A Synergistic Interaction between Chk1- and MK2 Inhibitors in KRAS-Mutant Cancer.

KRAS is one of the most frequently mutated oncogenes in human cancer. Despite substantial efforts, no clinically applicable strategy has yet been developed to effectively treat KRAS-mutant tumors. Here, we perform a cell-line-based screen and identify strong synergistic interactions between cell-cycle checkpoint-abrogating Chk1- and MK2 inhibitors, specifically in KRAS- and BRAF-driven cells. Mechanistically, we show that KRAS-mutant cancer displays intrinsic genotoxic stress, leading to tonic Chk1- and MK2 activity. We demonstrate that simultaneous Chk1- and MK2 inhibition leads to mitotic catastrophe in KRAS-mutant cells. This actionable synergistic interaction is validated using xenograft models, as well as distinct Kras- or Braf-driven autochthonous murine cancer models. Lastly, we show that combined checkpoint inhibition induces apoptotic cell death in KRAS- or BRAF-mutant tumor cells directly isolated from patients. These results strongly recommend simultaneous Chk1- and MK2 inhibition as a therapeutic strategy for the treatment of KRAS- or BRAF-driven cancers.

Oncogenic role and target properties of the lysine-specific demethylase KDM1A in chronic lymphocytic leukemia.

In chronic lymphocytic leukemia (CLL), epigenetic alterations are considered to centrally shape the transcriptional signatures that drive disease evolution and underlie its biological and clinical subsets. Characterizations of epigenetic regulators, particularly histone-modifying enzymes, are very rudimentary in CLL. In efforts to establish effectors of the CLL-associated oncogene T-cell leukemia 1A (TCL1A), we identified here the lysine-specific histone demethylase KDM1A to interact with the TCL1A protein in B cells in conjunction with an increased catalytic activity of KDM1A. We demonstrate that KDM1A is upregulated in malignant B cells. Elevated KDM1A and associated gene expression signatures correlated with aggressive disease features and adverse clinical outcomes in a large prospective CLL trial cohort. Genetic Kdm1a knockdown in Eµ-TCL1A mice reduced leukemic burden and prolonged animal survival, accompanied by upregulated p53 and proapoptotic pathways. Genetic KDM1A depletion also affected milieu components (T, stromal, and monocytic cells), resulting in significant reductions in their capacity to support CLL-cell survival and proliferation. Integrated analyses of differential global transcriptomes (RNA sequencing) and H3K4me3 marks (chromatin immunoprecipitation sequencing) in Eµ-TCL1A vs iKdm1aKD;Eµ-TCL1A mice (confirmed in human CLL) implicate KDM1A as an oncogenic transcriptional repressor in CLL which alters histone methylation patterns with pronounced effects on defined cell death and motility pathways. Finally, pharmacologic KDM1A inhibition altered H3K4/9 target methylation and revealed marked anti-B-cell leukemic synergisms. Overall, we established the pathogenic role and effector networks of KDM1A in CLL via tumor-cell intrinsic mechanisms and its impacts in cells of the microenvironment. Our data also provide rationales to further investigate therapeutic KDM1A targeting in CLL.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Chronic Hyponatremia and Brain Structure and Function Before and After Treatment.

