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11 results on '"Jeremy M. Aymard"'

1. Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old

Author

MD Anderson Head and Neck Cancer Symptom Working Group, Salman A. Eraj, Mona K. Jomaa, Crosby D. Rock, Abdallah S. R. Mohamed, Blaine D. Smith, Joshua B. Smith, Theodora Browne, Luke C. Cooksey, Bowman Williams, Brandi Temple, Kathryn E. Preston, Jeremy M. Aymard, Neil D. Gross, Randal S. Weber, Amy C. Hessel, Renata Ferrarotto, Jack Phan, Erich M. Sturgis, Ehab Y. Hanna, Steven J. Frank, William H. Morrison, Ryan P. Goepfert, Stephen Y. Lai, David I. Rosenthal, Tito R. Mendoza, Charles S. Cleeland, Kate A. Hutcheson, Clifton D. Fuller, Adam S. Garden, and G. Brandon Gunn

Subjects
Oropharynx, Symptoms, Patient reported outcomes, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Given the potential for older patients to experience exaggerated toxicity and symptoms, this study was performed to characterize patient reported outcomes in older patients following definitive radiation therapy (RT) for oropharyngeal cancer (OPC). Methods Cancer-free head and neck cancer survivors (>6 months since treatment completion) were eligible for participation in a questionnaire-based study. Participants completed the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN). Those patients ≥65 years old at treatment for OPC with definitive RT were included. Individual and overall symptom severity and clinical variables were analyzed. Results Of the 79 participants analyzed, 82% were male, 95% white, 41% T3/4 disease, 39% RT alone, 27% induction chemotherapy, 52% concurrent, and 18% both, and 96% IMRT. Median age at RT was 71 yrs. (range: 65–85); median time from RT to MDASI-HN was 46 mos. (2/3 > 24 mos.). The top 5 MDASI-HN items rated most severe in terms of mean (±SD) ratings (0–10 scale) were dry mouth (3.48 ± 2.95), taste (2.81 ± 3.29), swallowing (2.59 ± 2.96), mucus in mouth/throat (2.04 ± 2.68), and choking (1.30 ± 2.38) reported at moderate-severe levels (≥5) by 35, 29, 29, 18, and 13%, respectively. Thirty-nine % reported none (0) or no more than mild (1–4) symptoms across all 22 MDASI-HN symptoms items, and 38% had at least one item rated as severe (≥7). Hierarchical cluster analysis resulted in 3 patient groups: 1) ~65% with ranging from none to moderate symptom burden, 2) ~35% with moderate-severe ratings for a subset of classically RT-related symptoms (e.g. dry mouth, mucus, swallowing) and 3) 2 pts. with severe ratings of most items. Conclusions The overall long-term symptom burden seen in this older OPC cohort treated with modern standard therapy was largely favorable, yet a higher symptom group (~35%) with a distinct pattern of mostly local and classically RT-related symptoms was identified.

Published
2017
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2. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Author

Hesham Elhalawani, Timothy A. Lin, Stefania Volpe, Abdallah S. R. Mohamed, Aubrey L. White, James Zafereo, Andrew J. Wong, Joel E. Berends, Shady AboHashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke Cooksey, Shauna Campbell, Pei Yang, Khahn Nguyen, Rachel B. Ger, Carlos E. Cardenas, Xenia J. Fave, Carlo Sansone, Gabriele Piantadosi, Stefano Marrone, Rongjie Liu, Chao Huang, Kaixian Yu, Tengfei Li, Yang Yu, Youyi Zhang, Hongtu Zhu, Jeffrey S. Morris, Veerabhadran Baladandayuthapani, John W. Shumway, Alakonanda Ghosh, Andrei Pöhlmann, Hady A. Phoulady, Vibhas Goyal, Guadalupe Canahuate, G. Elisabeta Marai, David Vock, Stephen Y. Lai, Dennis S. Mackin, Laurence E. Court, John Freymann, Keyvan Farahani, Jayashree Kaplathy-Cramer, and Clifton D. Fuller

