Academic Radiology最新文献

Rationale and objectives: This study aimed to develop a radiomics nomogram model using preoperative digital breast tomosynthesis (DBT) images to predict Ki-67 expression in patients with invasive breast cancer (IBC).

Materials and methods: This retrospective study involved a cohort of 289 patients with IBC, who were randomly divided into a training dataset (N= 202) and a validation dataset (N= 87). Ki-67 expression was categorized into low and high groups using a 14% threshold. Radiomics features from both the intra- and peritumoral regions of DBT images were used to develop the radiomics model, referred to as Radscore. Clinical and nomogram models were constructed using multivariate logistic regression. The performance of the established models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: The clinical model was constructed using tumor size and DBT-reported lymph node metastasis (DBT_reported_LNM). By integrating Radscore_Combine-which incorporates both intra- and peritumoral radiomics features-along with tumor size and DBT_reported_LNM into the nomogram, the model achieved the highest area under the curve (AUC) values of 0.819 and 0.755 in the training and validation datasets, respectively. The notable improvement shown by the NRI and IDI suggests that Radscore_Combine could serve as a valuable biomarker for predicting Ki-67 expression effectively.

Conclusion: The nomogram offers a non-invasive method to predict Ki-67 expression in IBC patients, which could aid in creating personalized treatment plans.

Rationale and objectives: Multiple modalities exist for diagnosing ulnar neuropathy at the elbow (UNE), including electrodiagnostic testing (EDX), ultrasound (US), and magnetic resonance imaging (MRI), with no consensus on the optimal strategy. This study's objective was to determine the most cost-effective diagnostic strategy in patients with suspected UNE.

Materials and methods: We developed a decision analytic model from the U.S. healthcare perspective over a 1-year time horizon. Our hypothetical population comprised 56-year-old males with medial elbow pain and/or paresthesias radiating to the hand, without weakness. We compared incremental cost-effectiveness and total net monetary benefit (NMB) of single-modality strategies (EDX, US, MRI) and multimodality strategies (combinations of US/MRI, EDX/US, EDX/MRI). Input probabilities and utility values were obtained from the literature, and costs from Centers for Medicaid & Medicare Services and institutional data. The primary outcome was quality-adjusted life years (QALYs). Willingness-to-pay threshold was $100,000.

Results: The diagnostic strategy utilizing US first, followed by MRI, was favored with the highest total QALYs, .935, and total NMB, $92,667. EDX and US single-modality strategies were less favorable, with lower total QALYs, .894 and .906, respectively, and lower total NMB, $88,866 and $90,022. Other diagnostic strategies were excluded by absolute or extended dominance. One-way sensitivity analyses found model results sensitive to the utility of UNE recovery, but otherwise robust over a range of costs/probabilities.

Conclusion: Our cost-effectiveness analysis suggests an initial US, then MRI is the most cost-effective strategy in the workup of patients with suspected UNE.

Climate change has widespread impacts on patient health, affecting most body organs. At the same time, healthcare systems are a large contributor to global greenhouse emissions and other environmental harms, yet very few such organizations have taken concrete steps to encourage sustainable practices. Radiology should drive sustainable change because we are one of the most energy intensive and one of the fastest growing specialties within healthcare. While most current efforts focus on decreasing carbon emissions and other impacts of individual modalities, radiologists ought to broaden their perspectives. Incentives and education for trainees and clinicians to reduce unnecessary imaging is paramount to decrease radiology's environmental impact. A three-pronged approach guides change: increasing sustainability of essential studies, leveraging education to decrease low-value imaging, and expanding equitable access to preventative (high-value) imaging services. If radiology takes the lead, other specialties may follow.

Rationale and objectives: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.

Method: A total of 254 patients (training cohort, n=132; test cohort 1, n=56；test cohort 2, n=66) who underwent hip or pelvic CT scans were included. Three different machine learning methods were used to build the Support Vector Machine (SVM) model, Logistic Regression (LR) model and Random Forest (RF) model respectively. The method that exhibited the best performance in the training cohort and test cohort 1 was selected to represent the radiomics model for subsequent studies. The mean CT Hounsfield unit of three-dimensional CT images at the proximal femur was extracted to construct the mean CTHU model. Multivariate logistic regression was performed using mean CT Hounsfield unit together with radiomics features, and the combined model was subsequently developed with a visualized nomogram.

Results: Among the radiomics models based on three machine learning methods, the LR model showed the best performance in the training cohort (AUC=0.875, 95% CI=0.806-0.926) and in the test cohort 1 (AUC=0.851, 95% CI=0.730-0.932). Compared to the mean CT model and the LR model, the combined model showed superior discriminatory power in the training cohort (AUC=0.934, 95% CI=0.895-0.972), the test cohort 1 (AUC=0.893, 95% CI=0.812-0.974) and the test cohort 2 (AUC=0.851, 95% CI=0.742-0.927).

Conclusion: The combined model, based on the mean CT Hounsfield unit of the proximal femur and radiomics features, can provide an accurate quantitative imaging basis for individualized risk prediction of hip fragility fracture.

Rationale and objectives: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.

Materials and methods: The study retrospectively enrolled 148 patients with pathologically confirmed benign and malignant testicular lesions, dividing them into: training set (n=103) and validation set (n=45). Radiomics characteristics were derived from T2-weighted(T2WI)、contrast-enhanced T1-weighted(CE-T1WI)、diffusion-weighted imaging(DWI) and Apparent diffusion coefficient(ADC) MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad scores) from the optimal radiomics model along with clinical predictors. Draw the receiver operating characteristic (ROC) curve and use the area under the curve (AUC) to evaluate and compare the predictive performance of each model. The diagnostic efficacy of the various machine learning models was evaluated using the Delong test.

Results: Radiomics features were extracted from four sequence-based groups(CE-T1WI+DWI+ADC+T2WI), and the model that combined Logistic Regression(LR) machine learning showed the best performance in the radiomics model. The clinical model identified one independent predictors. The combined clinical-radiomics model showed the best performance, whose AUC value was 0.932(95% confidence intervals(CI)0.868-0.978), sensitivity was 0.875, specificity was 0.871 and accuracy was 0.884 in validation set.

Conclusion: The combined clinical-radiomics model can be used as a reliable tool to predict benign and malignant testicular lesions and provide a reference for clinical treatment method decisions.

Rationale and objectives: The tumor microenvironment (TME) is a critical regulator of cancer progression, metastasis, and treatment response. Currently, various imaging approaches exist to assess the pathophysiological features of the TME. This systematic review provides an overview of magnetic resonance imaging (MRI) methods used in clinical practice to characterize the pathophysiological features of the gliomas TME.

Methods: This review involved a systematic comprehensive search of original open-access articles reporting the clinical use of MR imaging in glioma patients of all ages in the PubMed, Scopus, and Web of Science databases between January 2010 and December 2023. We restricted our research to papers published in the English language.

Results: A total of 1137 studies were preliminarily identified through electronic database searches. After duplicate studies were removed, 44 studies met the eligibility criteria. The glioma TME was accompanied by alterations in metabolism, pH, vascularity, oxygenation, and extracellular matrix components, including tumor-associated macrophages, and sodium concentration.

Conclusion: Multiparametric MRI is capable of noninvasively assessing the pathophysiological features and tumor-supportive niches of the TME, which is in line with its application in personalized medicine.