Use of strain ultrasound elastography versus fineneedle aspiration cytology for the differential diagnosis of thyroid nodules: a retrospective analysis

OBJECTIVE: Fine-needle aspiration cytology is the risk stratification tool for thyroid nodules, and ultrasound elastography is not routinely used for the differential diagnosis of thyroid cancer. The current study aimed to compare the diagnostic parameters of ultrasound elastography and fine-needle aspiration cytology, using surgical pathology as the reference standard. METHODS: In total, 205 patients with abnormal thyroid function test results underwent ultrasound-guided fine-needle aspiration cytology on the basis of the American College of Radiology Thyroid Imaging-Reporting and Data System classification and strain ultrasound elastography according to the ASTERIA criteria. Histopathological examination of the surgical specimens was performed according to the 2017 World Health Organization classification system. Moreover, a beneficial score analysis for each modality was conducted. RESULTS: Of 265 nodules, 212 measured X1 cm. The strain index value increased from benign to malignant nodules, and the presence of autoimmune thyroid diseases did not affect the results (p40.05 for all categories). The sensitivities of histopathological examination, ultrasound elastography, and fine-needle aspiration cytology for detection of nodules measuring X1 cm were 1, 1, and 0.97, respectively. The working area for detecting nodule(s) in a single image was similar between strain ultrasound elastography and fine-needle aspiration cytology for highly and moderately suspicious nodules. However, for mildly suspicious, unsuspicious, and benign nodules, the working area for detecting nodule(s) in a single image was higher in strain ultrasound elastography than in fine-needle aspiration cytology. CONCLUSION: Strain ultrasound elastography for highly and moderately suspicious nodules facilitated the detection of mildly suspicious, unsuspicious, and benign nodules

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

The impact of stochastic mesoscale weather systems on the Atlantic Ocean

The ocean is forced by the atmosphere on a range of spatial and temporal scales. In numerical models the atmospheric resolution sets a limit on these scales and for typical climate models mesoscale (,500 km) atmospheric forcing is absent or misrepresented. Previous studies have demonstrated that mesoscale forcing significantly affects key ocean circulation systems such as the North Atlantic subpolar gyre (SPG) and the Atlantic meridional overturning circulation (AMOC). Here we present ocean model simulations that demonstrate that the addition of realistic mesoscale atmospheric forcing leads to coherent patterns of change: a cooler sea surface in the tropical and subtropical Atlantic Ocean and deeper mixed layers in the subpolar North Atlantic in autumn, winter, and spring. These lead to robust statistically significant increases in the volume transport of the North Atlantic SPG by 10% and the AMOC by up to 10%. Our simulations use a novel stochastic parameterization-based on a cellular automata algorithm-to represent spatially coherent weather systems realistically over a range of scales, including down to the smallest resolvable by the ocean grid (;10 km). Convection-permitting atmospheric models predict changes in the intensity and frequency of mesoscale weather systems due to climate change, so representing them in coupled climate models would bring higher fidelity to future climate projections