Subjects
machine learning, radiomics challenge, radiation oncology, head and neck, big data, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Published
2018
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3. Correction to: Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old

Author

MD Anderson Head and Neck Cancer Symptom Working Group, Salman A. Eraj, Mona K. Jomaa, Crosby D. Rock, Abdallah S. R. Mohamed, Blaine D. Smith, Joshua B. Smith, Theodora Browne, Luke C. Cooksey, Bowman Williams, Brandi Temple, Kathryn E. Preston, Jeremy M. Aymard, Neil D. Gross, Randal S. Weber, Amy C. Hessel, Renata Ferrarotto, Jack Phan, Erich M. Sturgis, Ehab Y. Hanna, Steven J. Frank, William H. Morrison, Ryan P. Goepfert, Stephen Y. Lai, David I. Rosenthal, Tito R. Mendoza, Charles S. Cleeland, Kate A. Hutcheson, Clifton D. Fuller, Adam S. Garden, and G. Brandon Gunn

Subjects
Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract In the original publication [1] the name of author Jeremy M. Aymard was spelled wrong. The original article was updated to rectify this error.

Published
2017
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4. Long‐term quality of life after definitive treatment of sinonasal and nasopharyngeal malignancies

Author

Gary Brandon Gunn, Ehab Y. Hanna, Michael E. Kupferman, Joshua B. Smith, Shirley Y. Su, Steven J. Frank, Matthew A. Tyler, Abdallah S.R. Mohamed, Jeremy M. Aymard, Renata Ferrarotto, Jack Phan, and Clifton D. Fuller

Subjects
Adult, Male, medicine.medical_specialty, Adolescent, Cross-sectional study, Visual analogue scale, Population, Disease, 03 medical and health sciences, 0302 clinical medicine, Quality of life, Surveys and Questionnaires, Internal medicine, medicine, Humans, Combined Modality Therapy, In patient, education, Aged, education.field_of_study, Nasopharyngeal Carcinoma, business.industry, Nasopharyngeal Neoplasms, Evidence-based medicine, Middle Aged, Cross-Sectional Studies, Otorhinolaryngology, 030220 oncology & carcinogenesis, Quality of Life, Female, business, Paranasal Sinus Neoplasms, 030217 neurology & neurosurgery
Abstract

OBJECTIVE To evaluate long-term global and site-specific health-related quality of life (HRQoL) in patients treated for sinonasal and nasopharyngeal malignancies. STUDY DESIGN Cross-sectional. METHODS One hundred fourteen patients with sinonasal and nasopharyngeal malignancies received surgery, radiation, systemic chemotherapy, or a combination thereof, with curative intent. Validated global ([EuroQol-5D] Visual Analogue Scale [EQ-5D VAS]) and disease-specific instruments (MD Anderson Symptom Inventory-Head and Neck [MDASI-HN], Anterior Skull Base Questionnaire [ASBQ]) were administered to patients who were both free of disease and had completed treatment at least 12 months previously. Associations between instruments, instrument domains, and specific clinical parameters were analyzed. RESULTS The median age was 55 years. The mean EQ-5D VAS, MDASI-22 composite score, and ASBQ score were 74 (standard deviation [SD] 21), 48 (SD 36), and 130 (SD 27), respectively. The most frequently reported high-severity items in MDASI-HN were dry mouth and difficulty tasting food. The most frequently reported high-severity items in ASBQ were difficulty with smell and nasal secretions. Advanced Tumor (T) classification was associated with worse overall ASBQ sum score (P = 0.02). ASBQ performance at home and MDASI-HN drowsy symptom items independently predicted worse global HRQoL as measured by the EQ-5D VAS (P

Published
2019

5. Patient reported dry mouth: Instrument comparison and model performance for correlation with quality of life in head and neck cancer survivors