RATIONALE & OBJECTIVE: Hyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in magnetic resonance imaging (MRI)-based measures of cerebral function in patients before and after treatment for hyponatremia. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Patients with presumed chronic hyponatremia without signs of hypo- or hypervolemia treated in the emergency department of a German tertiary-care hospital. EXPOSURE: Hyponatremia (ie, plasma sodium concentration [Na+]<125mmol/L) before and after treatment leading to [Na+]>130mmol/L. OUTCOMES: Standardized neuropsychological testing (Mini-Mental State Examination, DemTect, Trail Making Test A/B, Beck Depression Inventory, Timed Up and Go) and resting-state MRI were performed before and after treatment of hyponatremia to assess total brain and white and gray matter volumes as well as neuronal activity and its synchronization. ANALYTICAL APPROACH: Changes in outcomes after treatment for hyponatremia assessed using bootstrapped confidence intervals and Cohen d statistic. Associations between parameters were assessed using correlation analyses. RESULTS: During a 3.7-year period, 26 patients were enrolled. Complete data were available for 21 patients. Mean [Na+]s were 118.4mmol/L before treatment and 135.5mmol/L after treatment. Most measures of cognition improved significantly. Comparison of MRI studies showed a decrease in brain tissue volumes, neuronal activity, and synchronization across all gray matter after normalization of [Na+]. Volume effects were particularly prominent in the hippocampus. During hyponatremia, synchronization of neuronal activity was negatively correlated with [Na+] (r=-0.836; 95% CI, -0.979 to-0.446) and cognitive function (Mini-Mental State Examination, r=-0.523; 95% CI, -0.805 to-0.069; DemTect, r=-0.744; 95% CI, -0.951 to-0.385; and Trail Making Test A, r=0.692; 95% CI, 0.255-0.922). LIMITATIONS: Small sample size, insufficient quality of several MRI scans as a result of motion artifact. CONCLUSIONS: Resolution of hyponatremia was associated with improved cognition and reductions in brain volumes and neuronal activity. Impaired cognition during hyponatremia is closely linked to increased neuronal activity rather than to tissue volumes. Furthermore, the hippocampus appears to be particularly susceptible to hyponatremia, exhibiting pronounced changes in tissue volume. PLAIN-LANGUAGE SUMMARY: Hyponatremia is a common clinical problem, and patients often present with neurologic symptoms that are at least partially reversible. This study used neuropsychological testing and magnetic resonance imaging to examine patients during and after correction of hyponatremia. Treatment led to an improvement in patients' cognition as well as a decrease in their brain volumes, spontaneous neuronal activity, and synchronized neuronal activity between remote brain regions. Volume effects were particularly prominent in the hippocampus, an area of the brain that is important for the modulation of memory. During hyponatremia, patients with the lowest sodium concentrations had the highest levels of synchronized neuronal activity and the poorest cognitive test results.

Importance of magnetic resonance imaging and prostate-specific membrane antigen PET-CT in patients treated with salvage radical prostatectomy for radiorecurrent prostate cancer.

BACKGROUND: Accurate staging and identification of optimal candidates for local salvage therapy, such as salvage radical prostatectomy (SRP), is necessary to ensure optimal care in patients with radiorecurrent prostate cancer (PCa). We aimed to analyze performance of magnetic resonance imaging (MRI) and prostate-specific membrane antigen (PSMA)-positron emission tomography (PET)/computed tomography (CT) for predicting pathologic nonorgan confined disease (pT3) and lymph node involvement (pN+) in patients treated with SRP for radiorecurrent PCa. METHODS: We retrospectively reviewed the institutional database to identify patients who underwent MRI or 68 Ga-PSMA-PET/CT before SRP for radiorecurrent PCa. The diagnostic estimates of MRI and PSMA-PET/CT for pT3 and pN+, were calculated. RESULTS: We identified 113 patients with radiorecurrent PCa who underwent preoperative MRI followed by SRP; 53 had preoperative 68 Ga-PSMA-PET/CT. For the detection of pT3 disease, the overall accuracy of MRI was 70% (95% confidence interval [CI] 61-78), sensitivity 40% (95% CI 26-55) and specificity 94% (95% CI 85-98); PSMA-PET/CT had slightly higher accuracy of 77% (95% CI 64-88), and higher sensitivity of 90% (95% CI 68-99), but lower specificity of 70% (95% CI 51-84). For pN+ disease, MRI had poor sensitivity of 14% (95% CI 3-36), specificity of 50 (95% CI 39-61) and total accuracy of 43% (95% CI 34-53); PSMA-PET/CT had an accuracy of 85% (95% CI 72-93), sensitivity of 27% (95% CI 6-61), and specificity of 100% (95% CI 92-100). CONCLUSION: In patients with radiorecurrent PCa, both, MRI, and 68 Ga-PSMA PET/CT are valuable tools for the pre-SRP staging and should be integrated into the standard workup. For lymph node metastases, 68 Ga-PSMA PET/CT is a strong rule-in test with nearly perfect specificity; in contrast MRI had a low accuracy for lymph node metastases.

German specialists treating testicular cancer follow different guidelines with resulting inconsistency in assessment of retroperitoneal lymph-node metastasis: clinical implications and possible corrective measures.

