Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer

M. Avanzo, M. Trovo, C. Furlan, L. Barresi, A. Linda, J. Stancanello, L. Andreon, E. Minatel, M. Bazzocchi, M. G. Trovo, E. Capra

Research output: Contribution to journalArticle

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Abstract

Purpose: To derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up. Methods and materials: Lyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined. Results: The α/βs obtained with different models were 2.7-3.2Gy. The thresholds and optimal doses curves were EUDs of 3.2-7.8Gy and 15.2-18.1Gy with LEUD, LogEUD and RS models, and μd of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients' values of EUD and μd. Conclusions: The models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalPhysica Medica
Volume31
Issue number1
DOIs
Publication statusPublished - 2015

Fingerprint

Acute Lung Injury
Lung Injury
lungs
radiation therapy
Lung Neoplasms
Radiotherapy
cancer
Radiation
dosage
radiation
ROC Curve
Area Under Curve
Differential Diagnosis
tumors
Recurrence
Neoplasms
thresholds
curves
receivers
performance prediction

Keywords

  • IMRT
  • Lung injury
  • Lung tumors
  • NTCP
  • Tomotherapy

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Physics and Astronomy(all)
  • Medicine(all)

Cite this

Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer. / Avanzo, M.; Trovo, M.; Furlan, C.; Barresi, L.; Linda, A.; Stancanello, J.; Andreon, L.; Minatel, E.; Bazzocchi, M.; Trovo, M. G.; Capra, E.

In: Physica Medica, Vol. 31, No. 1, 2015, p. 1-8.

Research output: Contribution to journalArticle

Avanzo, M, Trovo, M, Furlan, C, Barresi, L, Linda, A, Stancanello, J, Andreon, L, Minatel, E, Bazzocchi, M, Trovo, MG & Capra, E 2015, 'Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer', Physica Medica, vol. 31, no. 1, pp. 1-8. https://doi.org/10.1016/j.ejmp.2014.10.006
Avanzo, M. ; Trovo, M. ; Furlan, C. ; Barresi, L. ; Linda, A. ; Stancanello, J. ; Andreon, L. ; Minatel, E. ; Bazzocchi, M. ; Trovo, M. G. ; Capra, E. / Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer. In: Physica Medica. 2015 ; Vol. 31, No. 1. pp. 1-8.
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abstract = "Purpose: To derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up. Methods and materials: Lyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined. Results: The α/βs obtained with different models were 2.7-3.2Gy. The thresholds and optimal doses curves were EUDs of 3.2-7.8Gy and 15.2-18.1Gy with LEUD, LogEUD and RS models, and μd of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients' values of EUD and μd. Conclusions: The models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.",
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AU - Avanzo, M.

AU - Trovo, M.

AU - Furlan, C.

AU - Barresi, L.

AU - Linda, A.

AU - Stancanello, J.

AU - Andreon, L.

AU - Minatel, E.

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AU - Trovo, M. G.

AU - Capra, E.

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N2 - Purpose: To derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up. Methods and materials: Lyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined. Results: The α/βs obtained with different models were 2.7-3.2Gy. The thresholds and optimal doses curves were EUDs of 3.2-7.8Gy and 15.2-18.1Gy with LEUD, LogEUD and RS models, and μd of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients' values of EUD and μd. Conclusions: The models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.

AB - Purpose: To derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up. Methods and materials: Lyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined. Results: The α/βs obtained with different models were 2.7-3.2Gy. The thresholds and optimal doses curves were EUDs of 3.2-7.8Gy and 15.2-18.1Gy with LEUD, LogEUD and RS models, and μd of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients' values of EUD and μd. Conclusions: The models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.

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