A two-variable linear model of parotid shrinkage during IMRT for head and neck cancer

Sara Broggi, Claudio Fiorino, Italo Dell'Oca, Nicola Dinapoli, Marta Paiusco, Alessandro Muraglia, Eleonora Maggiulli, Francesco Ricchetti, Vincenzo Valentini, Giuseppe Sanguineti, Giovanni Mauro Cattaneo, Nadia Di Muzio, Riccardo Calandrino

Research output: Contribution to journalArticle

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Abstract

Purpose: To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC). Materials: Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (ΔV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between ΔVcc/% and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a ΔVcc/% larger than the median value. Linear models of ΔV (continuous variable) based on the most predictive variables found at the MVA were developed. Results: Median ΔVcc/% were 6.95 cc and 26%, respectively. The most predictive independent variables of ΔVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p = 0.0002) and Dmean (OR: 1.059; p = 0.038). The main independent predictors of ΔV% at MVA were age (OR: 0.968; p = 0.041) and V40 (OR: 1.0338; p = 0.013). ΔVcc and ΔV% may be well described by the equations: ΔVcc = -2.44 + 0.076 Dmean (Gy) + 0.279 IPV (cc) and ΔV% = 34.23 + 0.192 V40 (%) - 0.2203 age (year). The predictive power of the ΔVcc model is higher than that of the ΔV% model. Conclusions: IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of ΔVcc and ΔV%. ΔVcc and ΔV% may be well described by bi-linear models including the above-mentioned variables.

Original languageEnglish
Pages (from-to)206-212
Number of pages7
JournalRadiotherapy and Oncology
Volume94
Issue number2
DOIs
Publication statusPublished - Feb 2010

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Head and Neck Neoplasms
Linear Models
Radiotherapy
Multivariate Analysis
Therapeutics

Keywords

  • Head and neck cancer
  • Parotid anatomic change
  • Parotid shrinkage predictivity

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Hematology

Cite this

A two-variable linear model of parotid shrinkage during IMRT for head and neck cancer. / Broggi, Sara; Fiorino, Claudio; Dell'Oca, Italo; Dinapoli, Nicola; Paiusco, Marta; Muraglia, Alessandro; Maggiulli, Eleonora; Ricchetti, Francesco; Valentini, Vincenzo; Sanguineti, Giuseppe; Cattaneo, Giovanni Mauro; Di Muzio, Nadia; Calandrino, Riccardo.

In: Radiotherapy and Oncology, Vol. 94, No. 2, 02.2010, p. 206-212.

Research output: Contribution to journalArticle

Broggi, Sara ; Fiorino, Claudio ; Dell'Oca, Italo ; Dinapoli, Nicola ; Paiusco, Marta ; Muraglia, Alessandro ; Maggiulli, Eleonora ; Ricchetti, Francesco ; Valentini, Vincenzo ; Sanguineti, Giuseppe ; Cattaneo, Giovanni Mauro ; Di Muzio, Nadia ; Calandrino, Riccardo. / A two-variable linear model of parotid shrinkage during IMRT for head and neck cancer. In: Radiotherapy and Oncology. 2010 ; Vol. 94, No. 2. pp. 206-212.
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abstract = "Purpose: To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC). Materials: Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (ΔV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between ΔVcc/{\%} and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a ΔVcc/{\%} larger than the median value. Linear models of ΔV (continuous variable) based on the most predictive variables found at the MVA were developed. Results: Median ΔVcc/{\%} were 6.95 cc and 26{\%}, respectively. The most predictive independent variables of ΔVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p = 0.0002) and Dmean (OR: 1.059; p = 0.038). The main independent predictors of ΔV{\%} at MVA were age (OR: 0.968; p = 0.041) and V40 (OR: 1.0338; p = 0.013). ΔVcc and ΔV{\%} may be well described by the equations: ΔVcc = -2.44 + 0.076 Dmean (Gy) + 0.279 IPV (cc) and ΔV{\%} = 34.23 + 0.192 V40 ({\%}) - 0.2203 age (year). The predictive power of the ΔVcc model is higher than that of the ΔV{\%} model. Conclusions: IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of ΔVcc and ΔV{\%}. ΔVcc and ΔV{\%} may be well described by bi-linear models including the above-mentioned variables.",
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AU - Fiorino, Claudio

AU - Dell'Oca, Italo

AU - Dinapoli, Nicola

AU - Paiusco, Marta

AU - Muraglia, Alessandro

AU - Maggiulli, Eleonora

AU - Ricchetti, Francesco

AU - Valentini, Vincenzo

AU - Sanguineti, Giuseppe

AU - Cattaneo, Giovanni Mauro

AU - Di Muzio, Nadia

AU - Calandrino, Riccardo

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N2 - Purpose: To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC). Materials: Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (ΔV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between ΔVcc/% and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a ΔVcc/% larger than the median value. Linear models of ΔV (continuous variable) based on the most predictive variables found at the MVA were developed. Results: Median ΔVcc/% were 6.95 cc and 26%, respectively. The most predictive independent variables of ΔVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p = 0.0002) and Dmean (OR: 1.059; p = 0.038). The main independent predictors of ΔV% at MVA were age (OR: 0.968; p = 0.041) and V40 (OR: 1.0338; p = 0.013). ΔVcc and ΔV% may be well described by the equations: ΔVcc = -2.44 + 0.076 Dmean (Gy) + 0.279 IPV (cc) and ΔV% = 34.23 + 0.192 V40 (%) - 0.2203 age (year). The predictive power of the ΔVcc model is higher than that of the ΔV% model. Conclusions: IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of ΔVcc and ΔV%. ΔVcc and ΔV% may be well described by bi-linear models including the above-mentioned variables.

AB - Purpose: To assess anatomical, clinical and dosimetric pre-treatment parameters, possibly predictors of parotid shrinkage during radiotherapy of head and neck cancer (HNC). Materials: Data of 174 parotids from four institutions were analysed; patients were treated with IMRT, with radical and adjuvant intent. Parotid shrinkage was evaluated by the volumetric difference (ΔV) between parotid volumes at the end and those at the start of the therapy, as assessed by CT images (MVCT for 40 patients, KVCT for 47 patients). Correlation between ΔVcc/% and a number of dosimetric, clinical and geometrical parameters was assessed. Univariate as well as stepwise logistic multivariate (MVA) analyses were performed by considering as an end-point a ΔVcc/% larger than the median value. Linear models of ΔV (continuous variable) based on the most predictive variables found at the MVA were developed. Results: Median ΔVcc/% were 6.95 cc and 26%, respectively. The most predictive independent variables of ΔVcc at MVA were the initial parotid volume (IPV, OR: 1.100; p = 0.0002) and Dmean (OR: 1.059; p = 0.038). The main independent predictors of ΔV% at MVA were age (OR: 0.968; p = 0.041) and V40 (OR: 1.0338; p = 0.013). ΔVcc and ΔV% may be well described by the equations: ΔVcc = -2.44 + 0.076 Dmean (Gy) + 0.279 IPV (cc) and ΔV% = 34.23 + 0.192 V40 (%) - 0.2203 age (year). The predictive power of the ΔVcc model is higher than that of the ΔV% model. Conclusions: IPV/age and Dmean/V40 are the major dosimetric and clinical/anatomic predictors of ΔVcc and ΔV%. ΔVcc and ΔV% may be well described by bi-linear models including the above-mentioned variables.

KW - Head and neck cancer

KW - Parotid anatomic change

KW - Parotid shrinkage predictivity

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