The validation of low-dose CT scans from the [18F]-FDG PET-CT scan to assess skeletal muscle mass in comparison with diagnostic neck CT scans.

PURPOSE: Radiologically defined sarcopenia, or a low skeletal muscle index (SMI), is an emerging biomarker for adverse clinical outcomes in head and neck cancer (HNC) patients. Recently, SMI measurements have been validated at the level of the third cervical vertebra (C3) on diagnostic neck CT scans but are not yet validated on low-dose (LD) neck CT scans from the [18F]-FDG PET-CT. This hampers SMI analysis in HNC patients without a diagnostic neck CT but with a [18F]-FDG PET-CT scan. Therefore, the aim was to study whether (low) SMI based on LD CT scan from [18F]-FDG PET-CT is comparable to those derived from diagnostic neck CT scans. METHODS: HNC patients with both diagnostic CT and [18F]-FDG PET-CT of the neck were prospectively included into the OncoLifeS data-biobank. Skeletal muscle was retrospectively delineated at the level of the third cervical vertebra (C3), and (low) SMI (cm2/m2) was calculated for diagnostic and LD neck CTs. (Low) SMI from the diagnostic neck CT was considered the reference standard. Intra-class correlation coefficient (ICC), Bland-Altman plots, and Cohen's Kappa analysis were performed. RESULTS: The cohort (n = 233) mean age was 66.2 ± 12.8 years, and 74.2% of patients were male. Inter-rater reliability was excellent (ICC > 0.990, 95% confidence interval 0.975-0.996, p < 0.001). The agreement of SMI between both modalities was high according to the Bland-Altman plot (mean ΔSMI = - 0.19 cm2/m2), and there was no substantial bias. Cohen's Kappa analysis showed an almost perfect agreement of low SMI between the two modalities (κ = 0.911, p < 0.001). The position of arms didn't affect the high agreement of (low) SMI. CONCLUSION: Skeletal muscle mass, as measured with (low) SMI, remains constant irrespective of CT acquisition parameters (diagnostic neck CT scans versus LD neck scans of the [18F]-FDG PET-CT scan), positioning of arms, and observers. These findings contribute to the construction of a clinically useful radiological biomarker for SMI and therefore identify patients at risk for adverse clinical outcomes.

Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility.

OBJECTIVES: This study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries. METHODS: A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed "focused view CTA") that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5 = completely visible, diagnostically sufficient; 4 = nearly completely visible, diagnostically sufficient; 3 = incompletely visible, barely diagnostically sufficient; 2 = hardly visible, diagnostically insufficient; 1 = not visible, diagnostically insufficient. RESULTS: At focused view CTA, median scores for the aortic arch, subclavian arteries, common carotid arteries, C1, C6, and C7 segments of the internal carotid arteries, V4 segment of the vertebral arteries, basilar artery, cerebellum including cerebellar arteries, cerebrum including cerebral arteries, and dural venous sinuses, were all 4. Median scores for the C2 to C5 segments of the internal carotid arteries, and V1 to V3 segments of the vertebral arteries ranged between 3 and 2. At unmodified CTA, median score for all above-mentioned anatomic structures was 5, which was significantly higher (p < 0.0001) than that at focused view CTA. CONCLUSION: Focused view CTA shows promise for the selective visualization of stroke-related arteries. Further improvements should focus on more accurately visualizing the smaller and tortuous internal carotid and vertebral artery segments close to bone. CLINICAL RELEVANCE: Focused view CTA may speed up image interpretation time for LVO detection and may potentially be used as a tool to study the clinical relevance of incidental findings in future prospective long-term follow-up studies. KEY POINTS: • A deep learning-based algorithm ("focused view CTA") was developed to selectively visualize relevant structures for acute ischemic stroke evaluation at CTA. • The elimination of unrequested anatomic background information was complete in all cases. • Focused view CTA may be used to study the clinical relevance of incidental findings.

Skeletal muscle mass and sarcopenia can be determined with 1.5-T and 3-T neck MRI scans, in the event that no neck CT scan is performed.

