Factors predicting radiation pneumonitis in lung cancer patients: a retrospective study

Rancati, T.; Ceresoli, Giovanni Luca; Gagliardi, Giovanna; Schipani, Stefano; Cattaneo, Giovanni Mauro

doi:10.1016/s0167-8140(03)00119-1

Cited by 261 publications

(183 citation statements)

References 35 publications

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“…In 14 of 84 patients (16.7%), the development of grade ≥2 RP was detected. Similar to our results, no correlation was detected between MLD and RP development (p>0,4) (Rancati et al, 2003).…”

Section: Discussionsupporting

confidence: 90%

“…In a study performed by Rancati et al, univariate analysis showed that V20 and V30 were significant for RP development (p<0.05). It was highlighted that V20 was the most powerful marker of RP development when mitomycin was used (Rancati et al, 2003). In this study, we could not have such a result because we did not evaluate chemotherapy agents.…”

Section: Discussionmentioning

confidence: 87%

“…While some studies excluded GTV from the lung volume, others excluded PTV from the lung volume (Rancati et al, 2003;Schallenkamp et al, 2007;Ramella et al 2010).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Radiation Pneumonia Development Risk in Lung Cancer Cases

Yılmaz¹,

Adaş²,

Hiçsönmez³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 87%

See 1 more Smart Citation

Evaluation of the Radiation Pneumonia Development Risk in Lung Cancer Cases

Yılmaz¹,

Adaş²,

Hiçsönmez³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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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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“…Some DVH values are studied also, and their value with uncertainty is computed from DeVH. A number of dose-volume parameters has been proved to have predictive value for certain side effects, according to the literature 23,24,25,26,27 : V20 and V30 for lungs, V65 for bladder, V60 or V72 for rectum in IMRT prostate treatments. Special fractionation schemes for prostate treatments have been developed in our institution, being the standard for conventional conformal radiotherapy 50 Gy in 16 fractions in 22 days 28 , and a simultaneous boost technique for IMRT treatments with 54 Gy (PTV1), 57 Gy (PTV2) and 60 Gy (PTV3) in 3 Gy fractions 29 .…”

mentioning

confidence: 99%

A Novel Method for the Evaluation of Uncertainty in Dose–Volume Histogram Computation

Henríquez

Castrillón

2008

International Journal of Radiation Oncology*Biology*Physics

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Abstract.Dose volume histograms are a useful tool in state-of-the-art radiotherapy planning, and it is essential to be aware of their limitations. Dose distributions computed by treatment planning systems are affected by several sources of uncertainty such as algorithm limitations, measurement uncertainty in the data used to model the beam and residual differences between measured and computed dose, once the model is optimized. In order to take into account the effect of uncertainty, a probabilistic approach is proposed and a new kind of histogram, a dose-expected volume histogram, is introduced. The expected value of the volume in the region of interest receiving an absorbed dose equal or greater than a certain value is found using the probability distribution of the dose at each point. A rectangular probability distribution is assumed for this point dose, and a relationship is given for practical computations. This method is applied to a set of dose volume histograms for different regions of interest for 6 brain patients, 8 lung patients, 8 pelvis patients and 6 prostate patients planned for IMRT. These results show how dose computation uncertainty has effects on PTV coverage and, to a lesser extent, in dose to organs at risk. This method allows to quantify these effects.

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Factors predicting radiation pneumonitis in lung cancer patients: a retrospective study

Cited by 261 publications

References 35 publications

Evaluation of the Radiation Pneumonia Development Risk in Lung Cancer Cases

Evaluation of the Radiation Pneumonia Development Risk in Lung Cancer Cases

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

A Novel Method for the Evaluation of Uncertainty in Dose–Volume Histogram Computation

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