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- Bölükbas, Deniz, et al.
(författare)
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X-Ray Dark-Field Imaging of Lung Cancer in Mice
- 2019. - 1
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Ingår i: Lung Imaging and CADx. - Boca Raton : Taylor & Francis, 2018. : CRC Press. - 9780429055959
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Bokkapitel (refereegranskat)abstract
- Lung cancer accounts for 1.6 million deaths per year worldwide. The majority of patients are diagnosed at advanced stages of the disease and often present with metastasis. Thus, the 5-year survival rate of lung cancer remains around 15%. Early diagnosis of lung cancer allows for better control of the disease with 5-year survival rates up to around 70%. Chest radiography is the most common technique for visualizing lungs. However, small lesions in the lung are often missed by conventional X-ray radiography. New technological advances, such as grating-based imaging, allow for better contrast in soft tissue. Grating-based imaging depends on the interactions between the specimen and the X-rays while they pass through, resulting in interference and refraction of the beam. Contrast acquisition from these interactions are categorized as interferometric methods. X-ray dark-field imaging relies on quantification of small-angle scattering of the X-rays during this traverse and has shown success in obtaining enhanced contrast from soft tissues such as the lung. In in vivo models, dark-field imaging has been shown to be superior to conventional radiography for visualization of pulmonary diseases including lung cancer. In this chapter, we summarize applications of this technology for imaging of lung cancer in small animals and discuss its future perspectives and potential challenges in translation.
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| 2. |
- Pisu, Francesco, et al.
(författare)
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Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography
- 2024
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Ingår i: European Radiology. - : Springer. - 0938-7994 .- 1432-1084. ; 34:6, s. 3612-3623
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)–based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques.Material and methods: We conducted a multicenter, retrospective diagnostic study (March 2013–May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications.Results: We included 790 patients (median age 72, IQR [61–80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63–76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58–0.78; p <.001) and sensitivity 80% (79–81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients.Conclusion: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy.Clinical relevance: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients.Key Points: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient’s arrival at the hospital, which streamlines the diagnosis of symptoms using ML. Graphical Abstract: [Figure not available: see fulltext.].
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| 3. |
- Saba, Luca, et al.
(författare)
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Carotid plaque-RADS : a novel stroke risk classification system
- 2024
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Ingår i: JACC Cardiovascular Imaging. - : Elsevier. - 1936-878X .- 1876-7591. ; 17:1, s. 62-75
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Tidskriftsartikel (refereegranskat)abstract
- Background: Carotid artery atherosclerosis is highly prevalent in the general population and is a well-established risk factor for acute ischemic stroke. Although the morphological characteristics of vulnerable plaques are well recognized, there is a lack of consensus in reporting and interpreting carotid plaque features.Objectives: The aim of this document is to establish a consistent and comprehensive approach for imaging and reporting carotid plaque by introducing the Plaque–Reporting and Data System (RADS) score.Methods: A panel of experts recognized the necessity to develop a classification system for carotid plaque and its defining characteristics. Using a multimodality analysis approach, the Plaque-RADS categories were established through consensus, drawing on existing published reports.Results: The authors present a universal classification that is applicable to both researchers and clinicians. The Plaque-RADS score offers a morphological assessment in addition to the prevailing quantitative parameter of “stenosis.” The Plaque-RADS score spans from grade 1 (indicating complete absence of plaque) to grade 4 (representing complicated plaque). Accompanying visual examples are included to facilitate a clear understanding of the Plaque-RADS categories.Conclusions: Plaque-RADS is a standardized and reliable system of reporting carotid plaque composition and morphology via different imaging modalities, such as ultrasound, computed tomography, and magnetic resonance imaging. This scoring system has the potential to help in the precise identification of patients who may benefit from exclusive medical intervention and those who require alternative treatments, thereby enhancing patient care. A standardized lexicon and structured reporting promise to enhance communication between radiologists, referring clinicians, and scientists.
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