Literature DB >> 24630535

Integrating pretreatment diffusion weighted MRI into a multivariable prognostic model for head and neck squamous cell carcinoma.

Maarten Lambrecht1, Ben Van Calster2, Vincent Vandecaveye3, Frederik De Keyzer3, Ilse Roebben3, Robert Hermans3, Sandra Nuyts4.   

Abstract

INTRODUCTION: In head and neck squamous cell carcinoma (HNSCC) the ability to anticipate an individual patient's outcome is very valuable. With this study we wanted to assess the prognostic value of pretreatment apparent diffusion coefficient (ADC) in a large patient population and integrate it into a multivariable prognostic model.
METHODS: From 2004 to 2010 175 patients with pathology proven HNSCC were included in this study. All patients underwent a pretreatment MRI with diffusion weighted imaging (DWI) using six b-values. For each tumor, three ADC values were calculated using different b-value combinations: ADC(low) (b 0-50-100 s/mm(2)), ADChigh (b 500-750-1000 s/mm(2)) and ADC(avg) (all b-values). The clinical and radiological variables included: tumor and nodal volume, tumor location and age. Disease recurrence was analyzed using competing risk regression. A prognostic model for disease recurrence was developed, and internal validation was performed using bootstrapping and by dividing patients in three equal sized groups based on prognosis.
RESULTS: One hundred and sixty-one patients were eligible for analysis. Median follow-up was 50 months (range 4-86). A total of 67 patients experienced disease recurrence during follow-up (42%). ADC(high) was a prognostic factor for disease recurrence (adjusted hazard ratio: 1.14 per 10(-4) mm(2)/s, 95% CI 1.04-1.25). Harrell's c-index of the multivariable prognostic model was 0.62 (95% CI 0.56-0.70) after internal validation. The validated 3-year disease recurrence rates for the groups with worst, intermediate, and best prognosis were 56%, 33% and 31% respectively.
CONCLUSION: Pretreatment ADC value derived from high b-values is an independent prognostic factor in HNSCC and increases the performance of a multivariable prognostic model in addition to known clinical and radiological variables. Integration of other biomarkers and external validation is necessary to ensure its clinical applicability.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Diffusion weighted imaging; Head and neck cancer; Prognostic model

Mesh:

Year:  2014        PMID: 24630535     DOI: 10.1016/j.radonc.2014.01.004

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  34 in total

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Authors:  Shu-Hang Ng; Chun-Ta Liao; Chien-Yu Lin; Sheng-Chieh Chan; Yu-Chun Lin; Tzu-Chen Yen; Joseph Tung-Chieh Chang; Sheung-Fat Ko; Kang-Hsing Fan; Hung-Ming Wang; Lan-Yan Yang; Jiun-Jie Wang
Journal:  Eur Radiol       Date:  2016-02-24       Impact factor: 5.315

2.  Adaptive Boost Target Definition in High-Risk Head and Neck Cancer Based on Multi-imaging Risk Biomarkers.

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Review 3.  Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications.

Authors:  Philip Touska; Steve E J Connor
Journal:  Br J Radiol       Date:  2019-09-24       Impact factor: 3.039

4.  Diffusion-Weighted Imaging of Nasopharyngeal Carcinoma: Can Pretreatment DWI Predict Local Failure Based on Long-Term Outcome?

Authors:  B K H Law; A D King; K S Bhatia; A T Ahuja; M K M Kam; B B Ma; Q Y Ai; F K F Mo; J Yuan; D K W Yeung
Journal:  AJNR Am J Neuroradiol       Date:  2016-05-05       Impact factor: 3.825

5.  Combining standardized uptake value of FDG-PET and apparent diffusion coefficient of DW-MRI improves risk stratification in head and neck squamous cell carcinoma.

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Journal:  Eur Radiol       Date:  2016-03-10       Impact factor: 5.315

6.  Prospective observer and software-based assessment of magnetic resonance imaging quality in head and neck cancer: Should standard positioning and immobilization be required for radiation therapy applications?

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Journal:  Pract Radiat Oncol       Date:  2014-12-17

7.  Diffusion-weighted imaging of nasopharyngeal carcinoma to predict distant metastases.

Authors:  Qi-Yong Ai; Ann D King; Benjamin King Hong Law; David Ka-Wai Yeung; Kunwar S Bhatia; Jing Yuan; Anil T Ahuja; Lok Yiu Sheila Wong; Brigette B Ma; Frankie Kwok Fai Mo; Michael K M Kam
Journal:  Eur Arch Otorhinolaryngol       Date:  2016-10-08       Impact factor: 2.503

8.  The COMPLETE trial: HolistiC early respOnse assessMent for oroPharyngeaL cancEr paTiEnts; Protocol for an observational study.

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Journal:  BMJ Open       Date:  2022-05-18       Impact factor: 3.006

9.  Pretreatment DWI with Histogram Analysis of the ADC in Predicting the Outcome of Advanced Oropharyngeal Cancer with Known Human Papillomavirus Status Treated with Chemoradiation.

Authors:  M Ravanelli; A Grammatica; M Maddalo; M Ramanzin; G M Agazzi; E Tononcelli; S Battocchio; P Bossi; M Vezzoli; R Maroldi; D Farina
Journal:  AJNR Am J Neuroradiol       Date:  2020-07-30       Impact factor: 3.825

10.  A prospective longitudinal assessment of MRI signal intensity kinetics of non-target muscles in patients with advanced stage oropharyngeal cancer in relationship to radiotherapy dose and post-treatment radiation-associated dysphagia: Preliminary findings from a randomized trial.

Authors: 
Journal:  Radiother Oncol       Date:  2018-09-08       Impact factor: 6.280

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