Faculty Bibliography

INTRODUCTION/BACKGROUND:In KRAS-mutant NSCLC, co-occurring alterations in LKB1 confer a negative prognosis compared with other mutations such as TP53. LKB1 is a tumor suppressor that coordinates several signaling pathways in response to energetic stress. Our recent work on pharmacologic and genetic inhibition of histone deacetylase 6 (HDAC6) revealed the impaired activity of numerous enzymes involved in glycolysis. On the basis of these previous findings, we explored the therapeutic window for HDAC6 inhibition in metabolically-active KRAS-mutant lung tumors. METHODS:Using cell lines derived from mouse autochthonous tumors bearing the KRAS/LKB1 (KL) and KRAS/TP53 mutant genotypes to control for confounding germline and somatic mutations in human models, we characterize the metabolic phenotypes at baseline and in response to HDAC6 inhibition. The impact of HDAC6 inhibition was measured on cancer cell growth in vitro and on tumor growth in vivo. RESULTS:Surprisingly, KL-mutant cells revealed reduced levels of redox-sensitive cofactors at baseline. This is associated with increased sensitivity to pharmacologic HDAC6 inhibition with ACY-1215 and blunted ability to increase compensatory metabolism and buffer oxidative stress. Seeking synergistic metabolic combination treatments, we found enhanced cell killing and antitumor efficacy with glutaminase inhibition in KL lung cancer models in vitro and in vivo. CONCLUSIONS:Exploring the differential metabolism of KL and KRAS/TP53-mutant NSCLC, we identified decreased metabolic reserve in KL-mutant tumors. HDAC6 inhibition exploited a therapeutic window in KL NSCLC on the basis of a diminished ability to compensate for impaired glycolysis, nominating a novel strategy for the treatment of KRAS-mutant NSCLC with co-occurring LKB1 mutations.

PURPOSE/OBJECTIVE:Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. MATERIALS/METHODS/METHODS:Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. RESULTS:The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78. CONCLUSIONS:Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.

PURPOSE/OBJECTIVE:The treatment couch position of a patient in external beam radiation therapy (EBRT) is usually acquired during initial treatment setup. This procedure has shown potential failure modes leading to near misses and adverse events in radiation treatment. This study aims to develop a method to automatically determine the couch position before setting up a patient for initial treatment. METHODS:The Qfix couch-tops (kVue and DoseMax) have embedded reference marks (BBs) indicating its index levels and couch centerline. With the ESAPI, a C# script was programmed to automatically find the couch-top and embedded BBs in the planning CT and derive the treatment couch position according to treatment isocenter of a plan. Couch positions of EBRT plans with the kVue couch-top and SBRT plans using the DoseMax were calculated using the script. The calculation was evaluated by comparing calculated positions with couch coordinates captured during the initial treatment setup after image guidance. The calculations were further compared with daily treatment couch positions post image-guided adjustment for each treatment fraction. RESULTS:For plans using the kVue couch-top for various treatment sites, the median (5-95 percentiles) differences between calculated and captured couch positions were 0.1 (-0.2 - 0.9), 0.5 (-1.1-2.0), 0.10 (-1.3-1.3) cm in the vertical, longitudinal, and lateral direction respectively. For the DoseMax couch-top, the median differences were 0.1 (-0.2-0.7), 0.2 (-0.3-1.1), and 0.2 (-0.7-0.9) cm in respective direction. The calculated positions were within 1 and 2 cm from the mean fraction positions for 95% patients on DoseMax and kVue couch-top respectively. CONCLUSIONS:A method that automatically and accurately calculates treatment couch position from simulation CT was implemented in Varian Eclipse for Qfix couch-tops. This technique increases the efficiency of patient setup and enhances patient safety by reducing the risks of positioning errors.

