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Li S, Luo X, Sun M, Wang Y, Zhang Z, Jiang J, Hu D, Zhang J, Wu Z, Wang Y, Huang W, Xia L. Context-dependent T-BOX transcription factor family: from biology to targeted therapy. Cell Commun Signal 2024; 22:350. [PMID: 38965548 PMCID: PMC11225425 DOI: 10.1186/s12964-024-01719-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/17/2024] [Indexed: 07/06/2024] Open
Abstract
T-BOX factors belong to an evolutionarily conserved family of transcription factors. T-BOX factors not only play key roles in growth and development but are also involved in immunity, cancer initiation, and progression. Moreover, the same T-BOX molecule exhibits different or even opposite effects in various developmental processes and tumor microenvironments. Understanding the multiple roles of context-dependent T-BOX factors in malignancies is vital for uncovering the potential of T-BOX-targeted cancer therapy. We summarize the physiological roles of T-BOX factors in different developmental processes and their pathological roles observed when their expression is dysregulated. We also discuss their regulatory roles in tumor immune microenvironment (TIME) and the newly arising questions that remain unresolved. This review will help in systematically and comprehensively understanding the vital role of the T-BOX transcription factor family in tumor physiology, pathology, and immunity. The intention is to provide valuable information to support the development of T-BOX-targeted therapy.
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Affiliation(s)
- Siwen Li
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Xiangyuan Luo
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Mengyu Sun
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Yijun Wang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Zerui Zhang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Junqing Jiang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Dian Hu
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Jiaqian Zhang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Zhangfan Wu
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Yufei Wang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China
| | - Wenjie Huang
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China.
- Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Clinical Medicine Research Center for Hepatic Surgery of Hubei Province, Key Laboratory of Organ Transplantation, Huazhong University of Science and Technology, Ministry of Education and Ministry of Public Health, Wuhan, Hubei, 430030, China.
| | - Limin Xia
- Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430030, China.
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, 710032, China.
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Malarkey M, Toscano AP, Bagheri MH, Solomon J, Machado LB, LoRusso P, Chen A, Folio LR, Goncalves PH. A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial. Heliyon 2023; 9:e18680. [PMID: 37593628 PMCID: PMC10428039 DOI: 10.1016/j.heliyon.2023.e18680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 06/23/2023] [Accepted: 07/25/2023] [Indexed: 08/19/2023] Open
Abstract
Rationale and objectives Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC represents a challenge toward an appropriate evaluation of therapy response. Objectives: 1) To describe and contrast volumetric and density changes at each time-point, including changes noted from baseline to best response, to currently used 2 dimensional-only criteria (RECIST) and 2) To report the coefficient of variation in volume measurement among three reviewers on a subset of ACC patients. Materials and methods We retrospectively assessed a cohort of 18 prospectively treated patients with ACC in a phase 2 trial with vorinostat using a volumetric (viable tumor volume, VTV) and density criteria. Three independent and blinded observers segmented target lesions across a sample of randomly selected computed tomography (CT) exams to examine inter-observer variation. Results We found that the average coefficient of variation among observers for all target lesions was 16.1%, with lung lesions displaying a smaller variation at 14.0% (p-value >0.17). We describe examples of decrease in volume and density in several lesions despite stable disease by RECIST. Conclusion This pilot study demonstrates that two-dimensional criteria such as RECIST may not be the best criteria to assess response to therapy, especially with evolving tools within picture archiving and communication system (PACS) that can assess volumetric size, density and texture, however, this should be prospectively studied.
