Health and Medical Physics Commons^™

Hyalinization-Based Pathologic Response And Immune Infiltration Following Neoadjuvant Radiotherapy With Or Without Immune-Checkpoint Blockade In Localized Undifferentiated Pleomorphic Sarcoma, R S Traweek, M Zoghbi, R Lazcano, B M Cope, A J Bishop, A Farooqi, D Mitra, A K Yoder, B A Guadagnolo, D R Ingram, K Wani, D Shamsutdinova, A J Lazar, W-L Wang, C P Scally, E Z Keung, K E Torres, K K Hunt, R Ratan, J A Livingston, M S Nakazawa, D M Araujo, S Patel, V Ravi, A P Conley, M A Zarzour, N Somaiah, C L Roland, E F Nassif Haddad

Faculty, Staff and Student Publications

BACKGROUND: Standard-of-care treatment of localized undifferentiated pleomorphic sarcoma (UPS) involves preoperative radiotherapy (RT) and surgery. Combination of RT with immune-checkpoint blockade (ICB) represents a promising new strategy.

METHODS: Our primary objective was to assess survival outcomes and pathologically documented response in patients receiving RT with or without ICB or chemotherapy.

RESULTS: In a retrospective cohort of 68 patients with primary or locally recurrent UPS who received neoadjuvant RT with or without ICB, the ICB group had greater tumor hyalinization than the non-ICB group (89% compared with 30%, P = 0.018). In the non-ICB group, 19/37 patients (51%) achieved hyalinization-based pathologic …

Dose Prediction Via Deep Learning To Enhance Treatment Planning Of Lung Radiotherapy Including Simultaneous Integrated Boost Techniques, Wenhua Cao, Mary Gronberg, Stephen Bilton, Hana Baroudi, Skylar Gay, Christopher Peeler, Zhongxing Liao, Thomas J Whitaker, Karen Hoffman, Laurence E Court

Faculty, Staff and Student Publications

Background: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated.

Purpose: To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer.

Methods: The dose prediction model was trained with 93 retrospective …

T2-Weighted Imaging Of Rectal Cancer Using A 3d Fast Spin Echo Sequence With And Without Deep Learning Reconstruction: A Reader Study, Dan Nguyen, Sarah Palmquist, Ken-Pin Hwang, Jingfei Ma, Usama Salem, Jia Sun, Xinzeng Wang, Jong Bum Son, Randy Ernst, Peng Wei, Harmeet Kaur, Nir Stanietzky

Faculty, Staff and Student Publications

Purpose: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

Methods: The study included 50 patients with rectal cancer who underwent rectal MRI consecutively between July 7, 2020 and January 20, 2021 using a T2W 3D FSE sequence with DLR and conventional reconstruction. Three radiologists reviewed the two sets of images, scoring overall SNR, motion artifacts, and overall image quality on a 3-point scale and indicating clinical preference for DLR or conventional reconstruction based on those three …

Robust Automated Method Of Spatial Resolution Measurement In Radiotherapy Ct Simulation Images, Pavel Govyadinov, Rick R Layman, Tucker Netherton, Raymond Mumme, Aaron K Jones, Laurence E Court, Moiz Ahmad

Faculty, Staff and Student Publications

Background: Variation in imaging protocol, patient positioning, and the presence of artifacts can vary image quality in CT images used for radiotherapy planning. Automated methods for spatial resolution (SR) estimation exist but require further investigation and validation for wider adoption.

Purpose: To validated previously existing algorithm for SR estimation and introduce improvements that make it robust to patient positioning, CT protocol, site, and artifacts.

Method: A reference algorithm based on the previous gold standard was recreated and modified to improve robustness. The algorithms were tested on three different datasets: (1) a cylindrical ACR CT QC phantom scanned using a Siemens …

Evaluating Automatically Generated Normal Tissue Contours For Safe Use In Head And Neck And Cervical Cancer Treatment Planning, Raphael Douglas, Adenike Olanrewaju, Raymond Mumme, Lifei Zhang, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan …

Automatic End-To-End Vmat Treatment Planning For Rectal Cancers, Kai Huang, Christine Chung, Ethan B Ludmir, Lifei Zhang, Constance A Owens, Jean Gumma-De La Vega, Jack Duryea, Yao Zhao, Xinru Chen, David Fuentes, Carlos E Cardenas, Tina Marie Briere, Sam Beddar, Laurence E Court, Prajnan Das

Faculty, Staff and Student Publications

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear.

PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs.

METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the …

Resection Cavity Auto-Contouring For Patients With Pediatric Medulloblastoma Using Only Ct Information, Soleil Hernandez, Callistus Nguyen, Skylar Gay, Jack Duryea, Rebecca Howell, David Fuentes, Jeannette Parkes, Hester Burger, Carlos Cardenas, Arnold C Paulino, Julianne Pollard-Larkin, Laurence Court

Faculty, Staff and Student Publications

PURPOSE: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma.

METHODS: One hundred four retrospective pediatric CT scans were used to train a GTV auto-contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number …

Factors Modulating 99mtc-Maa Planar Lung Dosimetry For 90y Radioembolization, Benjamin P Lopez, Armeen Mahvash, James P Long, Marnix G E H Lam, S Cheenu Kappadath

Faculty, Staff and Student Publications

Purpose: To investigate the accuracy and biases of predicted lung shunt fraction (LSF) and lung dose (LD) calculations via 99m Tc-macro-aggregated albumin (99m Tc-MAA) planar imaging for treatment planning of 90 Y-microsphere radioembolization.

Methods and materials: LSFs in 52 planning and LDs in 44 treatment procedures were retrospectively calculated, in consecutive radioembolization patients over a 2 year interval, using 99m Tc-MAA planar and SPECT/CT imaging. For each procedure, multiple planar LSFs and LDs were calculated using different: (1) contours, (2) views, (3) liver 99m Tc-MAA shine-through compensations, and (4) lung mass estimations. The accuracy of each planar-based LSF and LD …

Evaluation Of Plan Quality And Treatment Efficiency For Single-Isocenter/Two-Lesion Lung Stereotactic Body Radiation Therapy, Lana Sanford, Janelle A. Molloy, Sameera S. Kumar, Marcus Randall, Ronald C. Mcgarry, Damodar Pokhrel

Radiation Medicine Faculty Publications

Purpose/objectives: To evaluate the plan quality and treatment delivery efficiency of single‐isocenter/two‐lesions volumetric modulated arc therapy (VMAT) lung stereotactic body radiation therapy (SBRT).

Materials/methods: Eight consecutive patients with two peripherally located early stage nonsmall‐cell‐lung cancer (NSCLC) lung lesions underwent single‐isocenter highly conformal noncoplanar VMAT SBRT treatment in our institution. A single‐isocenter was placed between the two lesions. Doses were 54 or 50 Gy in 3 and 5 fractions respectively. Patients were treated every other day. Plans were calculated in Eclipse with AcurosXB algorithm and normalized to at least 95% of the planning target volume (PTV) receiving 100% of the prescribed …