PURPOSE/OBJECTIVE:The purpose of this study is to quantify dosimetric changes throughout the delivery of oropharyngeal cancer treatment and to investigate the application of statistical process control (SPC) for the management of significant deviations during the course of radiotherapy. METHODS:Thirteen oropharyngeal cancer patients with daily cone beam computed tomography (CBCT) were retrospectively reviewed. Cone beam computed tomography images of every other fraction were imported to the Velocity software and registered to planning CT using the 6 DOF (degrees of freedom) couch shifts generated during patient setup. Using Velocity "Adaptive Monitoring" module, the setup-corrected CBCT was matched to planning CT using a deformable registration. Volumes and dose metrics at each fraction were calculated and rated with plan values to evaluate interfractional dosimetric variations using a SPC framework. T-tests between plan and fraction volumes were performed to find statistically insignificant fractions. Average upper and lower process capacity limits (UCL, LCL) of each dose metric were derived from these fractions using conventional SPC guidelines. RESULTS:Gross tumor volume (GTV) and organ at risk (OAR) volumes in the first 13 fractions had no significant changes from the pretreatment planning CT. The GTV and the parotid glands subsequently decreased by 10% at the completion of treatment. There were 3-4% increases in parotid mean doses, but no significant differences in dose metrics of GTV and other OARs. The changes were organ and patient dependent. Control charts for various dose metrics were generated to assess the metrics at each fraction for individual patient. CONCLUSIONS:Daily CBCT could be used to monitor dosimetric variations of targets and OARs resulting from volume changes and tissue deformation in oropharyngeal cancer radiotherapy. Treatment review with the guidance of a SPC tool allows for an objective and consistent clinical decision to apply adaptive radiotherapy.

PURPOSE/OBJECTIVE:Through-plane motion introduces uncertainty in 3D motion monitoring when using single slice on board imaging (OBI) modalities such as cine MRI. We propose a principal component analysis (PCA) based framework to determine the optimal imaging plane to minimize the through-plane motion for single slice imaging based motion monitoring METHODS: Four-dimensional computed tomography (4DCT) images of 8 thoracic cancer patients were retrospectively analyzed. The target volumes were manually delineated at different respiratory phases of 4DCT. We performed automated image registration to establish the 4D respiratory target motion trajectories for all patients. PCA was conducted using the motion information to define the three principal components of the respiratory motion trajectories. Two imaging planes were determined perpendicular to the second and third principal component respectively to avoid imaging with the primary principal component of the through-plane motion. Single slice images were reconstructed from 4DCT in the PCA-derived orthogonal imaging planes, and were compared against the traditional AP/Lateral image pairs on through-plane motion, residual error in motion monitoring, absolute motion amplitude error, and the similarity between target segmentations at different phases. We evaluated the significance of the proposed motion monitoring improvement using paired t-test analysis. RESULTS:The PCA-determined imaging planes had overall less through-plane motion compared against the AP/Lateral image pairs. For all patients, the average through-plane motion was 3.6 mm (range: 1.6-5.6 mm) for the AP view, and 1.7 mm (range: 0.6-2.7 mm) for the lateral view. With PCA optimization, the average through-plane motion was 2.5 mm (range: 1.3-3.9 mm) and 0.6 mm (range: 0.2-1.5 mm) for the two imaging planes respectively. The absolute residual error of the reconstructed max-exhale-to-inhale motion averaged 0.7 mm (range: 0.4-1.3 mm, 95% CI: 0.4-1.1 mm) using optimized imaging planes, averaged 0.5 mm (range: 0.3-1.0 mm, 95% CI: 0.2-0.8 mm) using an imaging plane perpendicular to the minimal motion component only, and averaged 1.3 mm (range: 0.4-2.8 mm, 95% CI: 0.4-2.3 mm) in AP/Lat orthogonal image pairs. The root mean square error of reconstructed displacement was 0.8 mm for optimized imaging planes, 0.6 mm for imaging plane perpendicular to the minimal motion component only, and 1.6 mm for AP/Lat orthogonal image pairs. When using the optimized imaging planes for motion monitoring, there was no significant absolute amplitude error of the reconstructed motion (p=0.0988), while AP/Lat images had significant error (p=0.0097) with a paired t-test. The average surface distance (ASD) between overlaid 2D tumor segmentation at end-of-inhale and end-of-exhale for all eight patients was 0.6±0.2 mm in optimized imaging planes and 1.4±0.8 mm in AP/Lat images. The Dice similarity coefficient (DSC) between overlaid 2D tumor segmentation at end-of-inhale and end-of-exhale for all eight patients was 0.96±0.03 in optimized imaging planes and 0.89±0.05 in AP/Lat images. Both ASD (p=0.034) and DSC (p=0.022) were significantly improved in the optimized imaging planes. CONCLUSIONS:Motion monitoring using imaging planes determined by the proposed PCA-based framework had significantly improved performance. Single slice image based motion tracking can be used for clinical implementations such as MR Image Guided Radiation Therapy (MR-IGRT).