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Affiliation(s)
- Molly Malarkey
- National Institutes of Health, 10 Center Drive, Building 10, Room 1C 340, Bethesda, MD, 20892, USA
| | - Alex P. Toscano
- National Institutes of Health, 10 Center Drive, Building 10, Room 1C 340, Bethesda, MD, 20892, USA
| | - Mohammad Hadi Bagheri
- Radiology and Imaging Sciences Clinical Center, National Institutes of Health, 10 Center Drive, Bldg. 10, Room 1C342, Bethesda, MD, 20892, USA
| | - Jeffrey Solomon
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Laura B. Machado
- Department of Radiology, Division of Body Imaging, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia PA 19104, USA
| | - Patricia LoRusso
- Yale University, 333 Cedar Street, WWW217, New Haven, CT, 06510, USA
| | - Alice Chen
- Early Clinical Trials Development Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, 31 Center Blvd, 3A44, Bethesda, MD, 20892, USA
| | - Les R. Folio
- Acting Associate Chief Medical Informatics Officer, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
- Senior Member, Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
- Professor, Department of Oncologic Sciences, University of South Florida Morsani College of Medicine, 560 Channelside Dr, Tampa, FL 33602, USA
| | - Priscila H. Goncalves
- HIV and AIDS Malignancy Branch, National Institutes of Health, Bethesda, MD, 20892, USA
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Van Court B, Neupert B, Nguyen D, Ross R, Knitz MW, Karam SD. Measurement of mouse head and neck tumors by automated analysis of CBCT images. Sci Rep 2023; 13:12033. [PMID: 37491456 PMCID: PMC10368694 DOI: 10.1038/s41598-023-39159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/20/2023] [Indexed: 07/27/2023] Open
Abstract
Animal experiments are often used to determine effects of drugs and other biological conditions on cancer progression, but poor accuracy and reproducibility of established tumor measurement methods make results unreliable. In orthotopic mouse models of head and neck cancer, tumor volumes approximated from caliper measurements are conventionally used to compare groups, but geometrical challenges make the procedure imprecise. To address this, we developed software to better measure these tumors by automated analysis of cone-beam computed tomography (CBCT) scans. This allows for analyses of tumor shape and growth dynamics that would otherwise be too inaccurate to provide biological insight. Monitoring tumor growth by calipers and imaging in parallel, we find that caliper measurements of small tumors are weakly correlated with actual tumor volume and highly susceptible to experimenter bias. The method presented provides a unique window to sources of error in a foundational aspect of preclinical head and neck cancer research and a valuable tool to mitigate them.
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Affiliation(s)
- Benjamin Van Court
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Brooke Neupert
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Diemmy Nguyen
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Richard Ross
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Michael W Knitz
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Sana D Karam
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA.
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Recent Advances in DNA Vaccines against Lung Cancer: A Mini Review. Vaccines (Basel) 2022; 10:vaccines10101586. [PMID: 36298450 PMCID: PMC9612219 DOI: 10.3390/vaccines10101586] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022] Open
Abstract
Lung cancer is regarded as the major causes of patient death around the world. Although the novel tumor immunotherapy has made great progress in the past decades, such as utilizing immune checkpoint inhibitors or oncolytic viruses, the overall 5-year survival of patients with lung cancers is still low. Thus, development of effective vaccines to treat lung cancer is urgently required. In this regard, DNA vaccines are now considered as a promising immunotherapy strategy to activate the host immune system against lung cancer. DNA vaccines are able to induce both effective humoral and cellular immune responses, and they possess several potential advantages such as greater stability, higher safety, and being easier to manufacture compared to conventional vaccination. In the present review, we provide a global overview of the mechanism of cancer DNA vaccines and summarize the innovative neoantigens, delivery platforms, and adjuvants in lung cancer that have been investigated or approved. Importantly, we highlight the recent advance of clinical studies in the field of lung cancer DNA vaccine, focusing on their safety and efficacy, which might accelerate the personalized design of DNA vaccine against lung cancer.
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Stadler CB, Lindvall M, Lundström C, Bodén A, Lindman K, Rose J, Treanor D, Blomma J, Stacke K, Pinchaud N, Hedlund M, Landgren F, Woisetschläger M, Forsberg D. Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training. J Digit Imaging 2021; 34:105-115. [PMID: 33169211 PMCID: PMC7887127 DOI: 10.1007/s10278-020-00384-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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Affiliation(s)
- Caroline Bivik Stadler
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden. .,Department of Health, Medicine and Caring Sciences (HMV), Linköping University, SE-581 85, Linköping, Sweden.
