Christian Federau

Background: Intravoxel incoherent motion (IVIM) analysis of diffusion‐weighted MRI (DWI) provides microvascular perfusion and diffusion information. However, parameter estimation is limited by noise sensitivity, variability across fitting methods, and lack of standardization. Deep‐learning (DL)–based approaches, particularly spatially‐aware transformers, may improve robustness, but their clinical utility remains unexplored. Purpose: To evaluate conventional, Bayesian, and DL–based IVIM fitting methods in glioma patients, focusing on tumor grading accuracy. Study Type: Retrospective. Population: Fractal‐noise‐based simulations and preoperative DWI from 20 glioma patients (5 Grade‐2, 3 Grade‐3, 12 Grade‐4). Sequence: Spin‐echo echo‐planar DWI, 16 b values (0–900 s/mm ² ), three orthogonal directions, 3 T. Assessment: IVIM parameter maps were compared across least squares (LSQ), segmented (SEG), Bayesian shrinkage (BSP), and spatial‐homogeneity (FBM) priors, and DL–based methods, including IVIM‐NET, novel spatially‐aware transformers (NATTEN‐17), and a refined version (SA‐17). Simulation accuracy was evaluated using median absolute percentage error (MDAPE) and bias using median percentage error. In vivo data were visually assessed by the authors for noise suppression and structural preservation. Whole‐tumor diffusion coefficient ( D ), pseudo‐diffusion coefficient ( D *), and signal fraction ( f ) values were evaluated across tumor grades and for differentiating Grade‐4 from Grade‐2/3 tumors. Statistical Tests: Mann–Whitney U tests for group comparisons; tumor grading performance using receiver operating characteristic–area under the curve (ROC‐AUC), and pairwise AUC differences using the DeLong test. Significance: p < 0.05. Results: Transformer‐based methods achieved superior simulation accuracy, with significantly lower MDAPE for f and D * than all other approaches: NATTEN‐17 (5.91%, 13.31%), SA‐17 (7.73%, 13.66%), LSQ (21.95%, 54.34%), SEG (17.10%, 21.27%), BSP (12.35%, 22.79%), FBM (16.32%, 20.67%). In vivo, they provided superior visual quality and tumor delineation in f and D * maps, producing seemingly denoised versions of LSQ, while preserving tumor heterogeneity. The spatially‐aware transformers yielded consistently the highest ROC‐AUC values, particularly for D * (SA‐17: 0.78), significantly outperforming LSQ (0.62), SEG (0.58), FBM (0.62), and IVIM‐NET (0.71). Data Conclusion: Transformer‐based model fitting has the potential to provide clinically valuable IVIM parameter estimates and improved tumor grading accuracy. Evidence Level: 3. Technical Efficacy: Stage 2.

With the rapid dissemination of artificial intelligence acceleration tools for MRI, individual radiologists and health systems should critically and objectively evaluate the effectiveness of their use in clinical practice.

Purpose: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions. Methods: In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. Results: A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08). Conclusion: The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice. In this article, we seek to address three major topics: medical malpractice liability, regulation of AI-based medical devices, and privacy protection in shared medical imaging data, thereby focusing on the legal frameworks of the European Union and the USA. As many of the presented concepts are very complex and, in part, remain yet unsolved, this article is not meant to be comprehensive but rather thought-provoking. The goal is to engage clinical neuroradiologists in the debate and equip them to actively shape these topics in the future.

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.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.

Introduction: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions Methods: In three separate centers, 117 MS follow-up MRIs were blindly analyzed on FLuid Attenuated Inversion Recovery (FLAIR), pre- and post-Gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. Results: A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33sec ± 1min0sec per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa=0.5) and slight for SELs (0.08). Discussion: The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using a dedicated software such as Jazz.

Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technical specifications of these applications, notably as commercial tools very rarely provide full details. The clinical neuroradiologist is thus confronted with the increasing dilemma to base clinical decisions on the output of AI tools without knowing in detail what is happening inside the “black box” of those AI applications. This dilemma is aggravated by the fact that currently, no established and generally accepted rules exist concerning best clinical practice and scientific and clinical validation nor for the medico-legal consequences in cases of wrong diagnoses. The current review article provides a practical checklist of essential points, intended to aid the user to identify and double-check necessary aspects, although we are aware that not all this information may be readily available at this stage, even for certified and commercially available AI tools. Furthermore, we therefore suggest that the developers of AI applications provide this information.