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Shi M, Simiele E, Han B, Pham D, Palomares P, Aguirre M, Gensheimer M, Vitzthum L, Le QT, Surucu M, Kovalchuk N. First-Year Experience of Stereotactic Body Radiation Therapy/Intensity Modulated Radiation Therapy Treatment Using a Novel Biology-Guided Radiation Therapy Machine. Adv Radiat Oncol 2024; 9:101300. [PMID: 38260216 PMCID: PMC10801639 DOI: 10.1016/j.adro.2023.101300] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/16/2023] [Indexed: 01/24/2024] Open
Abstract
Purpose The aim of this study was to present the first-year experience of treating patients using intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) with a biology-guided radiation therapy machine, the RefleXion X1 system, installed in a clinical setting. Methods and Materials A total of 78 patients were treated on the X1 system using IMRT and SBRT from May 2021 to May 2022. Clinical and technical data including treatment sites, number of pretreatment kilovoltage computed tomography (kVCT) scans, beam-on time, patient setup time, and imaging time were collected and analyzed. Machine quality assurance (QA) results, machine performance, and user satisfactory survey were also collected and reported. Results The most commonly treated site was the head and neck (63%), followed by the pelvis (23%), abdomen (8%), and thorax (6%). Except for 5 patients (6%) who received SBRT treatments for bony metastases in the pelvis, all treatments were conventionally fractionated IMRT. The number of kVCT scans per fraction was 1.2 ± 0.5 (mean ± standard deviation). The beam-on time was 9.2 ± 3.5 minutes. The patient setup time and imaging time per kVCT was 4.8 ± 2.6 minutes and 4.6 ± 1.5 minutes, respectively. The daily machine output deviation was 0.4 ± 1.2% from the baseline. The patient QA had a passing rate of 97.4 ± 2.8% at 3%/2 mm gamma criteria. The machine uptime was 92% of the total treatment time. The daily QA and kVCT image quality received the highest level of satisfaction. The treatment workflow for therapists received the lowest level of satisfaction. Conclusions One year after the installation, 78 patients were successfully treated with the X1 system using IMRT and/or SBRT. With the recent Food and Drug Administration clearance of biology-guided radiation therapy, our department is preparing to treat patients using positron emission tomography-guidance via a new product release, which will address deficiencies in the current image-guided radiation therapy workflow.
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Affiliation(s)
- Mengying Shi
- Department of Radiation Oncology, Stanford University, Stanford, California
- Department of Radiation Oncology, University of California, Irvine, Orange, California
| | - Eric Simiele
- Department of Radiation Oncology, Stanford University, Stanford, California
- Department of Radiation Oncology, University of Alabama, Birmingham, Alabama
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Daniel Pham
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Paul Palomares
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Michaela Aguirre
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Lucas Vitzthum
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Murat Surucu
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Nataliya Kovalchuk
- Department of Radiation Oncology, Stanford University, Stanford, California
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Runge CL, Lyness J, Gillison M, Adelstein DJ, Harari PM, Ringash JG, Geiger JL, Krempl GA, Blakaj D, Bates J, Galloway TJ, Jones CU, Gensheimer M, Dunlap NE, Phan J, Caudell J, Pennington D, Torres-Saavedra P, Yom SS, Le QT, Movsas B. Hearing Outcomes in Cisplatin or Cetuximab Combined with Radiation for Patients with HPV-Associated Oropharyngeal Cancer in NRG/RTOG 1016. Int J Radiat Oncol Biol Phys 2023; 117:S122-S123. [PMID: 37784317 DOI: 10.1016/j.ijrobp.2023.06.462] [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) NRG/RTOG 1016 was a noninferiority phase 3 trial comparing the efficacy of radiation with either cisplatin (RT+Cisp) or cetuximab (RT+Cetux) for patients with HPV+ oropharyngeal cancer (OPC). Perceived hearing handicap was included as a patient-reported outcome (PRO) secondary endpoint. The primary hypothesis was that perceived hearing handicap would be greater for patients