Purpose: Air cavity in the digestive tract affects the dose distribution of radiation therapy plans when locates in the beam path. The number, size, and location of air cavity changes during the treatment course and the overall impact to different treatment approaches needs to be evaluated. Methods: We retrospectively picked 30 pancreas cancer patients who received radiation therapy. All patients were setup pretreatment based on daily acquired CBCT (OBI on TrueBeam, Varian Medical). For each patient, one VMAT, one fixed angle IMRT, and one traditional 4 fields 3DCRT plan have been generated. The daily CBCTwere manually registered to the planning CT. In each CBCT set, the air cavity, PTV, and selected OARs were contoured. To remove the impact caused by the uncertainty of CBCT HU calibration, the patient body was assigned with mass density of 1 g/cc except the air cavity, which was assigned to 0 g/cc. All three plans were copied to the CBCT and the dose volumetric histogram of the PTVand OARs were computed. For each type of plan, the cumulative dose from all CBCTs were compared against each other and the planning CT based plan. Results: Preliminary results illustrated that the 3DCRTwas the least vulnerable treatment type to the variation of air cavities. For 3DCRT plans the average variations of the max, min, and mean dose of PTV in the CBCT plans and the planning CTwere 0.8%, 0.3%, and 0.2%, with standard deviation of 1.3%, 0.7%, and 1.1%; for IMRT the average variations were 1.1%, 0.8%, and 0.7%, with standard deviation of 1.2%, 1.1%, and 1.5%; for VMAT the average variations were 2.3%, 2.4%, and 1.5%, with standard deviations 0.5%, 0.4%, and 0.6%. Conclusion: The impact of air cavity maybe significant for certain types of treatment approaches and clinical decisions needs to be made accordingly

Purpose: Recently the Gamma Index of registered CBCT and CT, combining mass density and distance-to-agreement (DTA), has been used as an effective means to evaluate the quality of image guided pretreatment setup, and to identify radiation-induced patient anatomy change. The pass/fail of the Gamma analysis relies heavily on the Gamma criteria. We propose a regression model based on multiple patient data to quantitatively determine the optimal HU and DTA criteria for CBCT Gamma analysis. Methods: We retrospectively analyzed daily setup kV-CBCT (acquired using OBI on True- Beam, Varian Medical) from 10 H&N, 10 thoracic, and 10 abdominal cancer patients. The registration between CBCT and the planning CTwas conducted online by therapists and reviewed by radiation oncologist. Visible region of interests were contoured in the CBCT by experts as the ground truth of CT/ CBCT similarity. Gamma analyses using different levels of criteria, combinations of mass density 0.2 g/cc, 01 g/cc, and 0.05 g/cc, and DTA 3 mm, 2 mm, and 1 mm, were repeated on CBCT using the MobiusCB (by Mobius Medical System) software. The Gamma passing rate of the total irradiated volume and selected region of interests at different levels of gamma criteria were used as training data for multi-variated parametric regression model to determine the optimal gamma criteria. Results: Based on the preliminary data, the optimal Gamma criteria is 0.5 g/cc and 3 mm for head&neck patients, 0.2 g/cc and 2 mm for thoracic cancer patients, and 0.1 g/cc and 2 mm for abdominal cancer patients, depending on clinically used setup margins. Conclusion: Gamma Index of the target volume should be used for CBCT gamma analysis. For different part of the body the optimal CBCT Gamma criteria varies. By using the optimal Gamma criteria, we expect the 90% and 85% Gamma passing rate which are the accepting and warning threshold respectively can be used to accurately guide clinical decisions

The goal of this study was to exam the efficacy of current DVH based clinical guidelines draw from photon experience for lung cancer radiation therapy on proton therapy. Comparison proton plans and IMRT plans were generated for 10 lung patients treated in our proton facility. A gEUD based plan evaluation method was developed for plan evaluation. This evaluation method used normal lung gEUD(a) curve in which the model parameter "a" was sampled from the literature reported value. For all patients, the proton plans delivered lower normal lung V_{5 Gy}with similar V_{20 Gy}and similar target coverage. Based on current clinical guidelines, proton plans were ranked superior to IMRT plans for all 10 patients. However, the proton and IMRT normal lung gEUD(a) curves crossed for 8 patients within the tested range of "a", which means there was a possibility that proton plan would be worse than IMRT plan for lung sparing. A concept of deficiency index (DI) was introduced to quantify the probability of proton plans doing worse than IMRT plans. By applying threshold on DI, four patients' proton plan was ranked inferior to the IMRT plan. Meanwhile if a threshold to the location of curve crossing was applied, 6 patients' proton plan was ranked inferior to the IMRT plan. The contradictory ranking results between the current clinical guidelines and the gEUD(a) curve analysis demonstrated there is potential pitfalls by applying photon experience directly to the proton world. A comprehensive plan evaluation based on radio-biological models should be carried out to decide if a lung patient would really be benefit from proton therapy.