Literature DB >> 28939226

Geometric Image Biomarker Changes of the Parotid Gland Are Associated With Late Xerostomia.

Lisanne V van Dijk1, Charlotte L Brouwer2, Hans Paul van der Laan2, Johannes G M Burgerhof3, Johannes A Langendijk2, Roel J H M Steenbakkers2, Nanna M Sijtsema2.   

Abstract

PURPOSE: To identify a surrogate marker for late xerostomia 12 months after radiation therapy (Xer12m), according to information obtained shortly after treatment. METHODS AND MATERIALS: Differences in parotid gland (PG) were quantified in image biomarkers (ΔIBMs) before and 6 weeks after radiation therapy in 107 patients. By performing stepwise forward selection, ΔIBMs that were associated with Xer12m were selected. Subsequently other variables, such as PG dose and acute xerostomia scores, were added to improve the prediction performance. All models were internally validated.
RESULTS: Prediction of Xer12m based on PG surface reduction (ΔPG-surface) was good (area under the receiver operating characteristic curve, 0.82). Parotid gland dose was related to ΔPG-surface (P<.001, R2 = 0.27). The addition of acute xerostomia scores to the ΔPG-surface improved the prediction of Xer12m significantly, and vice versa. The final model including ΔPG-surface and acute xerostomia had outstanding performance in predicting Xer12m early after radiation therapy (area under the receiver operating characteristic curve, 0.90).
CONCLUSIONS: Parotid gland surface reduction was associated with late xerostomia. The early posttreatment model with ΔPG-surface and acute xerostomia scores can be considered as a surrogate marker for late xerostomia.
Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28939226     DOI: 10.1016/j.ijrobp.2017.08.003

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  13 in total

1.  CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

Authors:  Shayan Mostafaei; Hamid Abdollahi; Shiva Kazempour Dehkordi; Isaac Shiri; Abolfazl Razzaghdoust; Seyed Hamid Zoljalali Moghaddam; Afshin Saadipoor; Fereshteh Koosha; Susan Cheraghi; Seied Rabi Mahdavi
Journal:  Radiol Med       Date:  2019-09-24       Impact factor: 3.469

2.  A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

Authors:  Kuo Men; Huaizhi Geng; Haoyu Zhong; Yong Fan; Alexander Lin; Ying Xiao
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-06-13       Impact factor: 7.038

Review 3.  Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques.

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

4.  Delta radiomics: a systematic review.

Authors:  Valerio Nardone; Alfonso Reginelli; Roberta Grassi; Luca Boldrini; Giovanna Vacca; Emma D'Ippolito; Salvatore Annunziata; Alessandra Farchione; Maria Paola Belfiore; Isacco Desideri; Salvatore Cappabianca
Journal:  Radiol Med       Date:  2021-12-04       Impact factor: 3.469

5.  Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy.

Authors:  Benjamin S Rosen; Peter G Hawkins; Daniel F Polan; James M Balter; Kristy K Brock; Justin D Kamp; Christina M Lockhart; Avraham Eisbruch; Michelle L Mierzwa; Randall K Ten Haken; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-07-10       Impact factor: 7.038

6.  Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images.

Authors:  Yanxia Liu; Hongyu Shi; Sijuan Huang; Xiaochuan Chen; Huimin Zhou; Hui Chang; Yunfei Xia; Guohua Wang; Xin Yang
Journal:  Quant Imaging Med Surg       Date:  2019-07

7.  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

Review 8.  Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).

Authors:  Rima Hajjo; Dima A Sabbah; Sanaa K Bardaweel; Alexander Tropsha
Journal:  Diagnostics (Basel)       Date:  2021-04-21

9.  Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

Authors:  Hubert S Gabryś; Florian Buettner; Florian Sterzing; Henrik Hauswald; Mark Bangert
Journal:  Front Oncol       Date:  2018-03-05       Impact factor: 6.244

10.  Radiation-induced parotid changes in oropharyngeal cancer patients: the role of early functional imaging and patient-/treatment-related factors.

Authors:  Simona Marzi; Alessia Farneti; Antonello Vidiri; Francesca Di Giuliano; Laura Marucci; Filomena Spasiano; Irene Terrenato; Giuseppe Sanguineti
Journal:  Radiat Oncol       Date:  2018-10-01       Impact factor: 3.481

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