Faculty Bibliography

BACKGROUND:Since the inception of spinal radiosurgery decades ago, the factors influencing spinal cord recovery have remained unclear. Elucidating these factors would aid in reirradiation planning and delivery. We describe modeling reirradiation normal tissue complication probabilities (NTCP) from compiled published data, akin to the collaborative Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) and High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic (HyTEC) projects. PURPOSE/OBJECTIVE:Simple dose-time recovery factors for the spinal cord serve as a basis to propose the Reirradiation Treatment Effects in the Clinic (ReTEC) project. ReTEC aims to define human organ recovery factors to convert the total composite dose to an equivalent physical dose compatible with the de novo therapy dose tolerance limits. Additionally, we present a system for automating analyses as well as clinical implementation of radiation dose-time recovery. METHODS:Published literature was queried for stereotactic voxel-wise composite reirradiation plans involving the spinal cord, but none were found. Hence, minimum reporting criteria were used instead: spinal cord maximum dose (Dmax) of each course, time interval between courses, and number of fractions or biologically effective dose, per patient or in sufficiently homogeneous groups of patients. Recovery factors and logistic model parameters were fitted with maximum likelihood techniques. RESULTS:An NTCP model was derived from 13 papers with data from 282 lesions, including six myelopathy cases. The model suggested spinal cord recovery of more than 50% in less than 1 year, which is faster and more complete than current clinical practice, but with wide confidence intervals (CI) (bootstrap 95% CI: 31%-95% when the time interval was 1 year). CONCLUSION/CONCLUSIONS:Due to limited data, we recommend remaining within current standard guidelines until more comprehensive analyses, optimally under the ReTEC initiative, refute or validate these estimates. ReTEC would require composite plans accounting for differing Dmax locations in each course. Nevertheless, this proof-of-concept Dmax model provides insight and serves as a basis to propose ReTEC.

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: Modern linear accelerators (LINACs) are equipped with kV-imaging systems for 2D-images and 3D-cone-beam CT (CBCT). The delivered single kV-CBCT-dose is small compared to the treatment dose but can add up to a cumulative dose of 1-3% for multi-fraction courses according to TG180. Thus, TG142 recommends an annual assessment of the imaging dose. The purpose of this project was to review the current practice, compare approaches suggested in recent literature and standardize annual kV-CBCT dose measurements.
Method(s): Current institutional practice is to measure the CBCT dose with the 10cmlong RaySafe X2 pencil-ion-chamber inserted in the center position of a CIRS lung phantom. We introduced the PMMA-CTDI phantom in a satellite-location for all measurements. Dose was measured at 3/6/9/12 o'clock and center positions of a 32cm-wide CTDI phantom to determine max dose heterogeneity for a half-trajectory imaging protocol and at the 12 o'clock and center position of either the head or abdomen phantom for 11 protocols ranging from 80kVp/100mAs to 140kVp/1688mAs using the Raysafe X2, the Standard Imaging A101 pencil-ion-chamber, and the A12 Farmer-ion-chamber. Measurements were repeated with decreased kV-tube-collimator opening as suggested by Varian.
Result(s): The dose discrepancy due to chamber position was largest between the 6 and 3 o'clock positions (10%) and the average was close to the peripheral measurement at 12 o'clock. All three detectors showed a linear dependency. Reducing CBCT beam collimation increased measurement complexity with only minimally improved agreement between measured and displayed CTDIw values. The standard deviation for dose measurements at different machines reduced from 22%/12% for the lung phantom to 7%/3% (max/mean) for the CTDI phantom.
Conclusion(s): Dose measurements at 12 o'clock and center position of CTDI-phantom have been defined as our new standard using un-modified treatment imaging protocols; conversion factors for center-only or lung-phantom measurements have been provided

Purpose: This study investigated the accuracy of two dose calculation algorithms (AAA version 15.6.05 and AcurosXB (AXB) version 15.6.05) in lung, fat, and bone regions of a lung phantom using 2D Gamma analysis.
Method(s): A commercial thorax phantom (CIRS) was marked and CT simulated with 1.5 mm slice thickness then planned in Eclipse v15.6 (Varian) using an oblique 3x3 cm field traversing soft tissue, lung, and spine regions. Dose was calculated using AAA and AXB algorithms at 1.0 mm resolution for four different beam energies with the same number of monitor units: 6X, 6X-FFF, 10X and 10X-FFF. Each field was separately delivered to the phantom with Gafchromic EBT3 film placed in the axial plane through the beam isocenter. Film alignment was performed using built-in phantom pins as registration marks. Gamma analysis was performed in SNC Patient 8.2 (Sun Nuclear Corp.) software using distanceto- agreement (DTA) to compare AAA and AXB calculated doses against one another and against measured dose.
Result(s): Considerable dosimetric differences occurred between AAA and AXB especially at tissue interfaces and in the beam penumbra within the lung. About 10% and 30% of analyzed points had dose differences larger than 3% for 6X and 10X, respectively. The differences were a few percentages smaller for non-flattened beams. In comparison of calculation with measurement, the 3%/2mm-DTA passing rate for AAA was 3.7% higher than AXB for the 6X beam (3.9% for 6X-FFF), but -15.9% lower for 10X (- 24.9% for 10X-FFF).
Conclusion(s): Dose calculation algorithm accuracy was assessed in a heterogeneous thorax phantom using 2D gamma analysis and Gafchromic film. Large differences in the passing rate, especially at 10X(FFF), suggest to verify that clinical plan evaluation metrics such as coverage and dose constrains are not compromised by the choice of dose calculation algorithm