receiving RT+Cisp compared to RT+Cetux. MATERIALS/METHODS Perceived hearing handicap was measured at baseline, end of treatment, 3, 6, and 12-months post-treatment using the Hearing Handicap Inventory for Adults Screening Version (HHIA-S), a 10-item self-assessment questionnaire designed to measure patients' reactions to their hearing loss. Total HHIA-S scores range from 0 to 40; higher total score indicates more severe perceived hearing handicap. Hearing handicap categories (none, mild/moderate, and severe) were also analyzed. Mixed ordinal logistic models were used to analyze the raw HHIA-S scores and handicap categories (2-sided alpha 0.05). RESULTS Participation in the PRO assessments was optional, with 368 patients participating in the hearing PRO. No significant differences in patient/tumor characteristics were found between PRO participants/non-participants. Pre-treatment (mean [SD]) HHIA-S scores were not different for RT+Cisp (3.23 [6.28]) and RT+Cetux (4.77 [8.14]) groups. Post-treatment HHIA-S scores increased for RT+Cisp, and remained stable at the later follow-up time points. RT+Cetux scores remained stable from baseline. Change score from pre- to post-treatment was higher for RT+Cisp (4.32, 95% CI = [2.57, 6.07]) than RT+Cetux (0.08, 95% CI = [-1.15, 1.31]; p < 0.001). For hearing handicap category, post-treatment RT+Cisp had a significantly higher percentage of mild/moderate and severe cases (32%) compared to RT+Cetux (20%). From pre- to post-treatment, worsening of hearing handicap category from normal to mild/moderate or severe was greater for RT+Cisp (24%) than for RT+Cetux (9%). The conditional odds of being in a higher self-perceived hearing handicap category in the RT+Cisp arm were 3.57 (95% CI [2.04, 6.25]) times that in the RT+Cetux arm. Averaging over patients, the marginal odds ratio was 2.46 (95% CI [1.65, 3.66]). CONCLUSION Patients receiving concurrent RT+Cisp for HPV-associated OPC have significantly higher odds of worsening self-perceived hearing handicap after treatment than with RT+Cetux. This was consistent across time through one-year post-treatment. These findings inform hearing-related outcomes for patients with HPV-associated OPC.
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Affiliation(s)
- C L Runge
- Medical College of Wisconsin, Milwaukee, WI
| | - J Lyness
- NRG Oncology Statistics and Data Management Center, Philadelphia, PA; The American College of Radiology, Philadelphia, PA
| | | | | | - P M Harari
- University of Wisconsin Carbone Cancer Center, Madison, WI
| | - J G Ringash
- University Health Network- Princess Margaret Hospital, Toronto, ON, Canada
| | - J L Geiger
- Cleveland Clinic Foundation, Cleveland, OH
| | - G A Krempl
- University of Oklahoma Health Sciences Center, OKLAHOMA CITY, OK
| | - D Blakaj
- Ohio State University Comprehensive Cancer Center, Columbus, OH
| | - J Bates
- Emory University Hospital Midtown, Atlanta, GA
| | | | - C U Jones
- Sutter Medical Center Sacramento, Roseville, CA
| | - M Gensheimer
- Stanford Cancer Institute Palo Alto, Stanford, CA
| | - N E Dunlap
- The James Graham Brown Cancer Center at University of Louisville, Louisville, KY
| | - J Phan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - D Pennington
- University of Arizona Cancer Center-North Campus, Tucson, AZ
| | - P Torres-Saavedra
- NRG Oncology Statistics and Data Management Center, Philadelphia, PA; The American College of Radiology, Philadelphia, PA
| | - S S Yom
- UCSF Medical Center-Mount Zion, San Francisco, CA
| | - Q T Le
- Stanford Cancer Institute Palo Alto, Stanford, CA
| | - B Movsas
- Henry Ford Hospital, Detroit, MI
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Simiele E, Han B, Skinner L, Pham D, Lewis J, Gensheimer M, Vitzthum L, Chang D, Surucu M, Kovalchuk N. Mitigation of IMRT/SBRT treatment planning errors on the novel RefleXion X1 system using FMEA within Six Sigma framework. Adv Radiat Oncol 2023; 8:101186. [PMID: 37035034 PMCID: PMC10073615 DOI: 10.1016/j.adro.2023.101186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/16/2023] [Indexed: 01/27/2023] Open
Abstract
