Correlating volumetric and linear measurements of brain metastases on MRI scans using intelligent automation software: a preliminary study.

PURPOSE: The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) working group proposed a guide for treatment responses for BMs by utilizing the longest diameter; however, despite recognizing that many patients with BMs have sub-centimeter lesions, the group referred to these lesions as unmeasurable due to issues with repeatability and interpretation. In light of RANO-BM recommendations, we aimed to correlate linear and volumetric measurements in sub-centimeter BMs on contrast-enhanced MRI using intelligent automation software. METHODS: In this retrospective study, patients with BMs scanned with MRI between January 1, 2018, and December 31, 2021, were screened. Inclusion criteria were: (1) at least one sub-centimeter BM with an integer millimeter-longest diameter was noted in the MRI report; (2) patients were a minimum of 18 years of age; (3) patients with available pre-treatment three-dimensional T1-weighted spoiled gradient-echo MRI scan. The screening was terminated when there were 20 lesions in each group. Lesion volumes were measured with the help of intelligent automation software Jazz (AI Medical, Zollikon, Switzerland) by two readers. The Kruskal-Wallis test was used to compare volumetric differences. RESULTS: Our study included 180 patients. The agreement for volumetric measurements was excellent between the two readers. The volumes of the following groups were not significantly different: 1-2 mm, 1-3 mm, 1-4 mm, 2-3 mm, 2-4 mm, 3-4 mm, 3-5 mm, 4-5 mm, 5-6 mm, 5-7 mm, 6-7 mm, 6-8 mm, 6-9 mm, 7-8 mm, 7-9 mm, 8-9 mm. CONCLUSION: Our findings indicate that the largest diameter of a lesion may not accurately represent its volume. Additional research is required to determine which method is superior for measuring radiologic response to therapy and which parameter correlates best with clinical improvement or deterioration.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models. METHODS: Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented. RESULTS: A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome. CONCLUSIONS: Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. METHODS: A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. RESULTS: We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively. CONCLUSION: AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.

Imaging Features of Primary Intracranial Sarcoma with DICER1 Mutation: A Multicenter Case Series.

Primary intracranial sarcoma, DICER1-mutant, is a rare, recently described entity in the fifth edition of the WHO Classification of CNS Tumors. Given the entity's rarity and recent description, imaging data on primary intracranial sarcoma, DICER1-mutant, remains scarce. In this multicenter case series, we present detailed multimodality imaging features of primary intracranial sarcoma, DICER1-mutant, with emphasis on the appearance of the entity on MR imaging. In total, 8 patients were included. In all 8 patients, the lesion demonstrated blood products on T1WI. In 7 patients, susceptibility-weighted imaging was obtained and demonstrated blood products. Primary intracranial sarcoma, DICER1-mutant, is a CNS neoplasm that primarily affects pediatric and young adult patients. In the present case series, we explore potential imaging findings that are helpful in suggesting this diagnosis. In younger patients, the presence of a cortical lesion with intralesional blood products on SWI and T1-weighted MR imaging, with or without extra-axial blood products, should prompt the inclusion of this entity in the differential diagnosis.

The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions.

The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.

Biomechanical Evaluation of Topographically and Tomographically Normal Fellow Eyes of Patients With Keratoconus.

PURPOSE: To determine the effectiveness of parameters and indices based on biomechanical measures at discriminating fellow eyes with topographically and tomographically normal corneas in patients with keratoconus from normal control corneas. METHODS: The study included 47 keratoconus suspect eyes, defined as the topographically and tomographically normal fellow eyes of patients with frank keratoconus in the other eye. Eyes were imaged using the Pentacam HR and Corvis ST (both Oculus Optikgeräte GmbH). Fellow eyes were then categorized as topographically/tomographically normal fellow eyes (TNF) and topographically/tomographically borderline fellow eyes (TBF). The ability of each of the Corvis Biomechanical Index (CBI), Tomographic and Biomechanical Index (TBI), stiffness parameter at applanation 1 (SP-A1), and stress-strain index (SSI) at discriminating between normal controls and keratoconus suspects was assessed. RESULTS: The TBI had the best discriminative ability with the greatest area under the receiver operating characteristic (AUROC) curve value of 0.946 for normal controls versus TBF eyes, and 0.824 for normal controls versus TNF eyes. Compared to the TBI AUROC curves, SP-A1 and CBI had AUROC curve values of 0.833 (P = .09) and 0.822 (P = .01) for normal controls versus TBF eyes, respectively, and AUROC curve values of 0.822 (P = .96) and 0.550 (P = .0002) for normal controls versus TNF eyes, respectively. The TBI had the best positive predictive value for TNF and TBF eyes, followed by CBI and SP-A1. CONCLUSIONS: The TBI and the purely biomechanical parameter SP-A1 were of moderate utility in distinguishing between normal and keratoconus suspect eyes. In the absence of topographic/tomographic evidence of keratectasia, an independently abnormal biomechanical parameter may suggest an increased risk of ectasia. [J Refract Surg. 2022;38(5):318-325.].