Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer. Issue 1 (February 2015)
- Record Type:
- Journal Article
- Title:
- Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer. Issue 1 (February 2015)
- Main Title:
- Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer
- Authors:
- Avanzo, M.
Trovo, M.
Furlan, C.
Barresi, L.
Linda, A.
Stancanello, J.
Andreon, L.
Minatel, E.
Bazzocchi, M.
Trovo, M.G.
Capra, E. - Abstract:
- <abstract xml:lang="en" abstract-type="author" id="abs0010"> <title id="sectitle0010">Abstract</title> <sec> <title id="sectitle0015">Purpose</title> <p id="abspara0010">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.</p> </sec> <sec> <title id="sectitle0020">Methods and materials</title> <p id="abspara0015">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.</p> </sec> <sec> <title id="sectitle0025">Results</title> <p id="abspara0020">The α/βs obtained with different models were 2.7–3.2 Gy. The thresholds and optimal doses curves were EUDs of 3.2–7.8 Gy and 15.2–18.1 Gy with LEUD, LogEUD and RS models, and μ<sub>d</sub><abstract xml:lang="en" abstract-type="author" id="abs0010"> <title id="sectitle0010">Abstract</title> <sec> <title id="sectitle0015">Purpose</title> <p id="abspara0010">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.</p> </sec> <sec> <title id="sectitle0020">Methods and materials</title> <p id="abspara0015">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.</p> </sec> <sec> <title id="sectitle0025">Results</title> <p id="abspara0020">The α/βs obtained with different models were 2.7–3.2 Gy. The thresholds and optimal doses curves were EUDs of 3.2–7.8 Gy and 15.2–18.1 Gy with LEUD, LogEUD and RS models, and μ<sub>d</sub> 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 μ<sub>d</sub>.</p> </sec> <sec> <title id="sectitle0030">Conclusions</title> <p id="abspara0025">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.</p> </sec> </abstract> … (more)
- Is Part Of:
- Physica medica. Volume 31:Issue 1(2015)
- Journal:
- Physica medica
- Issue:
- Volume 31:Issue 1(2015)
- Issue Display:
- Volume 31, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 31
- Issue:
- 1
- Issue Sort Value:
- 2015-0031-0001-0000
- Page Start:
- 1
- Page End:
- 8
- Publication Date:
- 2015-02
- Subjects:
- Medical physics -- Periodicals
Biophysics -- Periodicals
Biophysics -- Periodicals
Imagerie médicale -- Périodiques
Radiothérapie -- Périodiques
Rayons X -- Sécurité -- Mesures -- Périodiques
Physique -- Périodiques
Médecine -- Périodiques
610.153 - Journal URLs:
- http://www.sciencedirect.com/science/journal/11201797 ↗
http://www.clinicalkey.com/dura/browse/journalIssue/11201797 ↗
http://www.clinicalkey.com.au/dura/browse/journalIssue/11201797 ↗
http://www.elsevier.com/journals ↗
http://www.physicamedica.com ↗ - DOI:
- 10.1016/j.ejmp.2014.10.006 ↗
- Languages:
- English
- ISSNs:
- 1120-1797
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6475.070000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3507.xml