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Hartley-Blossom ZJ, Digumarthy SR. Dual-Energy Computed Tomography Applications in Lung Cancer. Radiol Clin North Am 2023; 61:987-994. [PMID: 37758365 DOI: 10.1016/j.rcl.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
This article examines the intrathoracic applications for dual-energy computed tomography (DECT), focusing on lung cancer. The topics covered include the image data sets, methods for iodine quantification, and clinical applications. The applications of DECT are to differentiate benign and malignant lung nodules, determining the grade of lung cancer and expression of ki-67 expression. Iodine quantification has role in assessment of treatment response in both the primary tumor and nodal metastases.
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Affiliation(s)
- Zachary J Hartley-Blossom
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Subba R Digumarthy
- Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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Merchant SA, Shaikh MJS, Nadkarni P. Tuberculosis conundrum - current and future scenarios: A proposed comprehensive approach combining laboratory, imaging, and computing advances. World J Radiol 2022; 14:114-136. [PMID: 35978978 PMCID: PMC9258306 DOI: 10.4329/wjr.v14.i6.114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 04/17/2022] [Accepted: 05/28/2022] [Indexed: 02/06/2023] Open
Abstract
Tuberculosis (TB) remains a global threat, with the rise of multiple and extensively drug resistant TB posing additional challenges. The International health community has set various 5-yearly targets for TB elimination: mathematical modelling suggests that a 2050 target is feasible with a strategy combining better diagnostics, drugs, and vaccines to detect and treat both latent and active infection. The availability of rapid and highly sensitive diagnostic tools (Gene-Xpert, TB-Quick) will vastly facilitate population-level identification of TB (including rifampicin resistance and through it, multi-drug-resistant TB). Basic-research advances have illuminated molecular mechanisms in TB, including the protective role of Vitamin D. Also, Mycobacterium tuberculosis impairs the host immune response through epigenetic mechanisms (histone-binding modulation). Imaging will continue to be key, both for initial diagnosis and follow-up. We discuss advances in multiple imaging modalities to evaluate TB tissue changes, such as molecular imaging techniques (including pathogen-specific positron emission tomography imaging agents), non-invasive temporal monitoring, and computing enhancements to improve data acquisition and reduce scan times. Big data analysis and Artificial Intelligence (AI) algorithms, notably in the AI sub-field called “Deep Learning”, can potentially increase the speed and accuracy of diagnosis. Additionally, Federated learning makes multi-institutional/multi-city AI-based collaborations possible without sharing identifiable patient data. More powerful hardware designs - e.g., Edge and Quantum Computing- will facilitate the role of computing applications in TB. However, “Artificial Intelligence needs real Intelligence to guide it!” To have maximal impact, AI must use a holistic approach that incorporates time tested human wisdom gained over decades from the full gamut of TB, i.e., key imaging and clinical parameters, including prognostic indicators, plus bacterial and epidemiologic data. We propose a similar holistic approach at the level of national/international policy formulation and implementation, to enable effective culmination of TB’s endgame, summarizing it with the acronym “TB - REVISITED”.
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Affiliation(s)
- Suleman Adam Merchant
- Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai 400022, Maharashtra, India
| | - Mohd Javed Saifullah Shaikh
- Department of Radiology, North Bengal Neuro Centre, Jupiter magnetic resonance imaging, Diagnostic Centre, Siliguri 734003, West Bengal, India
| | - Prakash Nadkarni
- College of Nursing, University of Iowa, Iowa 52242, IA, United States
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Dual-Energy Computed Tomography for Evaluation of Breast Cancer Follow-Ups: Comparison of Virtual Monoenergetic Images and Iodine-Map. Diagnostics (Basel) 2022; 12:diagnostics12040946. [PMID: 35453994 PMCID: PMC9028705 DOI: 10.3390/diagnostics12040946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/09/2022] [Indexed: 02/01/2023] Open
Abstract
Differentiating tumor tissue from dense breast tissue can be difficult. Dual-energy CT (DECT) could be suitable for making diagnoses at breast cancer follow-ups. This study investigated the contrast in DECT images and iodine maps for patients with breast cancer being followed-up. Chest CT images captured in 2019 were collected. Five cases of metastatic breast cancer in the lungs were analyzed; the contrast-to-noise ratio (for breast tissue and muscle: CNRb and CNRm, respectively), tumor-to-breast mammary gland ratio (T/B), and tumor-to-muscle ratio (T/M) were calculated. For 84 cases of no metastasis, monochromatic spectral and iodine maps were obtained to compare differences under various breast densities using the K-means algorithm. The optimal T/B, T/M, and CNRb (related to mammary glands) were achieved for the 40-keV image. Conversely, CNRm (related to lungs) was better for higher energy. The optimal balance was achieved at 80 keV. T/B, T/M, and CNR were excellent for iodine maps, particularly for density > 25%. In conclusion, energy of 80 keV is the parameter most suitable for observing the breast and lungs simultaneously by using monochromatic spectral images. Adding iodine mapping can be appropriate when a patient’s breast density is greater than 25%.
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Wang Y, Shang X, Wang L, Fan J, Tian F, Wang X, Kong W, Wang J, Wang Y, Ma X. Clinical characteristics and chest computed tomography findings related to the infectivity of pulmonary tuberculosis. BMC Infect Dis 2021; 21:1197. [PMID: 34837990 PMCID: PMC8627638 DOI: 10.1186/s12879-021-06901-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 11/22/2021] [Indexed: 02/06/2023] Open
Abstract
AIM This study mainly evaluates the clinical characteristics and chest chest computed tomography (CT) findings of AFB-positive and AFB-negative pulmonary tuberculosis (PTB) patients to explore the relationship between AFB-positive and clinico-radiological findings. METHODS A retrospective analysis of 224 hospitalized tuberculosis patients from 2018 to 2020 was undertaken. According to the AFB smear results, they were divided into AFB-positive pulmonary tuberculosis (positive by Ziehl-Neelsen staining) and AFB-negative pulmonary tuberculosis and patients' CT results and laboratory test results were analyzed. RESULTS A total of 224 PTB patients were enrolled. AFB-positive (n = 94, 42%) and AFB-negative (n = 130, 58%). AFB-positive patients had more consolidation (77.7% vs. 53.8%, p < 0.01), cavity (55.3% vs. 34.6%, p < 0.01), calcification (38.3% vs. 20%, p < 0.01), bronchiectasis (7.5% vs. 1.5%, p < 0.05), bronchiarctia (6.4% vs. 0.8%, p < 0.05), and right upper lobe involvement (57.5% vs. 33.1%, p < 0.01), left upper lobe involvement (46.8% vs. 33.1%, p < 0.05) and lymphadenopathy (58.5% vs. 37.7%, p < 0.01). CONCLUSION The study found that when pulmonary tuberculosis patients have consolidation, cavity, upper lobe involvement and lymphadenopathy on chest CT images, they may have a higher risk of AFB-positive tuberculosis.
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Affiliation(s)
- Yuanyuan Wang
- First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Xiaoqian Shang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Liang Wang
- First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Jiahui Fan
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Fengming Tian
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Xuanzheng Wang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Weina Kong
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China
| | - Jing Wang
- Respiratory Department of the Second Affiliated Hospital of Hainan Medical College, Haikou, 570000, Hainan, People's Republic of China
| | - Yunling Wang
- First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China.
| | - Xiumin Ma
- First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China.
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Laboratory Center, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi, 830011, Xinjiang, People's Republic of China.
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