Purpose The aim of this study was to apply the Six Sigma methodology and failure mode and effect analysis (FMEA) to mitigate errors in intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) treatment planning with the first clinical installation of RefleXion X1. Methods and Materials The Six Sigma approach consisted of 5 phases: define, measure, analyze, improve, and control. The define, measure, and analyze phases consisted of process mapping and an FMEA of IMRT and SBRT treatment planning on the X1. The multidisciplinary team outlined the workflow process and identified and ranked the failure modes associated with the plan check items using the American Association of Physicists in Medicine Task Group 100 recommendations. Items with the highest average risk priority numbers (RPNs) and severity ≥7 were prioritized for automation using the Eclipse Scripting Application Programming Interface (ESAPI). The "improve" phase consisted of developing ESAPI scripts before the clinical launch of X1 to improve efficiency and safety. In the "control" phase, the FMEA ranking was re-evaluated 1 year after clinical launch. Results Overall, 100 plan check items were identified in which the RPN values ranged from 10.2 to 429.0. Fifty of these items (50%) were suitable for automation within ESAPI. Of the 10 highest-risk items, 8 were suitable for automation. Based on the results of the FMEA, 2 scripts were developed: Planning Assistant, used by the planner during preparation for planning, and Automated Plan Check, used by the planner and the plan checker during plan preparation for treatment. After 12 months of clinical use of the X1 and developed scripts, only 3 errors were reported. The average prescript RPN was 138.0, compared with the average postscript RPN of 47.8 (P < .05), signifying a safer process. Conclusions Implementing new technology in the clinic can be an error-prone process in which the likelihood of errors increases with increasing pressure to implement the technology quickly. To limit errors in clinical implementation of the novel RefleXion X1 system, the Six Sigma method was used to identify failure modes, establish quality control checks, and re-evaluate these checks 1 year after clinical implementation.
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Affiliation(s)
- Eric Simiele
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Lawrie Skinner
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Daniel Pham
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Jonathan Lewis
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Lucas Vitzthum
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Daniel Chang
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Murat Surucu
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Nataliya Kovalchuk
- Department of Radiation Oncology, Stanford University, Stanford, California
- Corresponding author: Nataliya Kovalchuk, PhD
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Wang J, Qu V, Hui C, Sandhu N, Mendoza M, Panjwani N, Lin J, Chang Y, Liang C, LU J, Wang L, Kovalchuk N, Gensheimer M, Soltys S, Pollom E. Stratified Assessment of a Commercial Deep Learning Algorithm for Automated Detection and Contouring of Metastatic Brain Tumors in Stereotactic Radiosurgery. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.2193] [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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Lau B, Wu Y, Fu J, Cui S, Pham D, Gee H, Skinner L, Shirato H, Taguchi H, Chin A, Gensheimer M, Diehn M, Loo B, Vitzthum L. OA14.04 Chest Wall Toxicity after Individualized Stereotactic Ablative Radiotherapy for Lung Tumors. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.069] [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: 11/30/2022]
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Liang X, Bassenne M, Hristov DH, Islam T, Zhao W, Jia M, Zhang Z, Gensheimer M, Beadle B, Le Q, Xing L. Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Comput Biol Med 2022; 141:105139. [PMID: 34942395 PMCID: PMC8810749 DOI: 10.1016/j.compbiomed.2021.105139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 08/26/2021] [Revised: 12/10/2021] [Accepted: 12/11/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning. METHODS We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist. RESULTS The system positioning errors of translation and rotation are less than 0.47 mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13 mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0 mm/1.0°) from 66.67% to 90.91% as compared to standard registrations. CONCLUSIONS We proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.
