Search

Objective The objective is to explore the possibility of optimal/rational application of setup margin during treatment planning for frameless stereotactic Gamma Knife radiosurgery/therapy. Methods Uncertainty measurements for frameless Gamma Knife Icon treatment were used to calculate the necessary setup margin via four different published recipes and these margins were subsequently applied to treatment plans of 30 previously treated patients and replans were generated meeting comparable plan quality metrics. All plans were then analyzed based on the ability to maintain normal tissue dose tolerances and the relative increase in target dose coverage probability using a pass/fail scoring system based on published normal tissue dose constraints and an in-house developed optimal scoring method. Results Gross tumor volume/planning target volume (GTV/PTV) size strongly correlated with both meeting normal tissue tolerances and optimal scores for single fraction plans corroborating published clinical outcomes. The Van Herk Margin Formula (VHMF) and Parker margin formulae were indicated as good candidates for high probabilities of both meeting normal tissue goals and high optimal scores which generally translated to just over 1 mm in GTV to PTV margin. Conclusion For single fraction treatment, GTV size is highly significant in predicting failure to meet normal tissue goals whereas whether setup margin was used was not a significant predictor. Setup margin can rationally be applied when fraction number is dictated by clinically indicated metrics regarding GTV size of greater or less than 4 cc. 1 mm is a reasonable practical application of margin added to GTV to ensure physical prescription dose target coverage for most cases when clinically desired based on disease type and intended outcome.

Objective The objective of the study is to discern any factors that may be predictive of patient-specific uncertainty related to residual error after cone-beam CT (CBCT) correction and motion measured by the high-definition motion management (HDMM) system. Methods HDMM treatment logs were parsed via a Python 3 script and then analyzed for 30 patients. Additionally, CBCT registration and correction data was also collected and analyzed for the same 30 patients. Correlation analysis was then performed against various patient- and treatment-related factors to discern any potentially predictive factors. Results BMI was the only statistically significant predictor identified in this study with an r value of 0.393, p=0.032. Despite being identified as a predictor in other studies, treatment time, when treated as a continuous variable, did not show up as significant in this work. Conclusion BMI may be predictive of patients who might require extra tactics to mitigate motion during frameless Gamma Knife® treatment.

Introduction Stereotactic body radiation therapy (SBRT) is an effective treatment for early-stage non-small cell lung cancer (NSCLC) patients who are either medically inoperable or who decline surgery. SBRT improves tumor control and overall survival (OS) in medically inoperable, early-stage, NSCLC patients. In this study, we investigated the effectiveness of two different SBRT doses commonly used and present our institutional experience. Purpose To determine the clinical outcomes between two treatment regiments (50 Gray [Gy] vs. 55 Gy in five fractions) among Stage I NSCLC patients treated with SBRT at a state academic medical center. Methods We performed a retrospective analysis of 114 patients with Stage I (T1-2 N0 M0) NSCLC treated at a state academic medical center between October 2009 and April 2019. Survival analyses with treatment regimens of 50 Gy and 55 Gy in five fractions were conducted to detect any improvement in outcomes associated with the higher dose. The primary endpoints of this study included OS, local control (LC), and disease-free survival (DFS). Log-rank test and the Kaplan-Meier method were used to analyze the survival curves of the two treatment doses. The SPSS v.24.0 (IBM Corp., Armonk, NY, USA) was used for statistical analyses. Results The 114 early-stage NSCLC patients (median age, 68 years; range 12 to 87 years) had a median follow-up of 25 months (range two to 86 months). The number of males (n = 72; 63.2 %) exceeded the number of females (n = 42; 36.8 %). The majority of patients in this study were Caucasians (n = 68; 59.6 %) and 46 patients were African Americans (40.4 %). Two-thirds of the patients (n = 76; 66.7 %) were treated with 50 Gy in five fractions, and 38 patients (33.3 %) with 55 Gy in five fractions. The one-, two-, and three-year OS and DFS rates were improved in the patients treated with 55 Gy [OS, 81.7 % vs. 72.8 %; 81.7 % vs. 58.9 %; 81.7 % vs. 46.7 % (p = 0.049)], [DFS, 69.7 % vs. 69.7 %; 61.9 % vs. 55.7 %; 61.9 % vs. 52.0 % (p = 0.842)], compared to those treated with 50 Gy. Adenocarcinoma was the most common histology in both groups (51.3 % and 68.4 %). Failure rates were elevated for the 50 Gy regimen [39 (34.2 %) vs. 12 (8.5 %)]. Three year control rates were (66.3 % vs. 96.6 %; p = 0.002) local control; (63.3 % vs. 94.4 %; p = 0.000) regional control; and (65.7 % vs. 97.1 %; p = 0.000) distant control, compared to those treated with 55 Gy. Conclusion Early-stage NSCLC patients treated with SBRT 55 Gy in five fractions did better in terms of local control, overall survival, and disease-free survival rates compared to the 50 Gy in five fractions group.

