Browsing by Author "Yoo, Sua"
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Item Open Access Accuracy and efficiency of image-guided radiation therapy (IGRT) for preoperative partial breast radiosurgery.(Journal of radiosurgery and SBRT, 2020-01) Yoo, Sua; O'Daniel, Jennifer; Blitzblau, Rachel; Yin, Fang-Fang; Horton, Janet KObjective
To analyze and evaluate accuracy and efficiency of IGRT process for preoperative partial breast radiosurgery.Methods
Patients were initially setup with skin marks and 5 steps were performed: (1) Initial orthogonal 2D kV images, (2) pre-treatment 3D CBCT images, (3) verification orthogonal 2D kV images, (4) treatment including mid-treatment 2D kV images (for the final 15 patients only), and (5) post-treatment orthogonal 2D kV or 3D CBCT images. Patient position was corrected at each step to align the biopsy clip and to verify surrounding soft tissue positioning.Results
The mean combined vector magnitude shifts and standard deviations at the 5 imaging steps were (1) 0.96 ± 0.69, (2) 0.33 ± 0.40, (3) 0.05 ± 0.12, (4) 0.15 ± 0.17, and (5) 0.27 ± 0.24 in cm. The mean total IGRT time was 40.2 ± 13.2 minutes. Each step was shortened by 2 to 5 minutes with improvements implemented. Overall, improvements in the IGRT process reduced the mean total IGRT time by approximately 20 minutes. Clip visibility was improved by implementing oblique orthogonal images.Conclusion
Multiple imaging steps confirmed accurate patient positioning. Appropriate planning and imaging strategies improved the effectiveness and efficiency of the IGRT process for preoperative partial breast radiosurgery.Item Open Access Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models.(Frontiers in Oncology, 2019-01) Sheng, Yang; Li, Taoran; Yoo, Sua; Yin, Fang-Fang; Blitzblau, Rachel; Horton, Janet K; Ge, Yaorong; Wu, Q JackiePurpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning. Methods and Materials: A total of 40 WBRT plans from a single institution were included in this study under IRB approval. Twenty WBRT plans, 10 with single energy (SE, 6MV) and 10 with mixed energy (ME, 6/15MV), were randomly selected as training dataset to develop the methodology for automatic planning. The rest 10 SE cases and 10 ME cases served as validation. The auto-planning process consists of three steps. First, an energy prediction model was developed to automate energy selection. This model establishes an anatomy-energy relationship based on principle component analysis (PCA) of the gray level histograms from training cases' digitally reconstructed radiographs (DRRs). Second, a random forest (RF) model generates an initial fluence map using the selected energies. Third, the balance of overall dose contribution throughout the breast tissue is realized by automatically selecting anchor points and applying centrality correction. The proposed method was tested on the validation dataset. Non-parametric equivalence test was performed for plan quality metrics using one-sided Wilcoxon Signed-Rank test. Results: For validation, the auto-planning system suggested same energy choices as clinical-plans in 19 out of 20 cases. The mean (standard deviation, SD) of percent target volume covered by 100% prescription dose was 82.5% (4.2%) for auto-plans, and 79.3% (4.8%) for clinical-plans (p > 0.999). Mean (SD) volume receiving 105% Rx were 95.2 cc (90.7 cc) for auto-plans and 83.9 cc (87.2 cc) for clinical-plans (p = 0.108). Optimization time for auto-plan was <20 s while clinical manual planning takes between 30 min and 4 h. Conclusions: We developed an automatic treatment planning system that generates WBRT plans with optimal energy selection, clinically comparable plan quality, and significant reduction in planning time, allowing for near-real-time planning.Item Open Access Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.(Advances in radiation oncology, 2021-03) Yoo, Sua; Sheng, Yang; Blitzblau, Rachel; McDuff, Susan; Champ, Colin; Morrison, Jay; O'Neill, Leigh; Catalano, Suzanne; Yin, Fang-Fang; Wu, Q JackiePurpose
The machine learning-based automated treatment planning (MLAP) tool has been developed and evaluated for breast radiation therapy planning at our institution. We implemented MLAP for patient treatment and assessed our clinical experience for its performance.Methods and materials
A total of 102 patients of breast or chest wall treatment plans were prospectively evaluated with institutional review board approval. A human planner executed MLAP to create an auto-plan via automation of fluence maps generation. If judged necessary, a planner further fine-tuned the fluence maps to reach a final plan. Planners recorded the time required for auto-planning and manual modification. Target (ie, breast or chest wall and nodes) coverage and dose homogeneity were compared between the auto-plan and final plan.Results
Cases without nodes (n = 71) showed negligible (<1%) differences for target coverage and dose homogeneity between the auto-plan and final plan. Cases with nodes (n = 31) also showed negligible difference for target coverage. However, mean ± standard deviation of volume receiving 105% of the prescribed dose and maximum dose were reduced from 43.0% ± 26.3% to 39.4% ± 23.7% and 119.7% ± 9.5% to 114.4% ± 8.8% from auto-plan to final plan, respectively, all with P ≤ .01 for cases with nodes (n = 31). Mean ± standard deviation time spent for auto-plans and additional fluence modification for final plans were 12.1 ± 9.3 and 13.1 ± 12.9 minutes, respectively, for cases without nodes, and 16.4 ± 9.7 and 26.4 ± 16.4 minutes, respectively, for cases with nodes.Conclusions
