Engineering topics

Gong, Y

Publications and source records attributed to Gong, Y.

3 records

DOE OSTI2015

SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) were randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-578: On Definition of Minimum and Maximum Dose for Target Volume

Purpose: This study aims to investigate the impact of different minimum and maximum dose definitions in radiotherapy treatment plan quality evaluation criteria by using tumor control probability (TCP) models. Methods: Dosimetric criteria used in RTOG 1308 protocol are used in the investigation. RTOG 1308 is a phase III randomized trial comparing overall survival after photon versus proton chemoradiotherapy for inoperable stage II-IIIB NSCLC. The prescription dose for planning target volume (PTV) is 70Gy. Maximum dose (Dmax) should not exceed 84Gy and minimum dose (Dmin) should not go below 59.5Gy in order for the plan to be “per protocol” (satisfactory).A mathematical model that simulates the characteristics of PTV dose volume histogram (DVH) curve with normalized volume is built. The Dmax and Dmin are noted as percentage volumes Dη% and D(100-δ)%, with η and d ranging from 0 to 3.5. The model includes three straight line sections and goes through four points: D95%= 70Gy, Dη%= 84Gy, D(100-δ)%= 59.5 Gy, and D100%= 0Gy. For each set of η and δ, the TCP value is calculated using the inhomogeneously irradiated tumor logistic model with D50= 74.5Gy and γ50=3.52. Results: TCP varies within 0.9% with η; and δ values between 0 and 1. With η and η varies between 0 and 2, TCP change was up to 2.4%. With η and δ variations from 0 to 3.5, maximum of 8.3% TCP difference is seen. Conclusion: When defined maximum and minimum volume varied more than 2%, significant TCP variations were seen. It is recommended less than 2% volume used in definition of Dmax or Dmin for target dosimetric evaluation criteria. This project was supported by NIH grants U10CA180868, U10CA180822, U24CA180803, U24CA12014 and PA CURE Grant.

61 RADIATION PROTECTION AND DOSIMETRY↗