| | - Martin Lindvall
- Sectra AB, Teknikringen 20, SE-583 30, Linköping, Sweden.,Department of Science and Technology (ITN), Linköping University, Campus Norrköping, SE-601 74, Norrköping, Sweden
| | - Claes Lundström
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden.,Sectra AB, Teknikringen 20, SE-583 30, Linköping, Sweden.,Department of Science and Technology (ITN), Linköping University, Campus Norrköping, SE-601 74, Norrköping, Sweden
| | - Anna Bodén
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden.,Department of Clinical Pathology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden.,Department of Biomedical and Clinical Sciences (BKV), Linköping University, SE-581 85, Linköping, Sweden
| | - Karin Lindman
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden.,Department of Clinical Pathology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden.,Department of Biomedical and Clinical Sciences (BKV), Linköping University, SE-581 85, Linköping, Sweden
| | - Jeronimo Rose
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden
| | - Darren Treanor
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden.,Department of Clinical Pathology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden.,Department of Biomedical and Clinical Sciences (BKV), Linköping University, SE-581 85, Linköping, Sweden.,Department of Cellular Pathology, Leeds Teaching Hospital NHS Trust, Beckett St, Leeds, LS9 7TF, UK.,University of Leeds, Leeds, LS2 9JT, UK
| | - Johan Blomma
- Department of Radiology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden
| | - Karin Stacke
- Sectra AB, Teknikringen 20, SE-583 30, Linköping, Sweden.,Department of Science and Technology (ITN), Linköping University, Campus Norrköping, SE-601 74, Norrköping, Sweden
| | - Nicolas Pinchaud
- ContextVision AB, Klara Norra Kyrkogata 31, SE-111 22, Stockholm, Sweden
| | - Martin Hedlund
- ContextVision AB, Klara Norra Kyrkogata 31, SE-111 22, Stockholm, Sweden
| | - Filip Landgren
- Department of Radiology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden
| | - Mischa Woisetschläger
- Center for Medical Image Science and Visualization (CMIV), Linköping University Hospital, Linköping University, SE-581 85, Linköping, Sweden.,Department of Radiology, Region Östergötland, Linköping University Hospital, SE-581 85, Linköping, Sweden
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Shafiei A, Bagheri M, Farhadi F, Apolo AB, Biassou NM, Folio LR, Jones EC, Summers RM. CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1. Radiol Imaging Cancer 2021; 3:e200090. [PMID: 33874734 PMCID: PMC8189184 DOI: 10.1148/rycan.2021200090] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 02/11/2021] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.
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Gomes JPP, Veloso JDRC, Altemani AMDAM, Chone CT, Altemani JMC, de Freitas CF, Lima CSP, Braz-Silva PH, Costa ALF. Three-Dimensional Volume Imaging to Increase the Accuracy of Surgical Management in a Case of Recurrent Chordoma of the Clivus. AMERICAN JOURNAL OF CASE REPORTS 2018; 19:1168-1174. [PMID: 30275439 PMCID: PMC6180943 DOI: 10.12659/ajcr.911592] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Patient: Male, 53 Final Diagnosis: Clivus chordoma Symptoms: Pain the eye Medication: — Clinical Procedure: — Specialty: Radiology
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Affiliation(s)
- João Pedro Perez Gomes
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of Sao Paulo (USP), São Paulo, SP, Brazil
| | - José de Ribamar Castro Veloso
- Department of Orthodontics and Radiology, School of Dentistry, University City of São Paulo (UNICID), São Paulo, SP, Brazil
| | | | - Carlos Takahiro Chone
- Department of Ophthalmology and Otorhinolaryngology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Claudio Fróes de Freitas
- Department of Orthodontics and Radiology, School of Dentistry, University City of São Paulo (UNICID), São Paulo, SP, Brazil
| | - Carmen Silvia Passos Lima
- Department of Internal Medicine, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Paulo Henrique Braz-Silva
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
| | - Andre Luiz Ferreira Costa
- Department of Orthodontics and Radiology, School of Dentistry, University City of São Paulo (UNICID), São Paulo, SP, Brazil
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Folio LR, Machado LB, Dwyer AJ. Multimedia-enhanced Radiology Reports: Concept, Components, and Challenges. Radiographics 2018. [PMID: 29528822 DOI: 10.1148/rg.2017170047] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Multimedia-enhanced radiology report (MERR) development is defined and described from an informatics perspective, in which the MERR is seen as a superior information-communicating entity. Recent technical advances, such as the hyperlinking of report text directly to annotated images, improve MERR information content and accessibility compared with text-only reports. The MERR is analyzed by its components, which include hypertext, tables, graphs, embedded images, and their interconnections. The authors highlight the advantages of each component for improving the radiologist's communication of report content information and the user's ability to extract information. Requirements for MERR implementation (eg, integration of picture archiving and communication systems, radiology information systems, and electronic medical record systems) and the authors' initial experiences and challenges in MERR implementation at the National Institutes of Health are reviewed. The transition to MERRs has provided advantages over use of traditional text-only radiology reports because of the capacity to include hyperlinked report text that directs clinicians to image annotations, images, tables, and graphs. A framework is provided for thinking about the MERR from the user's perspective. Additional applications of emerging technologies (eg, artificial intelligence and machine learning) are described in the crafting of what the authors believe is the radiology report of the future. ©RSNA, 2018.