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Affiliation(s)
- Xiaokun Liang
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Maxime Bassenne
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Dimitre H. Hristov
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305 USA
| | - Tauhidul Islam
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305 USA
| | - Wei Zhao
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Mengyu Jia
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Zhicheng Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Beth Beadle
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Quynh Le
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
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7
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Pham D, Simiele E, Breitkreutz D, Capaldi D, Han B, Surucu M, Oderinde S, Vitzthum L, Gensheimer M, Bagshaw H, Chin A, Xing L, Chang DT, Kovalchuk N. IMRT and SBRT Treatment Planning Study for the First Clinical Biology-Guided Radiotherapy System. Technol Cancer Res Treat 2022; 21:15330338221100231. [PMID: 35579876 PMCID: PMC9118457 DOI: 10.1177/15330338221100231] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Purpose: The first clinical biology-guided radiation therapy (BgRT) system—RefleXionTM X1—was installed and commissioned for clinical use at our institution. This study aimed at evaluating the treatment plan quality and delivery efficiency for IMRT/SBRT cases without PET guidance. Methods: A total of 42 patient plans across 6 cancer sites (conventionally fractionated lung, head, and neck, anus, prostate, brain, and lung SBRT) planned with the EclipseTM treatment planning system (TPS) and treated with either a TrueBeam® or Trilogy® were selected for this retrospective study. For each Eclipse VMAT plan, 2 corresponding plans were generated on the X1 TPS with 10 mm jaws (X1-10mm) and 20 mm jaws (X1-20mm) using our institutional planning constraints. All clinically relevant metrics in this study, including PTV D95%, PTV D2%, Conformity Index (CI), R50, organs-at-risk (OAR) constraints, and beam-on time were analyzed and compared between 126 VMAT and RefleXion plans using paired t-tests. Results: All but 3 planning metrics were either equivalent or superior for the X1-10mm plans as compared to the Eclipse VMAT plans across all planning sites investigated. The Eclipse VMAT and X1-10mm plans generally achieved superior plan quality and sharper dose fall-off superior/inferior to targets as compared to the X1-20mm plans, however, the X1-20mm plans were still considered acceptable for treatment. On average, the required beam-on time increased by a factor of 1.6 across all sites for X1-10mm compared to X1-20mm plans. Conclusions: Clinically acceptable IMRT/SBRT treatment plans were generated with the X1 TPS for both the 10 mm and 20 mm jaw settings.
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Affiliation(s)
- Daniel Pham
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Eric Simiele
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Dylan Breitkreutz
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Dante Capaldi
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Bin Han
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Murat Surucu
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | | | - Lucas Vitzthum
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Michael Gensheimer
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Hilary Bagshaw
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Alex Chin
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - D T Chang
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
| | - Natalyia Kovalchuk
- Department of Radiation Oncology, 6429Stanford University, Stanford, CA, USA
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Liang X, Bassenne M, Zhao W, Jia M, Zhang Z, Huang C, Gensheimer M, Beadle B, Le Q, Xing L. Human-Level Comparable Control Volumes Mapping With an Unsupervised-Learning Model for CT-Guided Radiotherapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.481] [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: 10/20/2022]
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9
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Benson KRK, Sandhu N, Zhang C, Ko R, Toesca DAS, Lee PE, Von Eyben R, Diehn M, Gensheimer M, Maxim PG, Bush K, Loo BW, Soltys SG, Pollom EL, Chang DT. Local Recurrence Outcomes of Colorectal Cancer Oligometastases Treated With Stereotactic Ablative Radiotherapy. Am J Clin Oncol 2021; 44:559-564. [PMID: 34534143 DOI: 10.1097/coc.0000000000000864] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
PURPOSE The aim of this study was to report local failure (LF) outcomes and associated predictors in patients with oligometastatic colorectal cancer (CRC) treated with stereotactic ablative radiotherapy (SABR). MATERIALS AND METHODS We retrospectively reviewed patients with CRC metastases to the brain, liver, spine, or lung treated with SABR between 2001 and 2016. Time to LF was summarized using cumulative incidence of LF curves with death as a competing risk. RESULTS The analysis included a total of 130 patients and 256 lesions. Of the metastases treated, 129 (50%) were brain, 50 (20%) liver, 49 (19%) spine, and 28 (11%) lung. Median gross tumor volume was 24 mL for liver metastases, 2 mL for brain metastases, 4 mL for spine metastases, and 1 mL for lung metastases. The overall 1, 2, and 3-year cumulative incidence of LF rates were 21.6% (16.5, 27.1), 28.2% (22.3, 34.4), and 31.5% (25.2, 38.0), respectively. LF was highest among the liver metastases (1 y: 26.0%, 2 y: 38.5%), followed by spine (1 y: 25.1%, 2 y: 31.1%), brain (1 y: 20%, 2 y: 25.2%), and lung (1 y: 13.7%, 2 y: insufficient data). Metastases from right-sided primary CRC were significantly more likely to have LF (P=0.0146, HR=2.23). Biologically effective dose>70 Gy, defined using a standard linear quadratic model using α/β ratio of 10 on the individual lesion level, and pre-SABR chemotherapy were also significant predictors of LF (P= 0.0009 and 0.018, respectively). CONCLUSIONS CRC metastases treated with SABR had significantly higher rates of LF if they originated from right-sided primary CRC, compared with left-sided. Liver metastases had the highest rates of LF compared with other metastatic sites. Thus, CRC liver metastases and metastases from right-sided CRC may benefit from more aggressive radiotherapy.
