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Lee AW, Pasetsky J, Lavrova E, Wang YF, Sedor GJ, Li F, Gallitto M, Garrett MD, Elliston C, Price M, Kachnic LA, Horowitz DP. CT-Guided Online Adaptive Stereotactic Body Radiotherapy for Pancreas Ductal Adenocarcinoma: Dosimetric and Initial Clinical Experience. Int J Radiat Oncol Biol Phys 2023; 117:e312. [PMID: 37785126 DOI: 10.1016/j.ijrobp.2023.06.2340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Retrospective analysis suggests that dose escalation to a biologically effective dose of more than 70 Gy may improve overall survival in patients with pancreatic ductal adenocarcinoma (PDAC), but such treatments in practice are limited by proximity of organs at risk (OARs). We hypothesized that CT-guided online adaptive radiotherapy (OART) can account for interfraction movement of OARs, reduce dose to OARs, and improve coverage of targets. MATERIALS/METHODS This is a single institution retrospective analysis of patients with PDAC treated with OART on a CBCT-based OART platform. All patients were treated to 40 Gy in 5 fractions. PTV overlapping with a 5 mm planning risk volume expansion on the stomach, duodenum and bowel received 25 Gy. Initial treatment plans were created conventionally. For each fraction, PTV and OAR volumes were recontoured with AI assistance after initial cone beam CT (CBCT). The adapted plan was calculated, underwent QA, and then compared to the scheduled plan. A second CBCT was obtained prior to delivery of the selected plan. Total treatment time (first CBCT to end of radiation delivery) and active physician time (first to second CBCT) were recorded. PTV_4000 V95%, PTV_2500 V95%, and D0.03 cc to stomach, duodenum and bowel were reported for scheduled (S) and adapted (A) plans. CTCAEv5.0 toxicities were recorded. Statistical analysis was performed using a two-sided T test and α of 0.05. RESULTS Seven patients with unresectable or locally-recurrent PDAC were analyzed, with a total of 35 fractions. Average total time was 33:00 minutes (22:25-49:40) and average active time was 22:48 minutes (14:15-39:34). All fractions were treated with adapted plans. All adapted plans met PTV_4000 V95.0% > 95.0% coverage goal and OAR dose constraints. Dosimetric data for scheduled and adapted plans per fraction are in Table 1. Median follow up was 1.7 months. 5 (71%) patients experienced either Grade 1 or 2 toxicities. No patients experienced Grade 3+ toxicities. CONCLUSION Daily OART significantly reduced dose OARs while achieving superior PTV coverage. Treatment was generally well tolerated with no grade 3+ acute toxicity, and required only 22:48 minutes on average of active physician time. Our initial clinical experience demonstrates OART allows for safe dose escalation in the treatment of PDAC.
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Affiliation(s)
- A W Lee
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - J Pasetsky
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - E Lavrova
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - Y F Wang
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - G J Sedor
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - F Li
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - M Gallitto
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - M D Garrett
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - C Elliston
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - M Price
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - L A Kachnic
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
| | - D P Horowitz
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY
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Rogers W, Keek SA, Beuque M, Lavrova E, Primakov S, Wu G, Yan C, Sanduleanu S, Gietema HA, Casale R, Occhipinti M, Woodruff HC, Jochems A, Lambin P. Towards texture accurate slice interpolation of medical images using PixelMiner. Comput Biol Med 2023; 161:106701. [PMID: 37244145 DOI: 10.1016/j.compbiomed.2023.106701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 08/06/2022] [Accepted: 11/23/2022] [Indexed: 05/29/2023]
Abstract
Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.
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Affiliation(s)
- W Rogers
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - S A Keek
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - M Beuque
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - E Lavrova
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; GIGA Cyclotron Research Centre in Vivo Imaging, University of Liège, Liège, Belgium
| | - S Primakov
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - G Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - C Yan
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - S Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - H A Gietema
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - R Casale
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - M Occhipinti
- Radiomics, Clos Chanmurly 13, 4000, Liege, Belgium
| | - H C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - A Jochems
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - P Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
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Rayn K, Lee A, Lavrova E, Gallitto M, Mayeda M, Hwang M, Padilla O, Spina C, Deutsch I, Koutcher L. Multiparametric MRI as a Predictor of PSA Response in Patients Undergoing Stereotactic Body Radiation (SBRT) Therapy for Prostate Cancer. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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