specificity

Objective: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. Methods: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists’ diagnosis, and radiomic models were evaluated. Results: Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T2 weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma. Conclusion: MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. Advances in knowledge: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T2 weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

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