Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.

OBJECTIVES: To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. METHODS: 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. RESULTS: Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). CONCLUSION: Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. KEY POINTS: • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

Magnetic resonance fingerprinting: an overview.

Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.

PURPOSE: This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort. METHODS: Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests. RESULTS: Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05). CONCLUSION: Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.

Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard.

Artificial intelligence (AI) has recently become a trending tool and topic regarding productivity especially with publicly available free services such as ChatGPT and Bard. In this report, we investigate if two widely available chatbots chatGPT and Bard, are able to show consistent accurate responses for the best imaging modality for urologic clinical situations and if they are in line with American College of Radiology (ACR) Appropriateness Criteria (AC). All clinical scenarios provided by the ACR were inputted into ChatGPT and Bard with result compared to the ACR AC and recorded. Both chatbots had an appropriate imaging modality rate of of 62% and no significant difference in proportion of correct imaging modality was found overall between the two services (p>0.05). The results of our study found that both ChatGPT and Bard are similar in their ability to suggest the most appropriate imaging modality in a variety of urologic scenarios based on ACR AC criteria. Nonetheless, both chatbots lack consistent accuracy and further development is necessary for implementation in clinical settings. For proper use of these AI services in clinical decision making, further developments are needed to improve the workflow of physicians.

From guidelines to radiology practice: navigating the 2023 ASCO guidelines for advanced gastroesophageal cancer and beyond.

The American Society of Clinical Oncology (ASCO) updated the guidelines for the treatment of advanced gastroesophageal (GE) cancer in 2023, signifying a major shift towards targeted therapeutics and precision medicine. This article serves as an imaging-based review of recent developments in the care of patients with GE cancer. We cover the epidemiology, the developing treatment paradigms, and the imaging assessment of GE malignancy. In addition, this review aims to familiarize radiologists with the unique adverse effects pertaining to therapeutics, surgeries, radiation therapies, and associated imaging corollaries. A case-based approach will be used to both explore the efficacy of modern treatments and demonstrate their adverse effects, such as chemotherapy-associated pneumonitis, radiation esophagitis, and anastomotic failure. With this comprehensive exploration of gastroesophageal cancer, radiologists will be equipped with the essential tools to inform the treatment decisions made by medical oncologists, radiation oncologists, and surgical oncologists in the new era of precision medicine.

Sex-Specific Differences in Patients with IDH1-Wild-Type Grade 4 Glioma in the ReSPOND Consortium.

BACKGROUND AND PURPOSE: Understanding sex-based differences in patients with glioblastoma is necessary for accurate personalized treatment planning to improve patient outcomes. Our purpose was to investigate sex-specific differences in molecular, clinical, and radiologic tumor parameters, as well as survival outcomes in patients with glioblastoma, isocitrate dehydrogenase-1 wild-type (IDH1-WT), grade 4. MATERIALS AND METHODS: Retrospective data of 1832 patients with glioblastoma, IDH1-WT with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as age, molecular parameters, preoperative Karnofsky performance score (KPS), tumor volumes, epicenter, and laterality were assessed through nonparametric tests. Spatial atlases were generated by using preoperative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. RESULTS: Glioblastoma was diagnosed at a median age of 64 years in women compared with 61.9 years in men (false discovery rate [FDR] = 0.003). Men had a higher KPS (above 80) as compared with women (60.4% women versus 69.7% men, FDR = 0.044). Women had lower tumor volumes in enhancing (16.7 cm3 versus 20.6 cm3 in men, FDR = 0.001), necrotic core (6.18 cm3 versus 7.76 cm3 in men, FDR = 0.001), and edema regions (46.9 cm3 versus 59.2 cm3 in men, FDR = 0.0001). The right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in men. There were no sex-specific differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNA-methyltransferase promoter and undergoing subtotal resection increased the mortality risk in both men and women. CONCLUSIONS: Our study demonstrates significant sex-based differences in clinical and radiologic tumor parameters of patients with glioblastoma. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for men and women.

Radiology Reading Room for the Future: Harnessing the Power of Large Language Models Like ChatGPT.

Radiology has usually been the field of medicine that has been at the forefront of technological advances, often being the first to wholeheartedly embrace them. Whether it's from digitization to cloud side architecture, radiology has led the way for adopting the latest advances. With the advent of large language models (LLMs), especially with the unprecedented explosion of freely available ChatGPT, time is ripe for radiology and radiologists to find novel ways to use the technology to improve their workflow. Towards this, we believe these LLMs have a key role in the radiology reading room not only to expedite processes, simplify mundane and archaic tasks, but also to increase the radiologist's and radiologist trainee's knowledge base at a far faster pace. In this article, we discuss some of the ways we believe ChatGPT, and the likes can be harnessed in the reading room.