Regional differences in lung radiosensitivity after radiotherapy for non–small-cell lung cancer

Seppenwoolde, Yvette; Jaeger, Katrien De; Boersma, Liesbeth; Belderbos, J.; Lebesque, Joos V.

doi:10.1016/j.ijrobp.2004.04.037

Cited by 162 publications

(110 citation statements)

References 27 publications

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“…Inferior tumor position had been identified as significant in some previous publications. [14,15] The commonly used values of V20 and MLD do not take tumor position into account. This equation and nomogram ( Figures 6A, B, and C) perform well for a wide variety of irradiation patterns in this large database.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram to Predict Radiation Pneumonitis, Derived From a Combined Analysis of RTOG 9311 and Institutional Data

Bradley

Hope

Naqa

et al. 2007

International Journal of Radiation Oncology*Biology*Physics

160

109

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Section: Discussionmentioning

confidence: 99%

A Nomogram to Predict Radiation Pneumonitis, Derived From a Combined Analysis of RTOG 9311 and Institutional Data

Bradley

Hope

Naqa

et al. 2007

International Journal of Radiation Oncology*Biology*Physics

160

109

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“…Among such factors, the ones that appear with most frequency in published studies are the lung volume receiving >20 Gy (1,(9)(10)(11)(12)(13)(14)(15), lung volume receiving >30 Gy (9,14,16,17), and the mean lung dose (1,11,15,(17)(18)(19)(20). Other factors that have been identified are the lung volume receiving >5-13 Gy (21), >15 Gy (9), >40 Gy (14), and >45 Gy (14) and the tumor lobe location (22)(23)(24). In two recent studies, Kong et al (1) identified the lung volume receiving >20 Gy and the mean lung dose, and Hope et al (24) identified lower lobe tumor location, maximal lung dose, and minimal dose to the 35% volume receiving the highest doses as predictive of lung radiation pneumonitis.…”

Section: Introductionmentioning

confidence: 99%

Predicting Lung Radiotherapy-Induced Pneumonitis Using a Model Combining Parametric Lyman Probit With Nonparametric Decision Trees

Das¹,

Zhou²,

Zhang³

et al. 2007

International Journal of Radiation Oncology*Biology*Physics

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Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials:The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a "boosting" process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results:The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables.Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation.

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“…This hypothesis is supported by several reports in the literature. Lung dose-function metrics were found to improve predictive power for pulmonary toxicity compared to dose-volume metrics [6,7]. The mean functional V 20 (fV 20 ) (percent lung function receiving P20 Gy) for patients who developed Grade P3 pneumonitis was 4.3% greater (p = 0.09) than those who did not, and it was closer to statistical significance than the V 20 (percent lung volume receiving P20 Gy) (p = 0.33) [7].…”

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confidence: 99%

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

Kida

Bal

Kabus

et al. 2016

Radiotherapy and Oncology

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Radiation-induced lung injury has detrimental effects on lung function and is associated with radiation pneumonitis, which is a potentially fatal toxicity and occurs in up to 30% of lung cancer patients treated with radiotherapy [1,2]. Radiotherapy that selectively avoids irradiating highly-functional lung regions may reduce pulmonary toxicity [3][4][5]. This hypothesis is supported by several reports in the literature. Lung dose-function metrics were found to improve predictive power for pulmonary toxicity compared to dose-volume metrics [6,7]. The mean functional V 20 (fV 20 ) (percent lung function receiving P20 Gy) for patients who developed Grade P3 pneumonitis was 4.3% greater (p = 0.09) than those who did not, and it was closer to statistical significance than the V 20 (percent lung volume receiving P20 Gy) (p = 0.33) [7].Several modalities exist for pulmonary ventilation imaging [8][9][10][11]. Ventilation images can also be acquired by a method based on four-dimensional (4D) computed tomography (CT) and image processing/analysis [12,13], henceforth referred to as CT ventilation imaging. CT ventilation imaging has the potential for widespread clinical implementation, as 4D-CT is routinely acquired for treatment planning at many centers [14] and ventilation computation only involves image processing/analysis without extra scans to patients. Additionally, CT ventilation imaging has a shorter scan time, higher spatial resolution (the exact resolution is unknown), lower cost, and/or greater availability than other modalities.Validation studies for CT ventilation imaging have been focused on cross-modality image comparisons [15][16][17][18][19][20][21][22]. For example, studies with mechanically ventilated sheep have demonstrated strong correlations between CT ventilation and xenon-CT ventilation [15,16]. Human studies have also reported reasonable correlations with ventilation scintigraphy [21], single-photon emission CT (SPECT) ventilation [22] and other modalities [19,20]. Previously we have demonstrated that CT ventilation in SPECT ventilationdefined defect regions of interest (ROIs) is significantly lower than http://dx

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Regional differences in lung radiosensitivity after radiotherapy for non–small-cell lung cancer

Cited by 162 publications

References 27 publications

A Nomogram to Predict Radiation Pneumonitis, Derived From a Combined Analysis of RTOG 9311 and Institutional Data

A Nomogram to Predict Radiation Pneumonitis, Derived From a Combined Analysis of RTOG 9311 and Institutional Data

Predicting Lung Radiotherapy-Induced Pneumonitis Using a Model Combining Parametric Lyman Probit With Nonparametric Decision Trees

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

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