A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.ABBREVIATIONS: BTIP = Brain Tumor Imaging Protocol; CE = Contrast-Enhancing; CNS = Central Nervous System; CR = Complete Response; ECOG = Eastern Cooperative Oncology Group; HGG = High-Grade Glioma; IDH = Isocitrate Dehydrogenase; IRF = Independent Radiologic Facility; LGG = Low-Grade Glioma; KPS = Karnofsky Performance Status; MR = Minor Response; mRANO = Modified RANO; NANO = Neurological Assessment in Neuro-Oncology; ORR = Objective Response Rate; OS = Overall Survival; PD = Progressive Disease; PFS = Progression-Free Survival; PR = Partial Response; PsP = Pseudoprogression; RANO = Response Assessment in Neuro-Oncology; RECIST = Response Evaluation Criteria In Solid Tumors; RT = Radiation Therapy; SD = Stable Disease; Tx = Treatment.

Endoscopic Endonasal Transsphenoidal Resection of Pituitary Adenomas in Patients Presenting With Monocular Blindness.

BACKGROUND AND OBJECTIVES: Suprasellar tumors, particularly pituitary adenomas (PAs), commonly present with visual decline, and the endoscopic endonasal transsphenoidal approach (EETA) is the primary management for optic apparatus decompression. Patients presenting with complete preoperative monocular blindness comprise a high-risk subgroup, given concern for complete blindness. This retrospective cohort study evaluates outcomes after EETA for patients with PA presenting with monocular blindness. METHODS: Retrospective analysis of all EETA cases at our institution from June 2012 to August 2023 was performed. Inclusion criteria included adults with confirmed PA and complete monocular blindness, defined as no light perception, and a relative afferent pupillary defect secondary to tumor mass effect. RESULTS: Our cohort includes 15 patients (9 males, 6 females), comprising 2.4% of the overall PA cohort screened. The mean tumor diameter was 3.8 cm, with 6 being giant PAs (>4 cm). The mean duration of preoperative monocular blindness was 568 days. Additional symptoms included contralateral visual field defects (n = 11) and headaches (n = 10). Two patients presented with subacute PA apoplexy. Gross total resection was achieved in 46% of patients, reflecting tumor size and invasiveness. Postoperatively, 2 patients experienced improvement in their effectively blind eye and 2 had improved visual fields of the contralateral eye. Those with improvements were operated within 10 days of presentation, and no patients experienced worsened vision. CONCLUSION: This is the first series of EETA outcomes in patients with higher-risk PA with monocular blindness on presentation. In these extensive lesions, vision remained stable for most without further decline and improvement from monocular blindness was observed in a small subset of patients with no light perception and relative afferent pupillary defect. Timing from vision loss to surgical intervention seemed to be associated with improvement. From a surgical perspective, caution is warranted to protect remaining vision and we conclude that EETA is safe in the management of these patients.

Structural and practical identifiability of contrast transport models for DCE-MRI.

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Structural and practical identifiability of contrast transport models for DCE-MRI.

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.