BACKGROUND: Testicular germ cell tumors (GCTs) are aggressive but highly curable tumors. To avoid over/undertreatment, reliable clinical staging of retroperitoneal lymph-node metastasis is necessary. Current clinical guidelines, in their different versions, lack specific recommendations on how to measure lymph-node metastasis. OBJECTIVE: We aimed to assess the practice patterns of German institutions frequently treating testicular cancer for measuring retroperitoneal lymph-node size. METHODS: An 8-item survey was distributed among German university hospitals and members of the German Testicular Cancer Study Group. RESULTS: In the group of urologists, 54.7% assessed retroperitoneal lymph nodes depending on their short-axis diameter (SAD) (33.3% in any plane, 21.4% in the axial plane), while 45.3% used long-axis diameter (LAD) for the assessment (42.9% in any plane, 2.4% in the axial plane). Moreover, the oncologists mainly assessed lymph-node size based on the SAD (71.4%). Specifically, 42.9% of oncologists assessed the SAD in any plane, while 28.5% measured this dimension in the axial plane. Only 28.6% of oncologists considered the LAD (14.3% in any plane, 14.3% in the axial plane). None of the oncologists and 11.9% of the urologists (n = 5) always performed an MRI for the initial assessment, while for follow-up imaging, the use increased to 36.5% of oncologists and 31% of urologists. Furthermore, only 17% of the urologists, and no oncologists, calculated lymph-node volume in their assessment (p = 0.224). CONCLUSION: Clear and consistent measurement instructions are urgently needed to be present in all guidelines across different specialistic fields involved in testicular cancer management.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701).

BACKGROUND: Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation. PURPOSE: Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting. STUDY TYPE: Prospective multi-institutional. SUBJECTS: Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions. FIELD STRENGTH/SEQUENCE: 3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values. ASSESSMENT: Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 ("V1") and visit 2 ("V2," 2-14 days after V1), where V2 comprised scans before ("V2pre") and after ("V2post") a "coffee-break" interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions. STATISTICAL TESTS: Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC). RESULTS: The ADC biases from b0 -b600 and b0 -b800 phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the individual reader wCVs were 0.148 and 0.074 (repeatability) and 0.137 and 0.078 (reproducibility). All time points demonstrated good to excellent interreader reliability for focal lesion ADC (ICCV1  = 0.89; ICCV2pre  = 0.76; ICCV2post  = 0.94). DATA CONCLUSION: This study met the QIBA claim for prostate ADC. Test-retest repeatability and multi-day reproducibility were largely equivalent. Interreader reliability for focal lesion ADC was high across time points. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2 TOC CATEGORY: Pelvis.

Robustness of dual-energy CT-derived radiomic features across three different scanner types.

OBJECTIVES: To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems. METHODS: An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Radiomic features were extracted after standardized image processing, following ROI placement in phantom tissues and healthy appearing hepatic, splenic and muscular tissue of patients using virtual monoenergetic images at 65 keV (VMI65keV) and virtual unenhanced images (VUE). In total, 774 radiomic features were extracted including 86 original features and 8 wavelet transformations hereof. Concordance correlation coefficients (CCC) and analysis of variances (ANOVA) were calculated to determine inter-scanner robustness of radiomic features with a CCC of ≥ 0.9 deeming a feature robust. RESULTS: None of the phantom-derived features attained the threshold for high feature robustness for any inter-scanner comparison. The proportion of robust features obtained from patients scanned on all three scanners was low both in VMI65keV (dsDECT vs. rsDECT:16.1% (125/774), dlDECT vs. rsDECT:2.5% (19/774), dsDECT vs. dlDECT:2.6% (20/774)) and VUE (dsDECT vs. rsDECT:11.1% (86/774), dlDECT vs. rsDECT:2.8% (22/774), dsDECT vs. dlDECT:2.7% (21/774)). The proportion of features without significant differences as per ANOVA was higher both in patients (51.4-71.1%) and in the phantom (60.6-73.4%). CONCLUSIONS: The robustness of radiomic features across different DECT scanners in patients was low and the few robust patient-derived features were not reflected in the phantom experiment. Future efforts should aim to improve the cross-platform generalizability of DECT-derived radiomics. KEY POINTS: • Inter-scanner robustness of dual-energy CT-derived radiomic features was on a low level in patients who underwent clinical examinations on three DECT platforms. • The few robust patient-derived features were not confirmed in our phantom experiment. • Limited inter-scanner robustness of dual-energy CT derived radiomic features may impact the generalizability of models built with features from one particular dual-energy CT scanner type.