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Affiliation(s)
- Kathryn R K Benson
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Navjot Sandhu
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Carrie Zhang
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Ryan Ko
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Diego A S Toesca
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Phoebe E Lee
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Rie Von Eyben
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Peter G Maxim
- Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN
| | - Karl Bush
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Billy W Loo
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Scott G Soltys
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Erqi L Pollom
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
| | - Daniel T Chang
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford, CA
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Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW, Wakelee H, Diehn M, Li R. Radiological tumor classification across imaging modality and histology. NAT MACH INTELL 2021; 3:787-798. [PMID: 34841195 PMCID: PMC8612063 DOI: 10.1038/s42256-021-00377-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023]
Abstract
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Chao Li
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sukhmani Padda
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Fumi Kato
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroki Shirato
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Yiran Wei
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Stephen John Price
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Joel W Neal
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Heather Wakelee
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
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Xiang M, Gensheimer M, Pollom E, Holsinger FC, Colevas AD, Le QT, Beadle B. Treatment Breaks During Definitive Head/Neck Radiotherapy: Survival Impact and Predisposing Factors. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.02.558] [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: 10/23/2022]
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12
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Jin MC, Harris JP, Sabolch AN, Gensheimer M, Le QT, Beadle BM, Pollom EL. Proton radiotherapy and treatment delay in head and neck squamous cell carcinoma. Laryngoscope 2019; 130:E598-E604. [PMID: 31837165 DOI: 10.1002/lary.28458] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [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: 08/21/2019] [Revised: 10/12/2019] [Accepted: 11/16/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVE For patients with head and neck squamous cell carcinoma (HNSCC), delays in the initiation of radiotherapy (RT) have been closely associated with worse outcomes. We sought to investigate whether RT modality (proton vs. photon) is associated with differences in the time to initiation of RT. METHODS The National Cancer Database was queried for patients diagnosed with nonmetastatic HNSCC between 2004 and 2015 who received either proton or photon RT as part of their initial treatment. Wilcoxon rank-sum and chi-square tests were used to compare continuous and categorical variables, respectively. Multivariable logistic regression was used to determine the association between use of proton RT and delayed RT initiation. RESULTS A total of 175,088 patients with HNSCC receiving either photon or proton RT were identified. Patients receiving proton RT were more likely to be white, reside in higher income areas, and have private insurance. Proton RT was associated with delayed RT initiation compared to photon RT (median 59 days vs. 45, P < 0.001). Receipt of proton therapy was independently associated with RT initiation beyond 6 weeks after diagnosis (adjusted OR [aOR, definitive RT] = 1.69; 95% confidence interval [CI] 1.26-2.30) or surgery (aOR [adjuvant RT] = 4.08; 95% CI 2.64-6.62). In the context of adjuvant proton RT, increases in treatment delay were associated with worse overall survival (weeks, adjusted hazard ratio = 1.099, 95% CI 1.011-1.194). CONCLUSION Use of proton therapy is associated with delayed RT in both the definitive and adjuvant settings for patients with HNSCC and could be associated with poorer outcomes. LEVEL OF EVIDENCE 2b Laryngoscope, 122:0000-0000, 2019 Laryngoscope, 130:E598-E604, 2020.
