Search Results - "Fave, X."

Using Pretreatment Radiomics and Delta-Radiomics Features to Predict Non–Small Cell Lung Cancer Patient Outcomes by Fave, X, Zhang, L, Yang, J, Mackin, D, Balter, P, Gomez, D.R, Followill, D, Jones, A.K, Stingo, F, Mohan, R, Liao, Z, Court, L.E

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Prognostic Value and Potential Use of Quantitative Imaging Features From Pretreatment FDG-PET in Stage III NSCLC by Fried, D, Mawlawi, O.R, Zhang, L, Fave, X, Zhou, S, Tucker, S.L, Ibbott, G.S, Liao, Z, Court, L.E

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SU‐E‐J‐168: Investigating the Feasibility of Vertical CBCT Imaging Using the Varian TrueBeam LINAC by Fave, X, Balter, P, Martin, R, Ahmad, M, Pan, T, Court, L

“…Purpose: To assess the TrueBeam LINACs imaging capabilities for taking vertical CBCT in order to provide an alternative treatment position for radiation…”
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SU‐E‐T‐359: Patients Could (and Should) Be Treated in An Upright Position by Court, L, Yang, J, Fullen, D, Han, N, Ko, J, Mason, S, Nguyen, K, Stein, S, Fave, X, Hsieh, M, Kuruvila, S, Hillebrandt, E, Palmer, J, Beadle, B, Dabaja, B, Skinner, H, Ibbott, G, Balter, P

“…Purpose: Treating patients in an upright (seated) position has several potential advantages including increased lung volume/reduced respiratory motion (lung…”
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SU-F-R-09: Homogenization of CT Images for Radiomics Studies: It’s Like Butter(worth) by Mackin, D, Court, L, Ng, C, Yang, J, Zhang, L, Fave, X

“…Purpose: Previous studies have shown that differences in the CT image acquisition parameters produce variability in extracted quantitative image features. The…”
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MO-DE-207B-10: Impact of Morphologic Characteristics On Radiomics Features From Contast-Enhanced CT for Primary Lung Tumors by Fried, D, Zhang, L, Fave, X, Ibbott, G, Zhou, S, Mawlawi, O, Liao, Z, Court, L

“…Purpose: Determine the impact of morphologic characteristics (e.g. necrosis, vascular enhancement, and cavitation) on radiomic features from contrast enhanced…”
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SU‐E‐J‐261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features by Zhang, L, Fried, D, Fave, X, Mackin, D, Yang, J, Court, L

“…Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the…”
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PO-0991: Serial tumor radiomic features predict response of head and neck cancer treated with Radiotherapy by Elhalawani, H.E., Mohamed, A.S.R., Volpe, S., Yang, P., Campbell, S., Granberry, R., Ger, R., Fave, X., Zhang, L., Marai, G.E., Vock, D., Canahuate, G.M., Mackin, D., Court, L., Gunn, G.B., Rao, A., Fuller, C.D.

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SU‐D‐9A‐05: IBEX: An Open Software Infrastructure Platform to Accelerate Collaborative Work On Quantitative Image Features and Predictive Models by Zhang, L, Hunter, L, Fried, D, Fave, X, Yang, J, Court, L

“…Purpose: Quantitative imaging analysis has great potential to improve the accuracy and efficiency of cancer research, but the lack of software infrastructure…”
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TU‐A‐12A‐03: Monitoring Changes in Tumor Texture Features On Weekly CT and CBCT Scans of NSCLC Patients by Fave, X, Zhang, L, Yang, J, Fried, D, Balter, P, Court, L

“…Purpose: To (1) track changes in CT texture features through time and (2) identify correlations between CT and CBCT texture feature values for NSCLC patients…”
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EP-2121: Serial Parotid Gland Radiomic-based Model Predicts Post-Radiation Xerostomia in Oropharyngeal Cancer by Elhalawani, H., Mohamed, A.S.R., Kanwar, A., Dursteler, A., Rock, C.D., Eraj, S.E., Meheissen, M., Volpe, S., Yang, P., Granberry, R., Ger, R., Fave, X., Zhang, L., Yang, J., Marai, G.E., Vock, D., Canahuate, G., Mackin, D., Court, L., Gunn, G.B., Rao, A., Fuller, C.D.

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SU‐D‐BRA‐05: Toward Understanding the Robustness of Radiomics Features in CT by Mackin, D, Zhang, L, Fave, X, Fried, D, Yang, J, Taylor, B, Rodriguez‐Rivera, E, Dodge, C, Jones, A, Court, L

“…Purpose: To gauge the impact of inter‐scanner variability on radiomics features in computed tomography (CT). Methods: We compared the radiomics features…”
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TU-D-207B-02: Delta-Radiomics: The Prognostic Value of Therapy-Induced Changes in Radiomics Features for Stage III Non-Small Cell Lung Cancer Patients by Fave, X, Zhang, L, Yang, J, Mackin, D, Stingo, F, Followill, D, Balter, P, Jones, A, Gomez, D, Court, L

“…Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models…”
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TU-D-207B-01: A Prediction Model for Distinguishing Radiation Necrosis From Tumor Progression After Gamma Knife Radiosurgery Based On Radiomics Features From MR Images by Zhang, Z, Ho, A, Wang, X, Brown, P, Guha-Thakurta, N, Ferguson, S, Fave, X, Zhang, L, Mackin, D, Court, L, Li, J, Yang, J

“…Purpose: To develop and validate a prediction model using radiomics features extracted from MR images to distinguish radiation necrosis from tumor progression…”
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SU‐E‐J‐186: Acquiring and Assessing Upright CBCT Images for Future Treatment Planning by Fave, X, Yang, J, Balter, P, Court, L

“…Purpose: To acquire upright CBCT images using the onboard imager of a Varian TrueBeam. An easy to implement upright imaging protocol could allow for widespread…”
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SU‐D‐BRA‐07: A Phantom Study to Assess the Variability in Radiomics Features Extracted From Cone‐Beam CT Images by Fave, X, Fried, D, Zhang, L, Yang, J, Balter, P, Followill, D, Gomez, D, Jones, A, Stingo, F, Court, L

“…Purpose: Several studies have demonstrated the prognostic potential for texture features extracted from CT images of non‐small cell lung cancer (NSCLC)…”
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SU‐E‐J‐242: Volume‐Dependence of Quantitative Imaging Features From CT and CE‐CT Images of NSCLC by Fave, X, Fried, D, Zhang, L, Yang, J, Balter, P, Followill, D, Gomez, D, Jones, A, Stingo, F, Court, L

“…Purpose: To determine whether tumor volume plays a significant role in the values obtained for texture features when they are extracted from computed…”
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