The Aortic Ductus Diverticulum-Innocent Bystander or Potential Source of Thromboembolic Stroke?

OBJECTIVE: Due to reversal blood flow in the diastolic phase, outpouchings at the aortic isthmus may carry the risk of thrombus formation and subsequent thromboembolism. The objective was to evaluate the association between aortic ductus diverticula (ADDs) and ischemic brain alterations in cerebral magnetic resonance imaging. METHODS: A retrospective analysis of 218 patients who received both a dedicated computed tomography angiography of the thoracic aorta and a brain magnetic resonance imaging was performed. Two radiologists independently reviewed all examinations for the presence of ADD as well as ischemic alterations of the brain. The association between this anatomical variant and ischemic brain alterations was evaluated by univariate and bivariate logistic regression analyses. RESULTS: ADDs were identified/present in 35 of 218 patients (16%). Ischemic brain alterations were found in 57% of patients (20/35) with an ADD and in 42% of the control group (77/183, P = 0.1). The presence of an ADD did not prove to be an independent risk factor for ischemic brain alterations after multivariate adjustment (odds ratio = 1.7, 95% confidence interval = 0.72-3.96, P = 0.225). CONCLUSIONS: In the present study, ADDs were not significantly associated with ischemic brain alterations. Therefore, ADDs seem to be an innocent bystander with respect to the pathogenesis of ischemic brain alterations.

Magnetic Resonance Imaging-Guided Transurethral Ultrasound Ablation of Prostate Cancer.

PURPOSE: Magnetic resonance imaging-guided transurethral ultrasound ablation uses directional thermal ultrasound under magnetic resonance imaging thermometry feedback control for prostatic ablation. We report 12-month outcomes from a prospective multicenter trial (TACT). MATERIALS AND METHODS: A total of 115 men with favorable to intermediate risk prostate cancer across 13 centers were treated with whole gland ablation sparing the urethra and apical sphincter. The co-primary 12-month endpoints were safety and efficacy. RESULTS: In all, 72 (63%) had grade group 2 and 77 (67%) had NCCN® intermediate risk disease. Median treatment delivery time was 51 minutes with 98% (IQR 95-99) thermal coverage of target volume and spatial ablation precision of ±1.4 mm on magnetic resonance imaging thermometry. Grade 3 adverse events occurred in 9 (8%) men. The primary endpoint (U.S. Food and Drug Administration mandated) of prostate specific antigen reduction ≥75% was achieved in 110 of 115 (96%) with median prostate specific antigen reduction of 95% and nadir of 0.34 ng/ml. Median prostate volume decreased from 37 to 3 cc. Among 68 men with pretreatment grade group 2 disease, 52 (79%) were free of grade group 2 disease on 12-month biopsy. Of 111 men with 12-month biopsy data, 72 (65%) had no evidence of cancer. Erections (International Index of Erectile Function question 2 score 2 or greater) were maintained/regained in 69 of 92 (75%). Multivariate predictors of persistent grade group 2 at 12 months included intraprostatic calcifications at screening, suboptimal magnetic resonance imaging thermal coverage of target volume and a PI-RADS™ 3 or greater lesion at 12-month magnetic resonance imaging (p <0.05). CONCLUSIONS: The TACT study of magnetic resonance imaging-guided transurethral ultrasound whole gland ablation in men with localized prostate cancer demonstrated effective tissue ablation and prostate specific antigen reduction with low rates of toxicity and residual disease.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities. PURPOSE: To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. STUDY TYPE: Retrospective. POPULATION: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. ASSESSMENT: Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. STATISTICAL TESTS: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging. RESULTS: The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177). DATA CONCLUSION: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Quantitative distribution of iodinated contrast media in body computed tomography: data from a large reference cohort.