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Affiliation(s)
- Les R Folio
- From Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Bethesda, MD 20892
| | - Laura B Machado
- From Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Bethesda, MD 20892
| | - Andrew J Dwyer
- From Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Bethesda, MD 20892
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Radiology Reports With Hyperlinks Improve Target Lesion Selection and Measurement Concordance in Cancer Trials. AJR Am J Roentgenol 2017; 208:W31-W37. [PMID: 28112557 DOI: 10.2214/ajr.16.16845] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Radiology reports often lack the measurements of target lesions that are needed for oncology clinical trials. When available, the measurements in the radiology reports often do not match those in the records used to calculate therapeutic response. This study assessed the clinical value of hyperlinked tumor measurements in multimedia-enhanced radiology reports in the PACS and the inclusion of a radiologist assistant in the process of assessing tumor burden. MATERIALS AND METHODS We assessed 489 target lesions in 232 CT examinations of 71 patients with metastatic genitourinary cancer enrolled in two therapeutic trials. We analyzed target lesion selection and measurement concordance between oncology records (used to calculate therapeutic response) and two types of radiology reports in the PACS: multimedia-enhanced radiology reports and text-only reports. For statistical tests, we used the Wilcoxon signed rank, Wilcoxon rank sum test, and Fisher method to combine p values from the paired and unpaired results. The Fisher exact test was used to compare overall measurement concordance. RESULTS Concordance on target lesion selection was greater for multimedia-enhanced radiology reports (78%) than the text-only reports (52%) (p = 0.0050). There was also improved overall measurement concordance with the multimedia-enhanced radiology reports (68%) compared with the text-only reports (38%) (p < 0.0001). CONCLUSION Compared with text-only reports, hyperlinked multimedia-enhanced radiology reports improved concordance of target lesion selection and measurement with the measurements used to calculate therapeutic response.
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Fenerty KE, Folio LR, Patronas NJ, Marté JL, Gulley JL, Heery CR. Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST. BMC Cancer 2016; 16:672. [PMID: 27553491 PMCID: PMC4995658 DOI: 10.1186/s12885-016-2699-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 08/09/2016] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The Response Evaluation Criteria in Solid Tumors (RECIST) are the current standard for evaluating disease progression or therapy response in patients with solid tumors. RECIST 1.1 calls for axial, longest-diameter (or perpendicular short axis of lymph nodes) measurements of a maximum of five tumors, which limits clinicians' ability to adequately measure disease burden, especially in patients with irregularly shaped tumors. This is especially problematic in chordoma, a disease for which RECIST does not always adequately capture disease burden because chordoma tumors are typically irregularly shaped and slow-growing. Furthermore, primary chordoma tumors tend to be adjacent to vital structures in the skull or sacrum that, when compressed, lead to significant clinical consequences. METHODS Volumetric segmentation is a newer technology that allows tumor burden to be measured in three dimensions on either MR or CT. Here, we compared the ability of RECIST measurements and tumor volumes to predict clinical outcomes in a cohort of 21 chordoma patients receiving immunotherapy. RESULTS There was a significant difference in radiologic time to progression Kaplan-Meier curves between clinical outcome groups using volumetric segmentation (P = 0.012) but not RECIST (P = 0.38). In several cases, changes in volume were earlier and more sensitive reflections of clinical status. CONCLUSION RECIST is a useful evaluation method when obvious changes are occurring in patients with chordoma. However, in many cases, RECIST does not detect small changes, and volumetric assessment was capable of detecting changes and predicting clinical outcome earlier than RECIST. Although this study was small and retrospective, we believe our results warrant further research in this area.
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Affiliation(s)
- Kathleen E Fenerty
- Laboratory of Tumor Immunology and Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room 13N208, Bethesda, MD, 20892, USA
| | - Les R Folio
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nicholas J Patronas
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer L Marté
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christopher R Heery
- Laboratory of Tumor Immunology and Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room 13N208, Bethesda, MD, 20892, USA.
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