Object The purpose of this study was to compare two methods of stereotactic localization in Gamma Knife treatment planning: cone beam computed tomography (CBCT) or fiducial. While the fiducial method is the traditional method of localization, CBCT is now available for use with the Gamma Knife Icon. This study seeks to determine whether a difference exists between the two methods and then whether one is better than the other regarding accuracy and workflow optimization. Methods Cone beam computed tomography was used to define stereotactic space around the Elekta Film Pinprick phantom and then treated with film in place. The same phantom was offset known amounts from center and then imaged with CBCT and registered with the reference CBCT image to determine if measured offsets matched those known. Ten frameless and 10 frame-based magnetic resonance imaging (MRI) to CBCT patient fusions were retrospectively evaluated using the TG-132 TRE method. The stereotactic coordinates defined by CBCT and traditional fiducials were compared on the Elekta 8 cm Ball phantom, an anthropomorphic phantom, and actual patient data. Offsets were introduced to the anthropomorphic phantom in the stereotactic frame and CBCT's ability to detect those offsets was determined. Results Cone beam computed tomography defines stereotactic space well within the established limits of the mechanical alignment system. The CBCT to CBCT registration can detect offsets accurately to within 0.1 mm and 0.5°. In all cases, some disagreement existed between fiducial localization and that of CBCT which in some cases was small, but also was as high as 0.43 mm in the phantom domain and as much as 1.54 mm in actual patients. Conclusion Cone beam computed tomography demonstrates consistent accuracy in defining stereotactic space. Since both localization methods do not agree with each other consistently, the more reliable method must be identified. Cone beam computed tomography can accurately determine offsets occurring within stereotactic space that would be nondiscernible utilizing the fiducial method and seems to be more reliable. Using CBCT localization offers the opportunity to streamline workflow both from a patient and clinic perspective and also shows patient position immediately prior to treatment.

Cervical cancer remains a major health challenge in the United States (US), especially among the low socioeconomic and African American populations. The demographics of Mississippi constitute a relatively high percentage of this high-risk population. External beam radiation therapy (EBRT) combined with concurrent chemotherapy and followed by brachytherapy is the gold standard of treatment for stage IB3 through IVA cervical cancer. Arguably, brachytherapy is the most important component of this treatment process. Patterns of Care studies (PCS) and other more recent studies have shown that brachytherapy cannot be omitted or replaced by conventional or image-guided EBRT. The last decade has witnessed the expanding use of image-guided brachytherapy (IGBT). Studies have established the superiority of IGBT over point-based brachytherapy. MRI is associated with superior soft tissue definition compared with CT and is emerging as the new standard of care. The Gynaecological Groupe Europeen de Curietherapie and the European Society for Radiotherapy and Oncology [(GYN) GEC-ESTRO] have recommended that the dose be prescribed to the high-risk clinical target volume (HR-CTV). This volume includes residual tumor present at the time of brachytherapy, the cervix, and any gray areas seen on the scan. The (GYN) GEC-ESTRO has shown that a dose of >8500 cGy delivered in