OBJECTIVES: Dual-energy computed tomography allows for an accurate and reliable quantification of iodine. However, data on physiological distribution of iodine concentration (IC) is still sparse. This study aims to establish guidance for IC in abdominal organs and important anatomical landmarks using a large cohort of individuals without radiological tumor burden. METHODS: Five hundred seventy-one oncologic, portal venous phase dual-layer spectral detector CT studies of the chest and abdomen without tumor burden at time point of imaging confirmed by > 3-month follow-up were included. ROI were placed in parenchymatous organs (n = 25), lymph nodes (n = 6), and vessels (n = 3) with a minimum of two measurements per landmark. ROI were placed on conventional images and pasted to iodine maps to retrieve absolute IC. Normalization to the abdominal aorta was conducted to obtain iodine perfusion ratios. Bivariate regression analysis, t tests, and ANOVA with Tukey-Kramer post hoc test were used for statistical analysis. RESULTS: Absolute IC showed a broad scatter and varied with body mass index, between different age groups and between the sexes in parenchymatous organs, lymph nodes, and vessels (range 0.0 ± 0.0 mg/ml-6.6 ± 1.3 mg/ml). Unlike absolute IC, iodine perfusion ratios did not show dependency on body mass index; however, significant differences between the sexes and age groups persisted, showing a tendency towards decreased perfusion ratios in elderly patients (e.g., liver 18-44 years/≥ 64 years: 0.50 ± 0.11/0.43 ± 0.10, p ≤ 0.05). CONCLUSIONS: Distribution of IC obtained from a large-scale cohort is provided. As significant differences between sexes and age groups were found, this should be taken into account when obtaining quantitative iodine concentrations and applying iodine thresholds. KEY POINTS: • Absolute iodine concentration showed a broad variation and differed between body mass index, age groups, and between the sexes in parenchymatous organs, lymph nodes, and vessels. • The iodine perfusion ratios did not show dependency on body mass index while significant differences between sexes and age groups persisted. • Provided guidance values may serve as reference when aiming to differentiate healthy and abnormal tissue based on iodine perfusion ratios.

Reduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithms.

OBJECTIVES: To evaluate the reduction of artifacts from cardiac implantable electronic devices (CIEDs) by virtual monoenergetic images (VMI), metal artifact reduction (MAR) algorithms, and their combination (VMIMAR) derived from spectral detector CT (SDCT) of the chest compared to conventional CT images (CI). METHODS: In this retrospective study, we included 34 patients (mean age 74.6 ± 8.6 years), who underwent a SDCT of the chest and had a CIED in place. CI, MAR, VMI, and VMIMAR (10 keV increment, range: 100-200 keV) were reconstructed. Mean and standard deviation of attenuation (HU) among hypo- and hyperdense artifacts adjacent to CIED generator and leads were determined using ROIs. Two radiologists qualitatively evaluated artifact reduction and diagnostic assessment of adjacent tissue. RESULTS: Compared to CI, MAR and VMIMAR ≥ 100 keV significantly increased attenuation in hypodense and significantly decreased attenuation in hyperdense artifacts at CIED generator and leads (p < 0.05). VMI ≥ 100 keV alone only significantly decreased hyperdense artifacts at the generator (p < 0.05). Qualitatively, VMI ≥ 100 keV, MAR, and VMIMAR ≥ 100 keV provided significant reduction of hyper- and hypodense artifacts resulting from the generator and improved diagnostic assessment of surrounding structures (p < 0.05). Diagnostic assessment of structures adjoining to the leads was only improved by MAR and VMIMAR 100 keV (p < 0.05), whereas keV values ≥ 140 with and without MAR significantly worsened diagnostic assessment (p < 0.05). CONCLUSIONS: The combination of VMI and MAR as well as MAR as a standalone approach provides effective reduction of artifacts from CIEDs. Still, higher keV values should be applied with caution due to a loss of soft tissue and vessel contrast along the leads. KEY POINTS: • The combination of VMI and MAR as well as MAR as a standalone approach enables effective reduction of artifacts from CIEDs. • Higher keV values of both VMI and VMIMAR at CIED leads should be applied with caution since diagnostic assessment can be hampered by a loss of soft tissue and vessel contrast. • Recommended keV values for CIED generators are between 140 and 200 keV and for leads around 100 keV.

