Literature DB >> 29958772

Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia.

Lisanne V van Dijk1, Maria Thor2, Roel J H M Steenbakkers3, Aditya Apte2, Tian-Tian Zhai3, Ronald Borra4, Walter Noordzij5, Cherry Estilo6, Nancy Lee7, Johannes A Langendijk3, Joseph O Deasy2, Nanna M Sijtsema3.   

Abstract

PURPOSE: This study investigated whether Magnetic Resonance image biomarkers (MR-IBMs) were associated with xerostomia 12 months after radiotherapy (Xer12m) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer12m. Additionally, improvement of the reference Xer12m model based on parotid gland dose and baseline xerostomia, with MR-IBMs was explored.
METHODS: Parotid gland MR-IBMs of 68 head and neck cancer patients were extracted from pre-treatment T1-weighted MR images, which were normalized to fat tissue, quantifying 21 intensity and 43 texture image characteristics. The performance of the resulting multivariable logistic regression models after bootstrapped forward selection was compared with that of the logistic regression reference model. Validity was tested in a small external cohort of 25 head and neck cancer patients.
RESULTS: High intensity MR-IBM P90 (the 90th intensity percentile) values were significantly associated with a higher risk of Xer12m. High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer12m (likelihood-ratio-test; p = 0.002), with an increase in internally validated AUC from 0.78 (reference model) to 0.83 (P90). The MR-IBM P90 model also outperformed the reference model (AUC = 0.65) on the external validation cohort (AUC = 0.83).
CONCLUSION: Pre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer12m. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Head and neck cancer; Image biomarkers; Magnetic Resonance Imaging; NTCP; Radiomics; Xerostomia

Mesh:

Substances:

Year:  2018        PMID: 29958772      PMCID: PMC6625348          DOI: 10.1016/j.radonc.2018.06.012

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


  16 in total

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Authors:  Kuo Men; Huaizhi Geng; Haoyu Zhong; Yong Fan; Alexander Lin; Ying Xiao
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Authors:  Sonja Stieb; Kendall Kiser; Lisanne van Dijk; Nadia Roxanne Livingstone; Hesham Elhalawani; Baher Elgohari; Brigid McDonald; Juan Ventura; Abdallah Sherif Radwan Mohamed; Clifton David Fuller
Journal:  Hematol Oncol Clin North Am       Date:  2019-10-31       Impact factor: 3.722

3.  Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer.

Authors:  Annarita Fanizzi; Giovanni Scognamillo; Alessandra Nestola; Santa Bambace; Samantha Bove; Maria Colomba Comes; Cristian Cristofaro; Vittorio Didonna; Alessia Di Rito; Angelo Errico; Loredana Palermo; Pasquale Tamborra; Michele Troiano; Salvatore Parisi; Rossella Villani; Alfredo Zito; Marco Lioce; Raffaella Massafra
Journal:  Front Med (Lausanne)       Date:  2022-09-23

4.  Predicting late radiation-induced xerostomia with parotid gland PET biomarkers and dose metrics.

Authors:  Joel R Wilkie; Michelle L Mierzwa; Keith A Casper; Charles S Mayo; Matthew J Schipper; Avraham Eisbruch; Francis P Worden; Issam El Naqa; Benjamin L Viglianti; Benjamin S Rosen
Journal:  Radiother Oncol       Date:  2020-04-06       Impact factor: 6.280

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

Authors:  Lisanne V van Dijk; Clifton D Fuller
Journal:  Am Soc Clin Oncol Educ Book       Date:  2021-03

6.  Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.

Authors:  Kyle J Lafata; Yushi Chang; Chunhao Wang; Yvonne M Mowery; Irina Vergalasova; Donna Niedzwiecki; David S Yoo; Jian-Guo Liu; David M Brizel; Fang-Fang Yin
Journal:  Med Phys       Date:  2021-06-02       Impact factor: 4.506

7.  Incorporation of Dosimetric Gradients and Parotid Gland Migration Into Xerostomia Prediction.

Authors:  Rosario Astaburuaga; Hubert S Gabryś; Beatriz Sánchez-Nieto; Ralf O Floca; Sebastian Klüter; Kai Schubert; Henrik Hauswald; Mark Bangert
Journal:  Front Oncol       Date:  2019-07-31       Impact factor: 6.244

8.  Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.

Authors:  Martina Sollini; Lidija Antunovic; Arturo Chiti; Margarita Kirienko
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-18       Impact factor: 9.236

Review 9.  Radiomics for radiation oncologists: are we ready to go?

Authors:  Loïg Vaugier; Ludovic Ferrer; Laurence Mengue; Emmanuel Jouglar
Journal:  BJR Open       Date:  2020-03-25

10.  A longitudinal study on parotid and submandibular gland changes assessed by magnetic resonance imaging and ultrasonography in post-radiotherapy nasopharyngeal cancer patients.

Authors:  Vincent W C Wu; Michael Tc Ying; Dora Lw Kwong; Pek-Lan Khong; Gary Kw Wong; Shing-Yau Tam
Journal:  BJR Open       Date:  2020-09-02
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