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Affiliation(s)
- Michael C Jin
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford
| | - Jeremy P Harris
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford.,Palo Alto Veterans Affairs Health Care System, Palo Alto, California
| | - Aaron N Sabolch
- The Center for Health Research and the Department of Radiation Oncology, Kaiser Permanente, Portland, Oregon, U.S.A
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford
| | - Erqi L Pollom
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford.,Palo Alto Veterans Affairs Health Care System, Palo Alto, California
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Wu J, Gensheimer M, Liang R, Zhang C, Pollom E, Beadle B, Le Q, Li R. Habitat Evolution Imaging Biomarkers to Assess Early Response and Predict Treatment Outcomes in Oropharyngeal Cancer. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.444] [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: 10/26/2022]
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14
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Chaudhuri AA, Chabon JJ, Lovejoy AF, Newman AM, Stehr H, Azad TD, Zhou L, Liu CL, Scherer F, Kurtz DM, Esfahani MS, Say C, Carter JN, Merriott D, Dudley J, Binkley MS, Modlin L, Padda SK, Gensheimer M, West RB, Shrager JB, Neal JW, Wakelee HA, Billy, Loo W, Alizadeh AA, Diehn M. (S012) Circulating Tumor DNA Detects Residual Disease and Anticipates Tumor Progression Earlier Than CT Imaging. Int J Radiat Oncol Biol Phys 2017. [DOI: 10.1016/j.ijrobp.2017.02.048] [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: 10/19/2022]
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15
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Pollom EL, Qian Y, Durkee BY, von Eyben R, Maxim PG, Shultz DB, Gensheimer M, Diehn M, Loo BW. Hypofractionated Intensity-Modulated Radiotherapy for Patients With Non-Small-Cell Lung Cancer. Clin Lung Cancer 2016; 17:588-594. [PMID: 27378172 DOI: 10.1016/j.cllc.2016.05.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/29/2016] [Accepted: 05/31/2016] [Indexed: 12/25/2022]
Abstract
BACKGROUND Alternative treatment regimens are needed for patients with non-small cell lung cancer (NSCLC) who cannot receive definitive treatment with concurrent chemoradiotherapy, surgery, or stereotactic ablative radiotherapy (SABR). PATIENTS AND METHODS We report survival, patterns of failure and toxicity outcomes for patients with NSCLC who were not eligible for surgical resection, concurrent chemoradiotherapy, or SABR and underwent hypofractionated intensity-modulated radiotherapy (IMRT). Kaplan-Meier survival analysis was used to evaluate the progression-free and overall survival. Competing risk analysis was used to evaluate in-field, locoregional, and distant failure. RESULTS A total of 42 patients treated to 52.5 to 60 Gy in 15 fractions were included. Most of the patients had metastatic or recurrent disease (64%) and a relatively large, centrally located tumor burden (74%). The median follow-up period was 13 months (interquartile range, 6-18 months). All patients received the total prescribed dose. The median survival was 15.1 months. The overall and progression-free survival rates at 1 year were 63% and 22.5%, respectively. The pattern of failure was predominantly distant, with only 2% of patients experiencing isolated in-field recurrence. The cumulative incidence of in-field failure at 6 and 12 months was 2.5% (95% confidence interval, 0.4%-15.6%) and 16.1% (95% confidence interval, 7.5%-34.7%), respectively. The risk of esophageal toxicity was associated with the esophageal mean dose, maximal point dose, and dose to the 5 cm3 volume. The risk of pneumonitis was associated with the lung mean dose and volume receiving 18 Gy. CONCLUSION Hypofractionated IMRT without concurrent chemotherapy provides favorable rates of local control and survival for well-selected patients with NSCLC who cannot tolerate standard definitive therapy.
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Affiliation(s)
- Erqi L Pollom
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Yushen Qian
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Ben Y Durkee
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Rie von Eyben
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Peter G Maxim
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - David B Shultz
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA; Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Michael Gensheimer
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Maximilian Diehn
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA.
| | - Billy W Loo
- Department of Radiation Oncology and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA.
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16
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Chaudhuri AA, Binkley MS, Rigdon J, Carter JN, Aggarwal S, Dudley SA, Qian Y, Kumar KA, Hara WY, Gensheimer M, Nair VS, Maxim PG, Shultz DB, Bush K, Trakul N, Le QT, Diehn M, Loo BW, Guo HH. Pre-treatment non-target lung FDG-PET uptake predicts symptomatic radiation pneumonitis following Stereotactic Ablative Radiotherapy (SABR). Radiother Oncol 2016; 119:454-60. [PMID: 27267049 DOI: 10.1016/j.radonc.2016.05.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [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: 02/26/2016] [Revised: 05/09/2016] [Accepted: 05/16/2016] [Indexed: 12/25/2022]
Abstract
PURPOSE To determine if pre-treatment non-target lung FDG-PET uptake predicts for symptomatic radiation pneumonitis (RP) following lung stereotactic ablative radiotherapy (SABR). METHODS We reviewed a 258 patient database from our institution to identify 28 patients who experienced symptomatic (grade ⩾ 2) RP after SABR, and compared them to 57 controls who did not develop symptomatic RP. We compared clinical, dosimetric and functional imaging characteristics between the 2 cohorts including pre-treatment non-target lung FDG-PET uptake. RESULTS Median follow-up time was 26.9 months. Patients who experienced symptomatic RP had significantly higher non-target lung FDG-PET uptake as measured by mean SUV (p < 0.0001) than controls. ROC analysis for symptomatic RP revealed area under the curve (AUC) of 0.74, with sensitivity 82.1% and specificity 57.9% with cutoff mean non-target lung SUV > 0.56. Predictive value increased (AUC of 0.82) when mean non-target lung SUV was combined with mean lung dose (MLD). We developed a 0-2 point model using these 2 variables, 1 point each for SUV > 0.56 or MLD > 5.88 Gy equivalent dose in 2 Gy per fraction (EQD2), predictive for symptomatic RP in our cohort with hazard ratio 10.01 for score 2 versus 0 (p < 0.001). CONCLUSIONS Patients with elevated pre-SABR non-target lung FDG-PET uptake are at increased risk of symptomatic RP after lung SABR. Our predictive model suggests patients with mean non-target lung SUV > 0.56 and MLD > 5.88 Gy EQD2 are at highest risk. Our predictive model should be validated in an external cohort before clinical implementation.
