Mohan, Radhe

Abstract Background We investigated differences in radiation-induced grade 3+ lymphopenia (G3+L), defined as an absolute lymphocyte count (ALC) nadir of <500 cells/µL, after proton therapy (PT) or X-ray (photon) therapy (XRT) for patients with glioblastoma (GBM). Methods Patients enrolled in a randomized phase II trial received PT (n = 28) or XRT (n = 56) concomitantly with temozolomide. ALC was measured before, weekly during, and within 1 month after radiotherapy. Whole-brain mean dose (WBMD) and brain dose-volume indices were extracted from planned dose distributions. Univariate and multivariate logistic regression analyses were used to identify independent predictive variables. The resulting model was evaluated using receiver operating characteristic (ROC) curve analysis. Results Rates of G3+L were lower in men (7/47 [15%]) versus women (19/37 [51%]) (P < 0.001), and for PT (4/28 [14%]) versus XRT (22/56 [39%]) (P = 0.024). G3+L was significantly associated with baseline ALC, WBMD, and brain volumes receiving 5‒40 Gy(relative biological effectiveness [RBE]) or higher (ie, V5 through V40). Stepwise multivariate logistic regression analysis identified being female (odds ratio [OR] 6.2, 95% confidence interval [CI]: 1.95‒22.4, P = 0.003), baseline ALC (OR 0.18, 95% CI: 0.05‒0.51, P = 0.003), and whole-brain V20 (OR 1.07, 95% CI: 1.03‒1.13, P = 0.002) as the strongest predictors. ROC analysis yielded an area under the curve of 0.86 (95% CI: 0.79–0.94) for the final G3+L prediction model. Conclusions Sex, baseline ALC, and whole-brain V20 were the strongest predictors of G3+L for patients with GBM treated with radiation and temozolomide. PT reduced brain volumes receiving low and intermediate doses and, consequently, reduced G3+L.

AbstractRobust optimization of intensity‐modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so‐called “worst case dose” and “minmax” robust optimization approaches and conventional planning target volume (PTV)‐based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull‐based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP‐PTV‐based, NLP‐PTV‐based, LP‐worst case dose, NLP‐worst case dose, LP‐minmax, and NLP‐minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP‐based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP‐based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP‐based methods was superior for the skull‐based and head and neck cancer patients. Overall, LP‐based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull‐based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV‐based optimization, and the NLP‐PTV‐based optimization outperformed the LP‐PTV‐based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP‐based methods had notably fewer scanning spots than did those generated using NLP‐based methods.

Respiratory motion is traditionally assessed using tumor motion magnitude. In proton therapy, respiratory motion causes density variations along the beam path that result in uncertainties of proton range. This work has investigated the use of water‐equivalent thickness (WET) to quantitatively assess the effects of respiratory motion on calculated dose in passively scattered proton therapy (PSPT). A cohort of 29 locally advanced non‐small cell lung cancer patients treated with 87 PSPT treatment fields were selected for analysis. The variation in WET (ΔWET) along each field was calculated between exhale and inhale phases of the simulation four‐dimensional computed tomography. The change in calculated dose (ΔDose) between full‐inhale and full‐exhale phase was quantified for each field using dose differences, 3D gamma analysis, and differential area under the curve (ΔAUC) analysis. Pearson correlation coefficients were calculated between ΔDose and ΔWET. Three PSPT plans were redesigned using field angles to minimize variations in ΔWET and compared to the original plans. The median ΔWET over 87 treatment fields ranged from 1‐9 mm, while the ΔWET 95th percentile value ranged up to 42 mm. The ΔWET was significantly correlated to the ΔDose for all metrics analyzed. The patient plans that were redesigned using ΔWET analysis to select field angles were more robust to the effects of respiratory motion, as ΔAUC values were reduced by more than 60% in all three cases. The tumor motion magnitude alone does not capture the potential dosimetric error due to respiratory motion because the proton range is sensitive to the motion of all patient anatomy. The use of ΔWET has been demonstrated to identify situations where respiratory motion can impact the calculated dose. Angular analysis of ΔWET may be capable of designing radiotherapy plans that are more robust to the effects of respiratory motion.PACS number(s): 87.55.‐x