Schild, Steven E.

PurposeCurrently, in clinical practice of intensity‐modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization.Methods and materialsCao et al. have demonstrated a two‐stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings.ResultThe new method achieved better plan quality than the conventional method. The D95% of the clinical target volume (CTV) normalized to the prescription dose was (mean [min–max]): (99.4% [99.2%–99.6%]) vs. (99.2% [98.6%–99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose–volume histogram band gap (narrower band gap means more robust plans) at D95% normalized to the prescription dose was (mean [min–max]): (1.5% [0.5%–4.3%]) vs. (1.2% [0.6%–3.8%]).ConclusionOur new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine‐deliverable plans with better tumor coverage while maintaining high‐plan robustness.

Small-cell lung cancer (SCLC) has a high predilection for metastasizing to the brain after chemotherapy. This has been blamed on the blood-brain barrier, which prevents chemotherapy from penetrating into the brain, thus creating a sanctuary site. It has been estimated that up to three quarters of patients with SCLC will eventually develop brain metastases. This led investigators to administer prophylactic cranial irradiation (PCI) to decrease this risk. Several trials were performed in patients with SCLC after initial therapy (chemotherapy with or without thoracic radiotherapy) that compared the outcomes of PCI versus no PCI. Early trials generally found that PCI significantly decreased the risk of brain metastases but did not significantly improve survival. These trials were re-evaluated in two larger meta-analyses that included patients with either limited-stage SCLC or extensive-stage SCLC (ESCLC). Both meta-analyses reported that PCI significantly decreased brain metastases and improved survival in patients who had a complete response following initial therapy. These studies were performed before the advent of modern imaging with computed tomography or magnetic resonance imaging (MRI). There have been two modern trials of PCI versus no PCI in patients with ESCLC and both found that PCI decreases brain metastases. The first did not include brain MRI before registration and found that PCI improved survival, whereas the second study did include MRI before registration and at frequent intervals thereafter. That trial found that PCI did not confer a survival advantage. This review will examine the evidence and provide recommendations regarding the role of PCI for patients with ESCLC.

Purpose We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity‐modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs. Methods We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The “worst‐case robust optimization” model was applied. One additional term as a “biological surrogate (BS)” of OARs due to the high LET‐related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose‐averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in‐house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst‐case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited‐memory Broyden–Fletcher–Goldfarb–Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose–volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods. Results For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model. Conclusions Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.

AbstractBackgroundIn treatment planning for intensity‐modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed‐integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose‐volume constraints. In this study, we incorporated dose‐volume constraints in an MIP model to generate treatment plans for IMPT.MethodsWe created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited‐memory Broyden–Fletcher–Goldfarb–Shanno (L‐BFGS) method while the other plan was derived with our MIP model with dose‐volume constraints. We then compared these two plans by dose‐volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose‐volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection.ResultsThe MIP model with dose‐volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial‐and‐error process. Some notable reduction in the mean doses of OARs is observed.ConclusionsThe treatment plans from our MIP model with dose‐volume constraints can meet all dose‐volume constraints for OARs and targets without any tedious trial‐and‐error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

AbstractThis work is to show which is more relevant to cause local failures (LFs) due to patient setup uncertainty between the planning target volume (PTV) underdosage and the potential target underdosage subject to patient setup uncertainties in head and neck (H&N) cancer treated with volumetric‐modulated arc therapy (VMAT). Thirteen LFs in 10 H&N patients treated by VMAT were analyzed. Measures have been taken to minimize the chances of insufficient target delineation for these patients and the patients were clinically determined to have LF based on the PET/CT scan results by an experienced radiologist and then reviewed by a second experienced radiation oncologist. Two methods were used to identify the possible locations of LF due to underdosage: (a) examining the standard VMAT plan, in which the underdosed volume in the nominal dose distribution (UVN) was generated by subtracting the volumes receiving the prescription doses from PTVs, and (b) plan robustness analysis, in which in addition to the nominal dose distribution, six perturbed dose distributions were created by translating the CT iso‐center in three cardinal directions by the PTV margin. The coldest dose distribution was represented by the minimum of the seven doses in each voxel. The underdosed volume in the coldest dose distribution (UVC) was generated by subtracting the volumes receiving the prescription doses in the coldest dose distribution from the volumes receiving the prescription doses in the nominal dose distribution. UVN and UVC were subsequently examined for spatial association with the locations of LF. The association was tested using the binominal distribution and the Fisher's exact test of independence. We found that of 13 LFs, 11 were associated with UVCs (P = 0.011), while three were associated with UVNs (P = 0.99). We concluded that the possible target underdosage due to patient setup uncertainties appeared to be a more relevant factor associated with LF in VMAT for H&N cancer than the compromised PTV coverage at least for the patients included in this study.

Objective: High GATA2 expression has been associated with an increased risk of poor clinical outcomes after radical prostatectomy; however, this has not been studied in relation to risk of biochemical recurrence (BCR) after salvage radiation therapy (SRT) for recurrent prostate cancer after radical prostatectomy. Our aim was to evaluate the association between protein expression levels of GATA2 in primary prostate cancer tumour samples and the risk of BCR after SRT. Methods: 109 males who were treated with SRT were included. The percentage of cells with nuclear staining and GATA2 staining intensity were both measured. These two measures were multiplied together to obtain a GATA2 H-score (range 0–12) which was our primary GATA2 staining measure. Results: In unadjusted analysis, the risk of BCR was higher for patients with a GATA2 H-score >4 (hazard ratio = 2.04, p = 0.033). In multivariable analysis adjusting for SRT dose, pre-SRT PSA, pathological tumour stage and Gleason score, this association weakened substantially (hazard ratio = 1.45, p = 0.31). This lack of an independent association with BCR appears to be the result of correlations between GATA2 H-score >4 and higher pre-SRT PSA (p = 0.021), higher Gleason score (p = 0.044) and more severe pathological tumour stage (p = 0.068). Conclusion: Higher levels of GATA2 expression appear to be a marker of prostate cancer severity; however, these do not provide independent prognostic information regarding BCR beyond that of validated clinicopathological risk factors. Advances in knowledge: A higher GATA2 expression level appears to be correlated with known measures of prostate cancer severity and therefore is likely not an independent marker of outcome after SRT.