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Affiliation(s)
- Aadel A Chaudhuri
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Michael S Binkley
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Joseph Rigdon
- Quantitative Sciences Unit, Stanford University School of Medicine, United States
| | - Justin N Carter
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Sonya Aggarwal
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Sara A Dudley
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Yushen Qian
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Kiran A Kumar
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Wendy Y Hara
- Department of Radiation Oncology, Stanford University School of Medicine, United States; Stanford Cancer Institute, Stanford University School of Medicine, United States
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Viswam S Nair
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Stanford University School of Medicine, United States
| | - Peter G Maxim
- Department of Radiation Oncology, Stanford University School of Medicine, United States; Stanford Cancer Institute, Stanford University School of Medicine, United States
| | - David B Shultz
- Department of Radiation Oncology, University of Toronto, Princess Margaret Cancer Centre, Canada
| | - Karl Bush
- Department of Radiation Oncology, Stanford University School of Medicine, United States
| | - Nicholas Trakul
- Department of Radiation Oncology, University of Southern California School of Medicine, United States
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, United States; Stanford Cancer Institute, Stanford University School of Medicine, United States
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine, United States; Stanford Cancer Institute, Stanford University School of Medicine, United States; Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, United States.
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, United States; Stanford Cancer Institute, Stanford University School of Medicine, United States.
| | - Haiwei Henry Guo
- Department of Radiology and Nuclear Medicine, Stanford University School of Medicine, United States.
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Wu J, Gensheimer M, Dong X, Rubin D, Napel S, Diehn M, Loo B, Li R. SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. Med Phys 2016. [DOI: 10.1118/1.4955673] [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: 11/07/2022] Open
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18
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Juang T, Bush K, Loo B, Gensheimer M. SU-F-T-617: Remotely Pre-Planned Stereotactic Ablative Radiation Therapy: Validation of Treatment Plan Quality. Med Phys 2016. [DOI: 10.1118/1.4956802] [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: 11/07/2022] Open
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19
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Gao W, Nyflot M, Gensheimer M, Sponseller P, Jordan L, Carlson J, Kane G, Zeng J, Ford E. Do Emergent Treatments Result in More Severe Errors? Analysis of a Large Institutional Near-Miss Incident Learning Database. Int J Radiat Oncol Biol Phys 2014. [DOI: 10.1016/j.ijrobp.2014.05.576] [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: 11/29/2022]
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20
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Kollar L, Jour G, Bowen S, Gensheimer M, Hoch B, Conrad E, Eary J, Kane G, Kim E. Does Post–Neoadjuvant Therapy PET Response Predict Pathologic Outcomes in the Treatment of Sarcomas? Int J Radiat Oncol Biol Phys 2014. [DOI: 10.1016/j.ijrobp.2014.05.2201] [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: 11/16/2022]
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21
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Gensheimer M, Trister A, Hawkins D, Ermoian R. WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI. Med Phys 2014. [DOI: 10.1118/1.4889448] [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: 11/07/2022] Open
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22
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Cho E, Rubinstein L, Redman MW, Rockhill J, Halasz LM, Gensheimer M, Phillips M, Linden HM, Gadi VK. Differentiation of overall survival by breast cancer tumor subtype following stereotactic radiosurgery for brain metastasis. J Clin Oncol 2014. [DOI: 10.1200/jco.2014.32.15_suppl.e11584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Eunpi Cho
- University of Washington, Seattle, WA
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23