Author

Abdallah S.R. Mohamed, Mark S. Chambers, Jeremy M. Aymard, M.A.M. Meheissen, A. Dursteler, Katherine A. Hutcheson, Charles Lu, Karine A. Al Feghali, Tito R. Mendoza, S. Eraj, Joshua B. Smith, Stefania Volpe, Ryan P. Goepfert, Anderson Head, A. White, William H. Morrison, Mona Kamal, Adam S. Garden, David I. Rosenthal, Clifton D. Fuller, Joel Berends, Erich M. Sturgis, Richard C. Cardoso, Steven J. Frank, G. Brandon Gunn, and Bowman Williams

Subjects
Adult, Male, medicine.medical_specialty, Visual analogue scale, Recursive partitioning, Xerostomia, Article, 030218 nuclear medicine & medical imaging, Cohort Studies, Correlation, 03 medical and health sciences, 0302 clinical medicine, Cancer Survivors, Quality of life, Surveys and Questionnaires, medicine, Humans, Radiology, Nuclear Medicine and imaging, Radiation Injuries, Aged, Aged, 80 and over, business.industry, Head and neck cancer, Bayes Theorem, Regression analysis, Hematology, Middle Aged, medicine.disease, Oncology, Head and Neck Neoplasms, 030220 oncology & carcinogenesis, Cohort, Quality of Life, Physical therapy, Female, Self Report, business, Cut-point
Abstract

To identify a clinically meaningful cut-point for the single item dry mouth question of the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN).Head and neck cancer survivors who had received radiation therapy (RT) completed the MDASI-HN, the University of Michigan Hospital Xerostomia Questionnaire (XQ), and the health visual analog scale (VAS) of the EuroQol Five Dimension Questionnaire (EQ-5D). The Bayesian information criteria (BIC) were used to test the prediction power of each tool for EQ-5D VAS. The modified Breiman recursive partitioning analysis (RPA) was used to identify a cut point of the MDASI-HN dry mouth score (MDASI-HN-DM) with EQ-5D VAS, using a ROC-based approach; regression analysis was used to confirm the threshold effect size.Two-hundred seven respondents formed the cohort. Median follow-up from the end of RT to questionnaire completion was 88 months. The single item MDASI-HN-DM score showed a linear relationship with the XQ composite score (ρ = 0.80, p 0.001). The MDASI-HN-DM displayed improved model performance for association with EQ-5D VAS as compared to XQ (BIC of 1803.7 vs. 2016.9, respectively). RPA showed that an MDASI-HN-DM score of ≥6 correlated with EQ-5D VAS decline (LogWorth 5.5).The single item MDASI-HN-DM correlated with the multi-item XQ and performed favorably in the prediction of QOL. A MDASI-HN-DM cut point of ≥6 correlated with decline in QOL.

Published
2018

6. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Author

M.A.M. Meheissen, David I. Rosenthal, James Zafereo, Stephen Y. Lai, Rachel B. Ger, Joel Berends, Clifton D. Fuller, David M. Vock, Mona Kamal, Adam S. Garden, Abdallah S.R. Mohamed, Carlos E. Cardenas, Ben Warren, Subha Perni, Aasheesh Kanwar, A. White, Bassem Youssef, Pei Yang, Hesham Elhalawani, Lifei Zhang, X Fave, Bowman Williams, J.A. Messer, Dennis Stephen Mackin, Jeremy M. Aymard, Baher Elgohari, Shady Abohashem, G. Elisabeta Marai, Guadalupe Canahuate, Laurence E. Court, Andrew J. Wong, and G. Brandon Gunn

Subjects
Adult, Male, medicine.medical_specialty, Science, Texture (geology), Article, 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Image Processing, Computer-Assisted, medicine, Humans, Aged, Retrospective Studies, Aged, 80 and over, Multidisciplinary, business.industry, Head and neck cancer, Middle Aged, Prognosis, medicine.disease, Primary tumor, 3. Good health, Oropharyngeal Neoplasms, 030220 oncology & carcinogenesis, Medicine, Female, Radiology, Neoplasm Recurrence, Local, Tomography, X-Ray Computed, business
Abstract

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.