The MLAP tool has been successfully implemented for routine clinical practice and has significantly improved planning efficiency. Clinical experience indicates that auto-plans are sufficient for target coverage, but improvement is warranted to reduce high dose volume for cases with nodal irradiation. This study demonstrates the clinical implementation of auto-planning for patient treatment and the significant importance of integrating human experience and feedback to improve MLAP for better clinical translation.Item Open Access Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection.(2010) Webb, Lincoln JonPURPOSE: Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection. METHODS: The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis. RESULTS: The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148. CONCLUSIONS: The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.Item Open Access Goal-Driven Beam Setting Optimization for Whole-Breast Radiation Therapy.(Technology in cancer research & treatment, 2019-01) Wang, Wentao; Sheng, Yang; Yoo, Sua; Blitzblau, Rachel C; Yin, Fang-Fang; Wu, Q JackiePURPOSE:To develop an automated optimization program to generate optimal beam settings for whole-breast radiation therapy driven by clinically oriented goals. MATERIALS AND METHODS:Forty patients were retrospectively included in this study. Each patient's planning images, contoured structures of planning target volumes, organs-at-risk, and breast wires were used to optimize for patient-specific-beam settings. Two beam geometries were available tangential beams only and tangential plus supraclavicular beams. Beam parameters included isocenter position, gantry, collimator, couch angles, and multileaf collimator shape. A geometry-based goal function was defined to determine such beam parameters to minimize out-of-field target volume and in-field ipsilateral lung volume. For each geometry, the weighting in the goal function was trained with 10 plans and tested on 10 additional plans. For each query patient, the optimal beam setting was searched for different gantry-isocenter pairs. Optimal fluence maps were generated by an in-house automatic fluence optimization program for target coverage and homogeneous dose distribution, and dose calculation was performed in Eclipse. Automatically generated plans were compared with manually generated plans for target coverage and lung and heart sparing. RESULTS:The program successfully produced a set of beam parameters for every patient. Beam optimization time ranged from 10 to 120 s. The automatic plans had overall comparable plan quality to manually generated plans. For all testing cases, the mean target V95% was 91.0% for the automatic plans and 88.5% for manually generated plans. The mean ipsilateral lung V20Gy was lower for the automatic plans (15.2% vs 17.9%). The heart mean dose, maximum dose of the body, and conformity index were all comparable. CONCLUSION:We developed an automated goal-driven beam setting optimization program for whole-breast radiation therapy. It provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy on a substantially faster time frame.Item Open Access Investigation of sliced body volume (SBV) as respiratory surrogate.(Journal of applied clinical medical physics, 2013-01-07) Cai, Jing; Chang, Zheng; O'Daniel, Jennifer; Yoo, Sua; Ge, Hong; Kelsey, Christopher; Yin, Fang-FangThe purpose of this study was to evaluate the sliced body volume (SBV) as a respiratory surrogate by comparing with the real-time position management (RPM) in phantom and patient cases. Using the SBV surrogate, breathing signals were extracted from unsorted 4D CT images of a motion phantom and 31 cancer patients (17 lung cancers, 14 abdominal cancers) and were compared to those clinically acquired using the RPM system. Correlation coefficient (R), phase difference (D), and absolute phase difference (D(A)) between the SBV-derived breathing signal and the RPM signal were calculated. 4D CT reconstructed based on the SBV surrogate (4D CT(SBV)) were compared to those clinically generated based on RPM (4D CT(RPM)). Image quality of the 4D CT were scored (S(SBV) and S(RPM), respectively) from 1 to 5 (1 is the best) by experienced evaluators. The comparisons were performed for all patients, and for the lung cancer patients and the abdominal cancer patients separately. RPM box position (P), breathing period (T), amplitude (A), period variability (V(T)), amplitude variability (V(A)), and space-dependent phase shift (F) were determined and correlated to S(SBV). The phantom study showed excellent match between the SBV-derived breathing signal and the RPM signal (R = 0.99, D= -3.0%, D(A) = 4.5%). In the patient study, the mean (± standard deviation (SD)) R, D, D(A), T, V(T), A, V(A), and F were 0.92 (± 0.05), -3.3% (± 7.5%), 11.4% (± 4.6%), 3.6 (± 0.8) s, 0.19 (± 0.10), 6.6 (± 2.8) mm, 0.20 (± 0.08), and 0.40 (± 0.18) s, respectively. Significant differences in R and D(A) (p = 0.04 and 0.001, respectively) were found between the lung cancer patients and the abdominal cancer patients. 4D CT(RPM) slightly outperformed 4D CT(SBV): the mean (± SD) S(RPM) and S(SBV) were 2.6 (± 0.6) and 2.9 (± 0.8), respectively, for all patients, 2.5 (± 0.6) and 3.1 (± 0.8), respectively, for the lung cancer patients, and 2.6 (± 0.7) and 2.8 (± 0.9), respectively, for the abdominal cancer patients. The difference between S(RPM) and S(SBV) was insignificant for the abdominal patients (p = 0.59). F correlated moderately with S(SBV) (r = 0.72). The correlation between SBV-derived breathing signal and RPM signal varied between patients and was significantly better in the abdomen than in the thorax. Space-dependent phase shift is a limiting factor of the accuracy of the SBV surrogate.