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Bowen SR, Nyflot MJ, Gensheimer M, Hendrickson KRG, Kinahan PE, Sandison GA, Patel SA. Challenges and opportunities in patient-specific, motion-managed and PET/CT-guided radiation therapy of lung cancer: review and perspective. Clin Transl Med 2012; 1:18. [PMID: 23369522 PMCID: PMC3560984 DOI: 10.1186/2001-1326-1-18] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2012] [Accepted: 07/25/2012] [Indexed: 12/25/2022] Open
Abstract
The increasing interest in combined positron emission tomography (PET) and computed tomography (CT) to guide lung cancer radiation therapy planning has been well documented. Motion management strategies during treatment simulation PET/CT imaging and treatment delivery have been proposed to improve the precision and accuracy of radiotherapy. In light of these research advances, why has translation of motion-managed PET/CT to clinical radiotherapy been slow and infrequent? Solutions to this problem are as complex as they are numerous, driven by large inter-patient variability in tumor motion trajectories across a highly heterogeneous population. Such variation dictates a comprehensive and patient-specific incorporation of motion management strategies into PET/CT-guided radiotherapy rather than a one-size-fits-all tactic. This review summarizes challenges and opportunities for clinical translation of advances in PET/CT-guided radiotherapy, as well as in respiratory motion-managed radiotherapy of lung cancer. These two concepts are then integrated into proposed patient-specific workflows that span classification schemes, PET/CT image formation, treatment planning, and adaptive image-guided radiotherapy delivery techniques.
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Affiliation(s)
- Stephen R Bowen
- University of Washington Medical Center, Department of Radiation Oncology, 1959 NE Pacific St, Box 356043, Seattle, WA 98195, USA.
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Deeley M, Chen A, D'Haese P, Duggan D, Gensheimer M, Coffey C, Dawant B, Ding G. SU-GG-J-111: Initial Dosimetric Validation of An Atlas-Based Method for Automatic Intracranial Segmentation. Med Phys 2008. [DOI: 10.1118/1.2961660] [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: 11/07/2022] Open
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25
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Gensheimer M, Jones CA, Graves CR, Merchant NB, Lockhart AC. Administration of oxaliplatin to a pregnant woman with rectal cancer. Cancer Chemother Pharmacol 2008; 63:371-3. [PMID: 18357450 DOI: 10.1007/s00280-008-0731-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2007] [Accepted: 03/05/2008] [Indexed: 11/28/2022]
Abstract
PURPOSE The platinum agent oxaliplatin could be useful in treatment of cancer in pregnant women, but it is fetotoxic in rats and its effect on the human fetus is unknown. METHODS Oxaliplatin was administered to a 25-year-old pregnant woman with metastatic rectal cancer from 20 to 30 weeks gestational age as part of the mFOLFOX-6 regimen. RESULTS The patient gave birth to a healthy girl at 33 weeks gestational age. At follow-up, the 3-year-old child had achieved all appropriate growth and developmental milestones. DISCUSSION Oxaliplatin is a component of several modern chemotherapy regimens. This report demonstrates the administration of oxaliplatin in the second and third trimesters of pregnancy without apparent fetal harm.
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Affiliation(s)
- Michael Gensheimer
- Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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26
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Abstract
Chalcone isomerase, an enzyme in the isoflavonoid pathway in plants, catalyzes the cyclization of chalcone into (2S)-naringenin. Chalcone isomerase sequence family and three-dimensional fold appeared to be unique to plants and has been proposed as a plant-specific gene marker. Using sensitive methods of sequence comparison and fold recognition, we have identified genes homologous to chalcone isomerase in all completely sequenced fungi, in slime molds, and in many gammaproteobacteria. The residues directly involved in the enzyme's catalytic function are among the best conserved across species, indicating that the newly discovered homologs are enzymatically active. At the same time, fungal and bacterial species that have chalcone isomerase-like genes tend to lack the orthologs of the upstream enzyme chalcone synthase, suggesting a novel variation of the pathway in these species.
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Affiliation(s)
- Michael Gensheimer
- Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, MO 64110, USA.
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