Published
2018

7. Fatigue following radiation therapy in nasopharyngeal cancer survivors: a dosimetric analysis incorporating patient report and observer rating

Author

Aakash Batra, Joshua Smith, Charles S. Cleeland, Joel Berends, Carlos E. Cardenas, Clifton D. Fuller, D.I. Rosenthal, Mona Kamal, Adam S. Garden, Amit Jethanandani, G. Brandon Gunn, Abdallah S.R. Mohamed, Tito R. Mendoza, A. White, Erich M. Sturgis, A. Dursteler, S. Eraj, Jack Phan, Bowman Williams, Stefania Volpe, Ryan P. Goepfert, William H. Morrison, Jeremy M. Aymard, Baher Elgohari, and Steven J. Frank

Subjects
Adult, Male, Pituitary gland, Internal capsule, medicine.medical_treatment, Recursive partitioning, Article, 030218 nuclear medicine & medical imaging, Cohort Studies, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Cancer Survivors, Basal ganglia, medicine, Humans, Radiology, Nuclear Medicine and imaging, Radiation Injuries, Radiometry, Aged, Fatigue Syndrome, Chronic, Nasopharyngeal Carcinoma, business.industry, Radiotherapy Planning, Computer-Assisted, Brain, Common Terminology Criteria for Adverse Events, Chronic fatigue, Nasopharyngeal Neoplasms, Radiotherapy Dosage, Hematology, Middle Aged, Magnetic Resonance Imaging, Radiation therapy, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Pituitary Gland, Cohort, Female, Radiotherapy, Intensity-Modulated, Self Report, Nuclear medicine, business
Abstract

PURPOSE: To explore for fatigue-related regions of interest and the radiotherapy (RT) dose-fatigue relationship in nasopharyngeal cancer (NPC) survivors. METHODS: Eighty disease-free NPC survivors completed the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN) after RT. Fatigue was evaluated by the MDASI-HN fatigue item (MDASI-HN-F) and Common Terminology Criteria for Adverse Events v3.0 (CTC-AE), between 6 and 36 months after RT to determine the presence of chronic fatigue. Skull base MRIs and planning CT/RT dose were retrievable for 56 patients. Dosimetric data were extracted for 10 MRI-defined potential fatigue at-risk structures (FARS): brainstem (BS), pituitary gland (PG), hypothalamus (HT), basal ganglia, internal capsule, pineal gland, sub-thalamic nuclei, thalamus, substantia nigra, and hippocampus (HC). Recursive partitioning analysis (RPA) were used to identify dose-volume effects associated with chronic fatigue. RESULTS: 56 pts formed the cohort. Thirty patients (54%) reported any fatigue per MDASI-HN-F. Thirty-three pts (59%) had any fatigue by CTC-AE. The maximum point doses (Dmax) for PG, BS, HC, and HT were numerically higher in patients with fatigue. Dmax and Dmean of the PG were significantly higher in patients with chronic fatigue, p ≤ 0.01. A dose-volume threshold of PG V52Gy ≥16% (Logworth 2.4, AUC 0.7) was identified on RPA, and potential sensitivity to the PG doses was observed in younger patients (< 53 years-old). CONCLUSION: A dose-fatigue relationship was identified for the pituitary gland, both patient-reported and observer ratings. We recommend limiting the Dmax of PG to

Published
2019

8. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Author

Yao Ding, Luke Cooksey, Jayashree Kalpathy-Cramer, John Freymann, Aasheesh Kanwar, James Zafereo, Elisabeta G. Marai, Shady Abohashem, Shauna R. Campbell, A. White, Stephen Y. Lai, Clifton D. Fuller, Hesham Elhalawani, Crosby D. Rock, Guadalupe Canahuate, Abdallah S.R. Mohamed, Jeremy M. Aymard, Joel Berends, Subha Perni, Keyvan Farahani, David M. Vock, Andrew J. Wong, and Bowman Williams

Subjects
Statistics and Probability, medicine.medical_specialty, Data Descriptor, Computed tomography, Library and Information Sciences, Demographic data, Predictive markers, 030218 nuclear medicine & medical imaging, Education, 03 medical and health sciences, 0302 clinical medicine, Radiomics, medicine, Humans, Segmentation, Head and neck cancer, Demography, medicine.diagnostic_test, business.industry, Cancer, medicine.disease, 3. Good health, Computer Science Applications, Oropharyngeal Neoplasms, 030220 oncology & carcinogenesis, Cancer imaging, Radiology, Medical imaging, Statistics, Probability and Uncertainty, business, Tomography, X-Ray Computed, Information Systems
Abstract

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that ‘radiomics’, or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting.These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.

