CT angiography-based radiomics as a tool for carotid plaque characterization: a pilot study

Cilla, Savino; Macchia, G.; Lenkowicz, Jacopo; Tran, Huong Elena; Pierro, Antonio; Petrella, Lella; Fanelli, Mara; Sardu, Celestino; Re, Alessia; Boldrini, Luca; Indovina, Luca; Filippo, Carlo Maria De; Caradonna, Eugenio; Deodato, Francesco; Massetti, Massimo; Valentini, Vincenzo; Modugno, Pietro

doi:10.1007/s11547-022-01505-5

Cited by 18 publications

(15 citation statements)

References 46 publications

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“…This is due to several factors, including a lack of harmonization of imaging protocols, clinical validation issues, and an overall poor scientific quality of the studies in the field [ 43 , 45 , 103 , 104 ]. In line with this scenario, a RQS of 27.8% (range 22.2–38.9%) was calculated for the 8 articles retrieved in this review ( Table 2 ), consistent with other reviews focused on radiomics [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 ].…”

Section: Discussionsupporting

confidence: 85%

Radiomics in Lung Metastases: A Systematic Review

Gabelloni

Faggioni

Fusco

et al. 2023

JPM

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Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

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Section: Discussionsupporting

confidence: 85%

Radiomics in Lung Metastases: A Systematic Review

Gabelloni

Faggioni

Fusco

et al. 2023

JPM

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“…Radiomics is a rapidly evolving field in medical imaging, and studies have confirmed its applications and potential in guiding clinical decision-making and precision medicine [ 25 ]. Radiomics-based carotid artery CTA methodologies are an objective and effective means of assessing carotid atherosclerotic plaques and stratifying related risk [ 35 ]. However, the accuracy and reliability of CT-based radiomic analyses are heavily dependent on the quality of acquired images, and routine carotid artery CTA images are usually of low resolution, possibly leading to inaccurate feature extraction and subsequent risk evaluation [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Wang,

Guo,

Wang

et al. 2024

Heliyon

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“…Further, LR, LDA and ANN predictive models were constructed based on the PET quantitative indices, and ROC analysis showed that the LR model had greater predictive value in identifying epileptogenic tubers than the LDA and ANN models. LR, a sensitive and stable binary prediction machine learning method, is widely used in feature-based classification, and it has been applied to single-neuron recordings from depth-electrode microwires to predict seizure onset zones [34,35]. In addition, a predictive nomogram was constructed to make accurate localization assessments to quantitatively predict epileptogenic tubers in a personalized way.…”

Section: Discussionmentioning

confidence: 99%

Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study

Wang,

Li,

et al. 2023

BMC Med

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Background More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images. Methods Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis. Results PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70–0.83) than the LDA (AUC = 0.7506; 95% CI 0.68–0.82) and ANN models (AUC = 0.7425; 95% CI 0.67–0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71–0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71–0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74–0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75–0.87). Conclusions The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.

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CT angiography-based radiomics as a tool for carotid plaque characterization: a pilot study

Cited by 18 publications

References 46 publications

Radiomics in Lung Metastases: A Systematic Review

Radiomics in Lung Metastases: A Systematic Review

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study

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