OBJECT: To compare the consistency of the agreement between the Convolution and TMR10 algorithms using a homogeneous phantom and to identify target characteristics that lead to large changes in target isodose coverage when the Convolution algorithm is used in GammaPlan as opposed to the TMR10 algorithm. METHODS: The IROC phantom end-to-end test was performed and RTDose for both the TMR10 and Convolution algorithm were submitted for comparison to the measurement. Treatment plans for 16 patients and 26 different targets were retrospectively re-calculated with the Convolution algorithm when originally planned with the TMR10 algorithm. Multivariate regression was used to find statistically significant predictors of loss in target prescription isodose coverage. RESULTS: Both algorithms agreed well with the IROC TLD measurement (within 1 %) and slightly better agreement was seen in the film analysis for the Convolution algorithm. After multivariate regression, small target volumes, < 1cm from air cavity, and minimum dose to target were potential predictors of large percentage loss of prescription isodose coverage (p = 0.049, 0.026, and 0.002, respectively). CONCLUSION: Convolution and TMR10 appear to be equivalent in homogeneous situations. Some target characteristics have been identified that might be indications for use of the Convolution algorithm in clinical practice.

Background Peer review systems within radiation oncology are important to ensure quality radiation care. Several individualized methods for radiation oncology peer review have been described. However, despite the importance of peer review in radiation oncology barriers may exist to its effective implementation in practice. The purpose of this study was to quantify the rate of plan changes based on our group peer review process as well as the quantify amount of time and resources needed for this process. Methods Data on cases presented in our institutional group consensus peer review conference were prospectively collected. Cases were then retrospectively analyzed to determine the rate of major change (plan rejection) and any change in plans after presentation as well as the median time of presentation. Univariable logistic regression was used to determine factors associated with major change and any change. Results There were 73 cases reviewed over a period of 11 weeks. The rate of major change was 8.2% and the rate of any change was 23.3%. The majority of plans (53.4%) were presented in 6–10 min. Overall, the mean time of presentation was 8 min. On univariable logistic regression, volumetric modulated arc therapy plans were less likely to undergo a plan change but otherwise there were no factors significantly associated with major plan change or any type of change. Conclusion Group consensus peer review allows for a large amount of informative clinical and technical data to be presented per case prior to the initiation of radiation treatment in a thorough yet efficient manner to ensure plan quality and patient safety.

Purpose/Objective(s) The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify the occurrence of ECE as it changes staging and management for the patients. Current clinical ECE detection relying on radiologists' visual identification is extremely labor-intensive, time-consuming, and error-prone, and consequently, pathologic confirmation is required. Therefore, we aim to perform ECE identification automatically using a deep learning-based technique to analyze the presence or absence of ECE and correlate that with gold standard histopathological findings. Materials/Methods This research proposes a novel deep learning method to detect ECE from 3D computed tomography (CT) scans. The proposed network has multi-scale input that captures both local and global information. The two types of inputs are fitted into two pathways with deep convolutional neural networks (DCNNs), 3D CNN baseline and 3D DenseNet. A sliding-cube approach is applied to extract small paired 3D patches from patient data with different scales at data preparation. The network will classify the patient based on patch-level classification results. Different training scenarios are designed for the experimental test. Results Based on five-fold cross-validation, the experimental results have demonstrated that our model can identify ECE and non-ECE patients, specifically in the patch-level. We have achieved the ECE detection with 96.92% accuracy 98.84% AUC. Detailed results are shown in the Table 1. Conclusion The study demonstrates the ability to use a deep learning-based method for ECE direct detection. The proposed local-global network can help capture sufficient features and achieve high classification accuracy. The outcome of this research is expected to promote the implementation of artificial intelligence for ECE identification for head and neck cancer diagnosis in the radiology computer vision field.

