Yongqiang Li1, Ping Li2, Wenchien Hsi3, Zhengshan Hong2, Shen Fu4, Qing Zhang5. 1. Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China; Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China. 2. Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China; Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China. 3. Department of Radiation Oncology, University of Florida, Gainesville, FL 32610, USA; University of Florida Health Proton Therapy Institute, Jacksonville, FL 32206, USA. 4. Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai 200433, China; Department of Radiation Oncology, Shanghai Concord Cancer Hospital, Shanghai 200020, China; Soochow University Proton & Heavy Ion Medical Center, the State Key Laboratory of Radiation Medicine and Protection of Soochow University, Jiangsu 215325, China.. Electronic address: shen_fu@hotmail.com. 5. Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China; Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China. Electronic address: qing.zhang@sphic.org.cn.
Abstract
PURPOSE: To estimate the Lyman Kutcher Burman (LKB) and multivariate NTCP models predicting the AUT of prostate cancer treated with CIRT. MATERIALS AND METHODS: A cohort of 154 prostate adenocarcinoma patients were retrospectively analyzed. The AUT levels were graded according to CTCAE 4.03. Based on dosimetric parameters and/or clinical factors, a set of variables with best-fit values determined in the two models was validated by the area under the receiver operating characteristic curve (AUC) and used to correlate the predicted and observed NTCP rates for both levels and related endpoints. RESULT: 59 (38.3%) patients experienced AUT. For LKB model, the equivalent uniform doses (EUDs) were calculated to be 62.0 GyE (following V61.5 > 1.7%) and 61.2 GyE (following maximum dose > 63.0 GyE) with predicted NTCP rates of 37.0% (AUC: 0.71) and 15.6% (AUC: 0.65) for AUT G1&2 and G2 of bladder. While for the multivariate model, the predicted NTCP rates was 37.1% (AUC: 0.70) and 20.2% (AUC: 0.64) for AUT G1&2 and G2, associated with V61 and V65, respectively. Nocturia was associated with bladder volume and maximum dose for G1&2, with patient's age and maximum bladder dose for G2. Other predictable endpoints were associated with V≥61. The predicted NTCPs agree with the observed complication rates for bladder and its wall. CONCLUSIONS: The LKB model successfully predicted the NTCP rates of both AUT levels and urgency urination. The multivariate model predicted well on both levels and nocturia. Decreasing high bladder dose volume may reduce the incidence of AUT.
PURPOSE: To estimate the Lyman Kutcher Burman (LKB) and multivariate NTCP models predicting the AUT of prostate cancer treated with CIRT. MATERIALS AND METHODS: A cohort of 154 prostate adenocarcinomapatients were retrospectively analyzed. The AUT levels were graded according to CTCAE 4.03. Based on dosimetric parameters and/or clinical factors, a set of variables with best-fit values determined in the two models was validated by the area under the receiver operating characteristic curve (AUC) and used to correlate the predicted and observed NTCP rates for both levels and related endpoints. RESULT: 59 (38.3%) patients experienced AUT. For LKB model, the equivalent uniform doses (EUDs) were calculated to be 62.0 GyE (following V61.5 > 1.7%) and 61.2 GyE (following maximum dose > 63.0 GyE) with predicted NTCP rates of 37.0% (AUC: 0.71) and 15.6% (AUC: 0.65) for AUT G1&2 and G2 of bladder. While for the multivariate model, the predicted NTCP rates was 37.1% (AUC: 0.70) and 20.2% (AUC: 0.64) for AUT G1&2 and G2, associated with V61 and V65, respectively. Nocturia was associated with bladder volume and maximum dose for G1&2, with patient's age and maximum bladder dose for G2. Other predictable endpoints were associated with V≥61. The predicted NTCPs agree with the observed complication rates for bladder and its wall. CONCLUSIONS: The LKB model successfully predicted the NTCP rates of both AUT levels and urgency urination. The multivariate model predicted well on both levels and nocturia. Decreasing high bladder dose volume may reduce the incidence of AUT.