A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.

MRI radiomics based machine learning model of the periaqueductal gray matter in migraine patients.

Background and purpose:

The aim of the study was to investigate the question: Can MRI radiomics analysis of the periaqueductal gray region elucidate the pathophysiological mechanisms underlying various migraine subtypes, and can a machine learning model using these radiomics features accurately differentiate between migraine patients and healthy individuals, as well as between migraine subtypes, including atypical cases with overlapping symptoms?

The study analyzed initial MRI images of individuals taken after their first migraine diagnosis, and additional MRI scans were acquired from healthy subjects. Radiomics modeling was applied to analyze all the MRI images in the periaqueductal gray region. The dataset was randomized, and oversampling was used if there was class imbalance between groups. The optimal algorithm-based feature selection method was employed to select the most important 5-10 features to differentiate between the two groups. The classification performance of AI algorithms was evaluated using receiver operating characteristic analysis to calculate the area under the curve, classification accuracy, sensitivity, and specificity values. Participants were required to have a confirmed diagnosis of either episodic migraine, probable migraine, or chronic migraine. Patients with aura, those who used migraine-preventive medication within the past six months, or had chronic illnesses, psychiatric disorders, cerebrovascular conditions, neoplastic diseases, or other headache types were excluded from the study. Additionally, 102 healthy subjects who met the inclusion and exclusion criteria were included. 

The algorithm-based information gain method for feature reduction had the best performance among all methods, with the first-order, gray-level size zone matrix, and gray-level co-occurrence matrix classes being the dominant feature classes. The machine learning model correctly classified 82.4% of migraine patients from healthy subjects. Within the migraine group, 74.1% of the episodic migraine-probable migraine patients and 90.5% of the chronic migraine patients were accurately classified. No significant difference was found between probable migraine and episodic migraine patients in terms of the periaqueductal gray region radiomics features. The kNN algorithm showed the best performance for classifying episodic migraine-probable migraine subtypes, while the Random Forest algorithm demonstrated the best performance for classifying the migraine group and chronic migraine subtype.

A radiomics-based machine learning model, utilizing standard MR images obtained during the diagnosis and follow-up of migraine patients, shows promise not only in aiding migraine diagnosis and classification for clinical approach, but also in understanding the neurological mechanisms underlying migraines. 

Tracing the Footprints of AI in Radiology Literature: A Detailed Analysis of Journal Abstracts.

PURPOSE: To assess and compare the probabilities of AI-generated content within scientific abstracts from selected Q1 journals in the fields of radiology, nuclear medicine, and imaging, published between May and August 2022 and May and August 2023. MATERIALS AND METHODS: An extensive list of Q1 journals was acquired from Scopus in the fields of radiology, nuclear medicine, and imaging. All articles in these journals were acquired from the Medline databases, focusing on articles published between May and August in 2022 and 2023. The study specifically compared abstracts for limitations of the AI detection tool in terms of word constraints. Extracted abstracts from the two different periods were categorized into two groups, and each abstract was analyzed using the AI detection tool, a system capable of distinguishing between human and AI-generated content with a validated accuracy of 97.06 %. This tool assessed the probability of each abstract being AI-generated, enabling an in-depth comparison between the two groups in terms of the prevalence of AI-generated content probability. RESULTS: Group 1 and Group 2 exhibit significant variations in the characteristics of AI-generated content probability. Group 1, consisting of 4,727 abstracts, has a median AI-generated content probability of 3.8 % (IQR1.9-9.9 %) and peaks at 49.9 %, with the computation times contained within a range of 2 to 10 seconds (IQR 3-8 s). In contrast, Group 2, which is composed of 3,917 abstracts, displays a significantly higher median AI-generated content probability at 5.7 % (IQR2.8-12.9 %) surging to a maximum of 69.9 %, with computation times spanning from 2 to 14 seconds (IQR 4-11 s). This comparison yields a statistically significant difference in median AI-generated content probability between the two groups (p = 0.005). No significant correlation was observed between word count and AI probability, as well as between article type, primarily original articles and reviews, and AI probability, indicating that AI probability is independent of these factors. CONCLUSION: The comprehensive analysis reveals significant differences and variations in AI-generated content probabilities between 2022 and 2023, indicating a growing presence of AI-generated content. However, it also illustrates that abstract length or article type does not impact the likelihood of content being AI-generated. KEY POINTS: · The study examines AI-generated content probability in scientific abstracts from Q1 journals between 2022 to 2023.. · The AI detector tool indicates an increased median AI content probability from 3.8 % to 5.7 %.. · No correlation was found between abstract length or article type and AI probability..

