MR imaging prediction of local control of nasopharyngeal carcinoma treated with radiation therapy and chemotherapy

Wang, Y; Zhao, Hongyong; Zhang, Zhi‐Qiang; Ll, Huang; Y, Ye; Yb, Wang; Mj, Han

doi:10.1259/bjr.20130657

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Bakst et al hypothesized that tumor invasion and spread were of great significance to the prognosis of NPC ( 33 ). Invasion of the skull base ( 3 , 34 , 35 ) and parapharyngeal space ( 36 , 37 ) have often been used to analyze the prognosis of NPC. Huang et al identified parapharyngeal space involvement based on the number of subspaces involved (prestyloid space, carotid space, and areas outside the carotid space).…”

Section: Discussionmentioning

confidence: 99%

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

Chen¹,

Li²,

Yang³

et al. 2023

Quant Imaging Med Surg

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Background: Tumor invasion risk (TIR) is an important prognostic factor in nasopharyngeal carcinoma (NPC). We propose a novel prognostic analytic method for NPC based on a voxelwise analysis of TIR in a coordinate system of the nasopharynx.Methods: A stable nasopharynx coordinate system was constructed based on anatomical landmarks to obtain an accurate TIR profile for NPC. The coordinate system was validated by image registration of the lateral pterygoid muscle (LPM). The tumors were registered to the coordinate system through shift, scale, and rotation transformations. The voxelwise TIR map for NPC was obtained by superposition of all registered and mirrored tumor regions of interest. The minimum risk (MinR) point of the tumor region was used as an independent prognostic factor for NPC. The cutoff value was calculated with density plot and validated with restricted cubic splines (RCSs), and then the patients were divided into 2 groups for overall survival (OS) analysis. Results:The first voxelwise TIR map of NPC was obtained based on 778 patients. The OS of patients with a low TIR was 76.8% and was 92.6% for patients with a high TIR [P<0.001; hazard ratio (HR) =1/0.45; 95% CI: 0.27-0.77; adjusted P=0.004]. Thus, patients with a low TIR had a poor prognosis, whereas patients with a high TIR had a good prognosis. The MinR may be better at grading the prognosis of patients compared to the American Joint Committee on Cancer (AJCC) staging or tumor/node (T/N) classification systems. Conclusions:The voxelwise TIR map provides a new method for the prognostic analysis of NPC.Potential clinical applications of voxelwise TIR mapping are clinical target volume (CTV) delineation and dose-painting for NPC.

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Section: Discussionmentioning

confidence: 99%

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

Chen¹,

Li²,

Yang³

et al. 2023

Quant Imaging Med Surg

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“…52 The predictive value of semantic imaging features directly translates to the clinical TNM staging based on radiologic image evaluation. 53 Furthermore, Wang et al 54 showed that tumor invasion and heterogeneity evaluation on MRI were predictive for local control in patients with nasopharyngeal cancer.…”

Section: Semantic Image Evaluationmentioning

confidence: 99%

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges

Dijk

Fuller

2021

American Society of Clinical Oncology Educational Book

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The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for “learning algorithms”); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.

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Argumentaire clinique pour la radiothérapie guidée par imagerie par résonance magnétique

Maingon

2016

Cancer/Radiothérapie

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MR imaging prediction of local control of nasopharyngeal carcinoma treated with radiation therapy and chemotherapy

Cited by 7 publications

References 19 publications

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges

Argumentaire clinique pour la radiothérapie guidée par imagerie par résonance magnétique

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