OBJECTIVES: Cross-sectional area (CSA) measurements of the neck musculature at the level of third cervical vertebra (C3) on CT scans are used to diagnose radiological sarcopenia, which is related to multiple adverse outcomes in head and neck cancer (HNC) patients. Alternatively, these assessments are performed with neck MRI, which has not been validated so far. For that, the objective was to evaluate whether skeletal muscle mass and sarcopenia can be assessed on neck MRI scans. METHODS: HNC patients were included between November 2014 and November 2018 from a prospective data-biobank. CSAs of the neck musculature at the C3 level were measured on CT (n = 125) and MRI neck scans (n = 92 on 1.5-T, n = 33 on 3-T). Measurements were converted into skeletal muscle index (SMI), and sarcopenia was defined (SMI < 43.2 cm2/m2). Pearson correlation coefficients, Bland-Altman plots, McNemar test, Cohen's kappa coefficients, and interclass correlation coefficients (ICCs) were estimated. RESULTS: CT and MRI correlated highly on CSA and SMI (r = 0.958-0.998, p < 0.001). The Bland-Altman plots showed a nihil mean ΔSMI (- 0.13-0.44 cm2/m2). There was no significant difference between CT and MRI in diagnosing sarcopenia (McNemar, p = 0.5-1.0). Agreement on sarcopenia diagnosis was good with κ = 0.956-0.978 and κ = 0.870-0.933, for 1.5-T and 3-T respectively. Observer ICCs in MRI were excellent. In general, T2-weighted images had the best correlation and agreement with CT. CONCLUSIONS: Skeletal muscle mass and sarcopenia can interchangeably be assessed on CT and 1.5-T and 3-T MRI neck scans. This allows future clinical outcome assessment during treatment irrespective of used modality. KEY POINTS: • Screening for low amount of skeletal muscle mass is usually measured on neck CT scans and is highly clinical relevant as it is related to multiple adverse outcomes in head and neck cancer patients. • We found that skeletal muscle mass and sarcopenia determined on CT and 1.5-T and 3-T MRI neck scans at the C3 level can be used interchangeably. • When CT imaging of the neck is missing for skeletal muscle mass analysis, patients can be assessed with 1.5-T or 3-T neck MRIs.

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist.

OBJECTIVES: To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists. METHODS: The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test. RESULTS: There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful. CONCLUSION: Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization. CRITICAL RELEVANCE STATEMENT: The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care. KEY POINTS: • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.

A new regulatory switch in a JAK protein kinase.

Members of the JAK family of protein kinases mediate signal transduction from cytokine receptors to transcription factor activation. Over-stimulation of these pathways is causative in immune disorders like rheumatoid arthritis, psoriasis, lupus, and Crohn's disease. A search for selective inhibitors of a JAK kinase has led to our characterization of a previously unknown kinase conformation arising from presentation of Tyr962 of TYK2 to an inhibitory small molecule via an H-bonding interaction. A small minority of protein kinase domains has a Tyrosine residue in this position within the αC-ß4 loop, and it is the only amino acid commonly seen here with H-bonding potential. These discoveries will aid design of inhibitors that discriminate among the JAK family and more widely among protein kinases.

Drug-resistant aurora A mutants for cellular target validation of the small molecule kinase inhibitors MLN8054 and MLN8237.

The Aurora kinases regulate multiple aspects of mitotic progression, and their overexpression in diverse tumor types makes them appealing oncology targets. An intensive research effort over the past decade has led to the discovery of chemically distinct families of small molecule Aurora kinase inhibitors, many of which have demonstrated therapeutic potential in model systems. These agents are also important tools to help dissect signaling pathways that are orchestrated by Aurora kinases, and the antiproliferative target of pan-Aurora inhibitors such as VX-680 has been validated using chemical genetic techniques. In many cases the nonspecific nature of Aurora inhibitors toward unrelated kinases is well established, potentially broadening the spectrum of cancers to which these compounds might be applied. However, unambiguously demonstrating the molecular target(s) for clinical kinase inhibitors is an important challenge, one that is absolutely critical for deciphering the molecular basis of compound specificity, resistance, and efficacy. In this paper, we have investigated amino acid requirements for Aurora A sensitivity to the benzazepine-based Aurora inhibitor MLN8054 and the close analogue MLN8237, a second-generation compound that is in phase II clinical trials. A crystallographic analysis facilitated the design and biochemical investigation of a panel of resistant Aurora A mutants, a subset of which were then selected as candidate drug-resistance targets for further evaluation. Using inducible human cell lines, we show that cells expressing near-physiological levels of a functional but partially drug-resistant Aurora A T217D mutant survive in the presence of MLN8054 or MLN8237, authenticating Aurora A as a critical antiproliferative target of these compounds.