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Yildirim O, Peck KK, Saha A, Karimi S, Lis E. Dynamic Contrast Enhanced MR Perfusion and Diffusion-Weighted Imaging of Marrow-Replacing Disorders of the Spine: A Comprehensive Review. Radiol Clin North Am 2024; 62:287-302. [PMID: 38272621 DOI: 10.1016/j.rcl.2023.09.004] [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: 01/27/2024]
Abstract
Significant advancements in cancer treatment have led to improved survival rates for patients, particularly in the context of spinal metastases. However, early detection and monitoring of treatment response remain crucial for optimizing patient outcomes. Although conventional imaging methods such as bone scan, PET, MR imaging, and computed tomography are commonly used for diagnosing and monitoring treatment, they present challenges in differential diagnoses and treatment response monitoring. This review article provides a comprehensive overview of the principles, applications, and practical uses of dynamic contrast-enhanced MR imaging and diffusion-weighted imaging in the assessment and monitoring of marrow-replacing disorders of the spine.
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Affiliation(s)
- Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | | | - Atin Saha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Sasan Karimi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Eric Lis
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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Dekker HM, Stroomberg GJ, Van der Molen AJ, Prokop M. Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents. Insights Imaging 2024; 15:62. [PMID: 38411847 PMCID: PMC10899148 DOI: 10.1186/s13244-024-01626-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024] Open
Abstract
Gadolinium-based contrast agents (GBCA) are essential for diagnostic MRI examinations. GBCA are only used in small quantities on a per-patient basis; however, the acquisition of contrast-enhanced MRI examinations worldwide results in the use of many thousands of litres of GBCA per year. Data shows that these GBCA are present in sewage water, surface water, and drinking water in many regions of the world. Therefore, there is growing concern regarding the environmental impact of GBCA because of their ubiquitous presence in the aquatic environment. To address the problem of GBCA in the water system as a whole, collaboration is necessary between all stakeholders, including the producers of GBCA, medical professionals and importantly, the consumers of drinking water, i.e. the patients. This paper aims to make healthcare professionals aware of the opportunity to take the lead in making informed decisions about the use of GBCA and provides an overview of the different options for action.In this paper, we first provide a summary on the metabolism and clinical use of GBCA, then the environmental fate and observations of GBCA, followed by measures to reduce the use of GBCA. The environmental impact of GBCA can be reduced by (1) measures focusing on the application of GBCA by means of weight-based contrast volume reduction, GBCA with higher relaxivity per mmol of Gd, contrast-enhancing sequences, and post-processing; and (2) measures that reduce the waste of GBCA, including the use of bulk packaging and collecting residues of GBCA at the point of application.Critical relevance statement This review aims to make healthcare professionals aware of the environmental impact of GBCA and the opportunity for them to take the lead in making informed decisions about GBCA use and the different options to reduce its environmental burden.Key points• Gadolinium-based contrast agents are found in sources of drinking water and constitute an environmental risk.• Radiologists have a wide spectrum of options to reduce GBCA use without compromising diagnostic quality.• Radiology can become more sustainable by adopting such measures in clinical practice.
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Affiliation(s)
- Helena M Dekker
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
| | - Gerard J Stroomberg
- RIWA-Rijn - Association of River Water Works, Groenendael 6, 3439 LV, Nieuwegein, The Netherlands
| | - Aart J Van der Molen
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
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Allam KE, Abd Elkhalek YI, Hassan HGEMA, Emara MAE. Diffusion-weighted magnetic resonance imaging in differentiation between different vertebral lesions using ADC mapping as a quantitative assessment tool. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00827-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Diffusion-weighted imaging is one of the most useful clinical MRI techniques. Including this technique with other sequences used for routine spine scanning improves sensitivity and the capacity to characterize lesions. This study aims to evaluate the utility of apparent diffusion coefficient obtained from diffusion-weighted MR imaging in differentiating between benign and malignant vertebral lesions according to the optimal cutoff ADC value.
Results
This study included 30 patients at Ain Shams University hospitals; all of them were subjected to full clinical assessment and magnetic resonance imaging. Patients were classified into 4 groups: inflammatory lesions (12 cases) followed by malignant lesions (7 cases), then benign neoplastic lesions (6 cases), then traumatic lesions (3 cases) and osteoporosis (two cases). Inflammatory lesions revealed restricted diffusion. Benign neoplastic lesions/hemangioma showed low signal at DWIs due to free diffusion, while malignant/metastatic lesions showed restricted diffusion. Traumatic lesions showed restricted diffusion. The osteoporotic lesions showed iso- to hyper-intense signal at DWIs. The mean ADC value of the benign lesions was 1.8 ± 0.43 mm2/s, while metastatic tumors was 0.96 ± 0.5 × 10–3 mm2/s; however, overlapping values may be present.
Conclusions
Compared with benign tumors, malignant tumors have lower ADC values; nevertheless, some lesions, such as tuberculosis, have low ADC values that are like those of malignant tumors. Diffusion MRI and ADC values should always be analyzed in conjunction with standard MRI sequences as well as a thorough clinical history and examination.
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Deng Y, Li C, Lv X, Xia W, Shen L, Jing B, Li B, Guo X, Sun Y, Xie C, Ke L. The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106702. [PMID: 35228147 DOI: 10.1016/j.cmpb.2022.106702] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/25/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Administration of contrast is not desirable for all cases in clinical setting, and no consensus in sequence selection for deep learning model development has been achieved, thus we aim to explore whether contrast-enhanced magnetic resonance imaging (ceMRI) can be substituted in the identification and segmentation of nasopharyngeal carcinoma (NPC) with the aid of deep learning models in a large-scale cohort. METHODS A total of 4478 eligible individuals were randomly split into training, validation and test sets, and self-constrained 3D DenseNet and V-Net models were developed using axial T1-weighted imaging (T1WI), T2WI or enhanced T1WI (T1WIC) images separately. The differential diagnostic performance between NPC and benign hyperplasia were compared among models using chi-square test. Segmentation evaluation metrics, including dice similarity coefficient (DSC) and average surface distance (ASD), were compared using paired student's t-test between T1WIC and T1WI or T2WI models or M_T1/T2, a merged output of malignant region derived from T1WI and T2WI models. RESULTS All models exhibited similar satisfactory diagnostic performance in discriminating NPC from benign hyperplasia, all attaining overall accuracy over 99.00% in all T stages of NPC. And T1WIC model exhibited similar average DSC and ASD with those of M_T1/T2 (DSC, 0.768±0.070 vs 0.764±0.070; ASD, 1.573±10.954 mm vs 1.626±10.975 mm 1.626±0.975 mm vs 1.573±0.954 mm, all p > 0.0167) in primary NPC using DenseNet, but yielded a significantly higher DSC and lower ASD than either T1WI model or T2WI model (DSC, 0.759±0.065 or 0.755±0.071; ASD, 1.661±0.898 mm or 1.722±1.133 mm, respectively, all p < 0.01) in the entire test set of NPC cohort. Moreover, the average DSCs and ASDs were not statistically significant between T1WIC model and M_T1/T2 in both.
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Affiliation(s)
- Yishu Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Chaofeng Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; Precision Medicine Center, Sun Yat-Sen University, Guangzhou 510060, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Weixiong Xia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Lujun Shen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Minimally Invasive Therapy, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Bingzhong Jing
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Bin Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xiang Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
| | - Liangru Ke
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
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