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches. METHODS: The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma. RESULTS: The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5-10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%). CONCLUSIONS: The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

Evaluation of equivocal small cystic pancreatic lesions with spectral-detector computed tomography.

BACKGROUND: Evaluation of small cystic lesions of the pancreas remains a challenging task, as due to their size appearance can be rather hypodense than clearly fluid-filled. PURPOSE: To evaluate whether additional information provided by novel dual-layer spectral-detector computed tomography (SDCT) imaging can improve assessment of these lesions. MATERIAL AND METHODS: For this retrospective study, we reviewed reports of 1192 contrast-enhanced portal-venous phase SDCT scans of the abdomen conducted between May 2017 and January 2019. On basis of the radiological report 25 small (≤1.5 cm) cystic pancreatic lesions in 22 patients were identified, in which additional short-term follow-up imaging was recommended to confirm/clarify cystic nature. Conventional images (CI) and spectral images (SI) including virtual-monoenergetic images at 40 keV (VMI), iodine-density and iodine-overlay images were reconstructed. Two readers indicated lesion conspicuity and confidence for presence of cystic nature on three-point scales. First, solely CI were evaluated, while in a second reading after a four-week interval, the combination of CI and corresponding SI were reviewed. Quantitatively, ROI-based mean attenuation was measured in CI and VMI. RESULTS: In the subjective reading, SI significantly improved lesion conspicuity (CI 2 [1-2], SI 3 [2-3], P < 0.001) and confidence regarding presence of cystic nature (CI 2 [1-2], SI 3 [3-3], P < 0.001). Inter-observer agreement depicted by intraclass correlation coefficient improved considerably from 0.51 with only CI to 0.85 when the combination with SI was used. Further, VMI displayed significantly higher signal-to-noise (CI 1.2 ± 0.8, VMI 3.2 ± 1.8, P < 0.001) and contrast-to-noise ratios (CI 2.6 ± 0.8, VMI 4.7 ± 1.9). CONCLUSION: Compared to CI alone, combination with SI significantly improves visualization and confidence in evaluation of small equivocal cystic pancreatic lesions.

Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives.

BACKGROUND: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce. OBJECTIVE: This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable. METHODS: Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded. RESULTS: In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001). CONCLUSIONS: Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.

Maternal high-fat diet induces long-term obesity with sex-dependent metabolic programming of adipocyte differentiation, hypertrophy and dysfunction in the offspring.

Maternal obesity determines obesity and metabolic diseases in the offspring. The white adipose tissue (WAT) orchestrates metabolic pathways, and its dysfunction contributes to metabolic disorders in a sex-dependent manner. Here, we tested if sex differences influence the molecular mechanisms of metabolic programming of WAT in offspring of obese dams. To this end, maternal obesity was induced with high-fat diet (HFD) and the offspring were studied at an early phase [postnatal day 21 (P21)], a late phase (P70) and finally P120. In the early phase we found a sex-independent increase in WAT in offspring of obese dams using magnetic resonance imaging (MRI), which was more pronounced in females than males. While the adipocyte size increased in both sexes, the distribution of WAT differed in males and females. As mechanistic hints, we identified an inflammatory response in females and a senescence-associated reduction in the preadipocyte factor DLK in males. In the late phase, the obese body composition persisted in both sexes, with a partial reversal in females. Moreover, female offspring recovered completely from both the adipocyte hypertrophy and the inflammatory response. These findings were linked to a dysregulation of lipolytic, adipogenic and stemness-related markers as well as AMPKα and Akt signaling. Finally, the sex-dependent metabolic programming persisted with sex-specific differences in adipocyte size until P120. In conclusion, we do not only provide new insights into the molecular mechanisms of sex-dependent metabolic programming of WAT dysfunction, but also highlight the sex-dependent development of low- and high-grade pathogenic obesity.

COVID-19 complicated by parainfluenza co-infection in a patient with chronic lymphocytic leukemia.

The number of people suffering from the new coronavirus SARS-CoV-2 continues to rise. In SARS-CoV-2, superinfection with bacteria or fungi seems to be associated with increased mortality. The role of co-infections with respiratory viral pathogens has not yet been clarified. Here, we report the course of COVID-19 in a CLL patient with secondary immunodeficiency and viral co-infection with parainfluenza.