Purpose: The Joint Commission has encouraged the healthcare industry to become “High Reliability Organizations” by “Chasing Zero Harm” in patient care. In radiation oncology, the time point of quality checks determines whether errors are prevented or only mitigated. Thus, to “chase zero” in radiation oncology, peer review has to be implemented prior to treatment initiation. A multidisciplinary group consensus peer review (GCPR) model is used pre-treatment at our institution and has been successful in our efforts to “chase zero harm” in patient care. Methods: With the GCPR model, policy-defined complex cases go through a treatment planning conference, which includes physicians, residents, physicists, and dosimetrists. Three major plan aspects are reviewed: target volumes, target and normal tissue dose coverage, and dose distributions. During the review, any team member can ask questions and afterwards a group consensus is taken regarding plan approval. Results: The GCPR model has been implemented through a commitment to peer review and creative conference scheduling. Automated analysis software is used to depict color-coded results for department approved target coverage and dose constraints. About 8% of plans required re-planning while about 23% required minor changes. The mean time for review of each plan was 8 min. Conclusions: Catching errors prior to treatment is the only way to “chase zero” in radiation oncology. Various types of errors may exist in treatment plans and our GCPR model succeeds in preventing many errors of all shapes and sizes in target definition, dose prescriptions, and treatment plans from ever reaching the patients.

Object The purpose of this study was to compare two methods of stereotactic localization in Gamma Knife treatment planning: cone beam computed tomography (CBCT) or fiducial. While the fiducial method is the traditional method of localization, CBCT is now available for use with the Gamma Knife Icon. This study seeks to determine whether a difference exists between the two methods and then whether one is better than the other regarding accuracy and workflow optimization. Methods Cone beam computed tomography was used to define stereotactic space around the Elekta Film Pinprick phantom and then treated with film in place. The same phantom was offset known amounts from center and then imaged with CBCT and registered with the reference CBCT image to determine if measured offsets matched those known. Ten frameless and 10 frame‐based magnetic resonance imaging (MRI) to CBCT patient fusions were retrospectively evaluated using the TG‐132 TRE method. The stereotactic coordinates defined by CBCT and traditional fiducials were compared on the Elekta 8 cm Ball phantom, an anthropomorphic phantom, and actual patient data. Offsets were introduced to the anthropomorphic phantom in the stereotactic frame and CBCT's ability to detect those offsets was determined. Results Cone beam computed tomography defines stereotactic space well within the established limits of the mechanical alignment system. The CBCT to CBCT registration can detect offsets accurately to within 0.1 mm and 0.5°. In all cases, some disagreement existed between fiducial localization and that of CBCT which in some cases was small, but also was as high as 0.43 mm in the phantom domain and as much as 1.54 mm in actual patients. Conclusion Cone beam computed tomography demonstrates consistent accuracy in defining stereotactic space. Since both localization methods do not agree with each other consistently, the more reliable method must be identified. Cone beam computed tomography can accurately determine offsets occurring within stereotactic space that would be nondiscernible utilizing the fiducial method and seems to be more reliable. Using CBCT localization offers the opportunity to streamline workflow both from a patient and clinic perspective and also shows patient position immediately prior to treatment.

Extracapsular extension (ECE) is a decisive indication for treatment planning of patients with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify whether ECE occurs in HNSCC patient treatment. Current ECE detection practice mainly relies on the visual identification of doctors and professionals, which can be extremely labor intensive and time-consuming. Therefore, we aim to automatically perform ECE detection using a machine learning technique to classify whether ECE appears or not. In this research, we propose a systematic machine learning approach to detect ECE from 3D computed tomography (CT) scans. The process includes four steps: 1) apply sliding-cube to extract small 3D samples from patient data; 2) various 3D features are extracted for each sample; and 3) three machine learning models, gradient boosting (GB), random forest (RF), and support vector machine (SVM), with feature extraction and selection approaches, are employed for sample classification task; 4) classify the patient based on sample classification result. Different training scenarios are designed for the experimental test. Based on five-fold cross-validation, the experimental results have demonstrated that our model is able to classify ECE and non-ECE patients. To check the explainability of the models, feature analysis is also conducted. The outcome of this research is expected to promote the implementation of artificial intelligence for ECE classification and detection as well as head and neck cancer diagnosis in the radiology computer vision field. Citation Format: Yibin Wang, William N. Duggar, Toms V. Thomas, Paul Roberts, Linkan Bian, Haifeng Wang. Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-039.