Published
2017

9. Fatigue Following Radiation Therapy in Nasopharyngeal Cancer Survivors: A Dosimetric Analysis Incorporating Patient Report and Observer Rating

Author

Jack Phan, A.D. Batra, A. White, Clifton D. Fuller, Gary Brandon Gunn, Steven J. Frank, A. Dursteler, S. Eraj, Carlos E. Cardenas, William H. Morrison, Bowman Williams, Erich M. Sturgis, Joshua Smith, Abdallah S.R. Mohamed, Tito R. Mendoza, Stefania Volpe, Ryan P. Goepfert, D.I. Rosenthal, Mona Kamal, Adam S. Garden, Jeremy M. Aymard, Baher Elgohari, Katherine A. Hutcheson, and Joel Berends

Subjects
Cancer Research, medicine.medical_specialty, Radiation, business.industry, medicine.medical_treatment, Observer rating, Radiation therapy, Oncology, medicine, Radiology, Nuclear Medicine and imaging, Radiology, Patient report, business, Nasopharyngeal cancer
Published
2018

10. Patient-Reported Dry Mouth after Radiation Therapy for Head and Neck Cancer: Dosimetric Analysis of Long-Term Outcomes

Author

Carlos E. Cardenas, A. White, Jack Phan, Clifton D. Fuller, Richard C. Cardoso, B. O'Donnell, Bowman Williams, Mark S. Chambers, S. Eraj, A. Dursteler, Erich M. Sturgis, S.J. Frank, Joel Berends, Joshua Smith, William H. Morrison, Heath D. Skinner, Stefania Volpe, Ryan P. Goepfert, Jeremy M. Aymard, Katherine A. Hutcheson, D.I. Rosenthal, Mona Kamal, Adam S. Garden, Abdallah S.R. Mohamed, Tito R. Mendoza, and Gary Brandon Gunn

Subjects
Cancer Research, medicine.medical_specialty, Radiation, business.industry, medicine.medical_treatment, Head and neck cancer, medicine.disease, Dry mouth, Radiation therapy, Oncology, Long term outcomes, medicine, Radiology, Nuclear Medicine and imaging, Radiology, medicine.symptom, business
Published
2018

11. Correction to: Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old

Author

David I. Rosenthal, Stephen Y. Lai, Clifton D. Fuller, Blaine D. Smith, G. Brandon Gunn, Bowman Williams, Jack Phan, Steven J. Frank, Amy C. Hessel, Kathryn E. Preston, Erich M. Sturgis, Neil D. Gross, Renata Ferrarotto, Tito R. Mendoza, S. Eraj, Joshua B. Smith, Brandi Temple, William H. Morrison, Theodora Browne, Charles S. Cleeland, Abdallah S.R. Mohamed, Crosby D. Rock, Jeremy M. Aymard, Ehab Y. Hanna, Katherine A. Hutcheson, Adam S. Garden, Luke Cooksey, Ryan P. Goepfert, Mona K Jomaa, and Randal S. Weber

Subjects
lcsh:Medical physics. Medical radiology. Nuclear medicine, Pediatrics, medicine.medical_specialty, business.industry, medicine.medical_treatment, lcsh:R895-920, MEDLINE, Cancer, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, Radiation therapy, 03 medical and health sciences, 0302 clinical medicine, Oncology, 030220 oncology & carcinogenesis, medicine, Radiology, Nuclear Medicine and imaging, Medical physics, In patient, business
Abstract

In the original publication [1] the name of author Jeremy M. Aymard was spelled wrong. The original article was updated to rectify this error.

Published
2017

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