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management. However, data requirements, computational resources, and regulatory and ethical concerns remain challenges that need to be addressed to fully realize the potential of this technology. The fusion of photon-counting CT and deep learning algorithms is poised to revolutionize medical imaging and transform patient care.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artifical intelligence.

AIMS: To develop a deep learning model, with the aid of ChatGPT, for thyroid nodules, utilizing ultrasound images. The cytopathology of the fine needle aspiration biopsy (FNAB) serves as the baseline. MATERIAL AND METHODS: After securing IRB approval, a retrospective study was conducted, analyzing thyroid ultrasound images and FNAB results from 1,061 patients between January 2017 and January 2022. Detailed examinations of their demographic profiles, imaging characteristics, and cytological features were conducted. The images were used for training a deep learning model to identify various thyroid pathologies. ChatGPT assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. RESULTS: The model demonstrated an accuracy of 0.81 on the testing set, within a 95% confidence interval of 0.76 to 0.87. It presented remarkable results across thyroid subgroups, particularly in the benign category, with high precision (0.78) and recall (0.96), yielding a balanced F1-score of 0.86. The malignant category also displayed high precision (0.82) and recall (0.92), with an F1-score of 0.87. CONCLUSIONS: The study demonstrates the potential of artificial intelligence, particularly ChatGPT, in aiding the creation of robust deep learning models for medical image analysis.

Improving radiology workflow using ChatGPT and artificial intelligence.

Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language. Radiology is a vital aspect of modern medicine that involves the use of imaging technologies to diagnose and treat medical conditions artificial intelligence, including ChatGPT, can be integrated into radiology workflows to improve efficiency, accuracy, and patient care. ChatGPT can streamline various radiology workflow steps, including patient registration, scheduling, patient check-in, image acquisition, interpretation, and reporting. While ChatGPT has the potential to transform radiology workflows, there are limitations to the technology that must be addressed, such as the potential for bias in artificial intelligence algorithms and ethical concerns. As technology continues to advance, ChatGPT is likely to become an increasingly important tool in the field of radiology, and in healthcare more broadly.

A mimicker of inflammatory breast carcinoma: Lupus mastitis and evolving imaging features.

Rare case of lupus mastitis in a 58-year-old female with discoid lupus erythematosus presented with fever, left breast swelling, and painful palpable lesion. Accurate imaging and histopathologic evaluation allowed for appropriate management and regression of breast findings with hydroxychloroquine treatment, emphasizing the need to avoid unnecessary biopsies and surgeries.

Differentiation of pure cystic sellar lesions on magnetic resonance imaging.

BACKGROUND AND PURPOSE: Cystic pituitary adenomas and cystic craniopharyngiomas may mimic Rathke cleft cysts when there is no solid enhancing component on magnetic resonance imaging (MRI). This study aims to investigate the efficiency of MRI findings in differentiating Rathke cleft cysts from pure cystic pituitary adenoma and pure cystic craniopharyngioma. MATERIALS AND METHODS: 109 patients were included in this study (56 Rathke cleft cysts, 38 pituitary adenomas, and 15 craniopharyngiomas). Preoperative magnetic resonance images were evaluated using 9 imaging findings. These findings include intralesional fluid-fluid level, intralesional septations, midline /off-midline location, suprasellar extension, an intracystic nodule, a hypointense rim on T2-weighted images, ≥ 2 mm thickness of contrast-enhancing wall, T1 hyperintensity and T2 hypointensity. p < 0.01 was considered statistically significant. RESULTS: There was a statistically significant difference among groups for these 9 findings. Intracystic nodule and T2 hypointensity were the most specific MRI findings in differentiating Rathke cleft cyst from the others (98.1% and 100%, respectively). Intralesional septation and thick contrast-enhancing wall were the most sensitive MRI findings ruling out Rathke cleft cysts with 100% sensitivity. CONCLUSION: Rathke cleft cysts can be distinguished from pure cystic adenoma and craniopharyngioma with the presence of an intracystic nodule, T2 hypointensity, the absence of the thick contrast-enhancing wall, and absence of intralesional septations.