scholarly journals Role of diffusion-weighted imaging in head and neck lesions: Pictorial review

2017 ◽  
Vol 30 (4) ◽  
pp. 356-369 ◽  
Author(s):  
Neeraj Bhatt ◽  
Nishant Gupta ◽  
Neetu Soni ◽  
Kusum Hooda ◽  
Joshua M Sapire ◽  
...  
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Diffusion Weighted Imaging ◽  
Tissue Characterization ◽  
Head And Neck Cancers ◽  
Added Value ◽  
Primary Tumors ◽  
Diffusion Weighted ◽  
Pictorial Review ◽  
Clinical Scenarios

Head and neck cancers are very common worldwide, causing significant morbidity and mortality. Squamous cell carcinoma originating from the epithelial lining of the upper aerodigestive tract is the most common histology. Many patients with head and neck cancers present with advanced stage disease requiring aggressive treatment consisting of extensive surgery and chemo-radiation. Appropriate treatment planning as well as prognosis of tumors depends to a large extent on accurate histological diagnosis and differentiation of malignant from benign lesions. Routine imaging modalities such as computed tomography and magnetic resonance imaging give volumetric and morphologic information. However, these modalities cannot be reliably used as a substitute for biopsy in treatment planning. However, diffusion-weighted imaging has shown promise in tissue characterization for primary tumors and nodal metastases, differentiation of recurrent tumor from post therapeutic changes, prediction and monitoring of treatment response, and many other clinical scenarios as described later in this article. In this review article, we describe the imaging findings in applications of diffusion-weighted imaging in the head and neck lesions and discuss their added value over anatomic imaging.

scholarly journals Diffusion-weighted imaging in head and neck cancers

2009 ◽  
Vol 5 (7) ◽  
pp. 959-975 ◽  
Author(s):  
Sanjeev Chawla ◽  
Sungheon Kim ◽  
Sumei Wang ◽  
Harish Poptani
Keyword(s):  
Head And Neck ◽  
Diffusion Weighted Imaging ◽  
Head And Neck Cancers ◽  
Diffusion Weighted

Added value of diffusion-weighted imaging to conventional MRI for predicting fascial involvement of soft tissue sarcomas

2018 ◽  
Vol 29 (4) ◽  
pp. 1863-1873 ◽  
Author(s):  
Min A Yoon ◽  
Choong Guen Chee ◽  
Hye Won Chung ◽  
Joon Seon Song ◽  
Jong Seok Lee ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Diffusion Weighted Imaging ◽  
Soft Tissue Sarcomas ◽  
Added Value ◽  
Conventional Mri ◽  
Diffusion Weighted

scholarly journals Non-Gaussian Analysis of Diffusion Weighted Imaging in Head and Neck at 3T: A Pilot Study in Patients with Nasopharyngeal Carcinoma

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e87024 ◽  
Author(s):  
Jing Yuan ◽  
David Ka Wai Yeung ◽  
Greta S. P. Mok ◽  
Kunwar S. Bhatia ◽  
Yi-Xiang J. Wang ◽  
...  
Keyword(s):  
Pilot Study ◽  
Nasopharyngeal Carcinoma ◽  
Head And Neck ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Gaussian Analysis ◽  
Non Gaussian

scholarly journals Added value of diffusion weighted mri to conventional mri in the assessment of uterine cervix cancer post therapy

QJM ◽  
2020 ◽  
Vol 113 (Supplement_1) ◽  
Author(s):  
A M Nasr ◽  
O A Kamal ◽  
O F Kamel ◽  
S A N Hashim
Keyword(s):  
Cervical Cancer ◽  
Diffusion Weighted Imaging ◽  
Tumor Resection ◽  
Post Treatment ◽  
Added Value ◽  
Diffusion Weighted Mri ◽  
Conventional Mri ◽  
Diffusion Weighted ◽  
Adc Mapping ◽  
Post Therapy

Abstract Purpose of this study is: Assessing the role of Diffusion weighted imaging with ADC mapping in the evaluation of uterine cervical cancer post therapy regarding tumor residual, recurrence or post treatment benign changes/ complications after tumor resection and/or chemotherapy/radiotherapy. Methods The study included 48 female underwent cervical cancer treatment, referred to Radio diagnosis Department of National Cancer Institute for post therapy assessment. Each patient included in the study was subjected to full history taking, reviewing medical sheet and MR examination including: Conventional MR examination and Diffusion Weighted imaging. Results The study showed that the use of quantitative DW imaging with ADC mapping provide added value in the detection of post-treatment malignant masses and differentiating it from post-treatment benign changes. Conclusion The current application of diffusion Weighted MRI as a routine with conventional MRI sequences increased the accuracy of detection of post therapy benign and malignant masses , Our results suggested also that the use of ADC can be helpful in differentiating post-treatment malignant masses from benign post-treatment changes.

scholarly journals Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jens P.E. Schouten ◽  
Samantha Noteboom ◽  
Roland M. Martens ◽  
Steven W. Mes ◽  
C. René Leemans ◽  
...  
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Primary Tumor ◽  
Tumor Volume ◽  
Automatic Segmentation ◽  
Neck Region ◽  
Primary Tumors ◽  
Head And Neck Region ◽  
Primary Tumor Volume ◽  
Mri Sequences

Abstract Background  Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.

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