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10.3109/0284186X.2015.1016624

http://scihub22266oqcxt.onion/10.3109/0284186X.2015.1016624
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C4786010!4786010!25800857
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suck abstract from ncbi


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pmid25800857      Acta+Oncol 2015 ; 54 (10): 1796-804
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  • PREDICTING RADIATION-INDUCED VALVULAR HEART DAMAGE #MMPMID25800857
  • Cella L; Oh JH; Deasy JO; Palma G; Liuzzi R; D?Avino V; Conson M; Picardi M; Salvatore M; Pacelli R
  • Acta Oncol 2015[Nov]; 54 (10): 1796-804 PMID25800857show ga
  • Background: To develop a predictive multivariate normal tissue complication probability (NTCP) model for radiation-induced heart valvular damage (RVD). The influence of combined heart-lung irradiation on RVD development was included. Methods and Materials: Multivariate logistic regression modeling with the least absolute shrinkage and selection operator (LASSO) was used to build an NTCP model to predict RVD based on a cohort of 90 Hodgkin lymphoma patients treated with sequential chemo-radiation therapy. In addition to heart irradiation factors, clinical variables, along with left and right lung dose-volume histogram statistics, were included in the analysis. To avoid overfitting, 10-fold cross validation (CV) was used for LASSO logistic regression modeling, with 50 reshuffled cycles. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and Spearman?s correlation coefficient (Rs). Results: At a median follow-up time of 55 months (range, 12?92 months) after the end of radiation treatment, twenty-seven out of 90 patients (30%) manifested at least one kind of RVD (mild or moderate), with a higher incidence of left-sided valve defects (64%). Fourteen prognostic factors were frequently selected (more than 100/500 model fits) by LASSO, which included mainly heart and left lung dosimetric variables along with their volume variables. The averaged cross-validated performance was AUC-CV = 0.685 and Rs = 0.293. The overall performance of a final NTCP model for RVD obtained applying LASSO logistic regression to the full dataset was satisfactory (AUC= 0.84, Rs= 0.55, p < 0.001). Conclusion: LASSO proved to be an improved and flexible modeling method for variable selection. Applying LASSO, we showed, for the first time, the importance of jointly considering left lung irradiation and left lung volume size in the prediction of subclinical radiation related heart disease resulting in RVD.
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