The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement.

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

Lipid Metabolism Pathway Genes and Lung Cancer: ACADSB rs12220683G>C Is Associated with Better Survival Outcome in Patients with Non-Small Cell Lung Cancer.

INTRODUCTION: Altered lipid metabolism has been reported to be associated with prognosis in multiple cancers. This study aimed to investigate the association of polymorphisms in lipid metabolism pathway genes with survival outcomes in patients with surgically resected non-small cell lung cancer (NSCLC). METHODS: In total, 744 patients with surgically resected NSCLC (380 in the discovery cohort and 364 in the validation cohort) were included in this study. The association between 176 polymorphisms of lipid metabolism pathway genes and the clinical outcomes of NSCLC patients was analyzed. RESULTS: Among the polymorphisms investigated, ACADSB rs10902859G>A was associated with significantly better overall survival (OS) in the discovery, validation, and combined cohorts. ACADSB rs10902859G>A was located in the repressed region and had strong linkage disequilibrium (D' = 1.00 and r2 = 0.94), with rs12220683G>C located in the H3K4me3 peak region, which indicates the presence of active promoters. ACADSB rs12220683G>C was also associated with better OS in the discovery, validation, and combined cohorts (in a dominant model; adjusted hazard ratio [aHR] = 0.53, 95% confidence interval [CI] = 0.30-0.94, p = 0.03; aHR = 0.37, 95% CI = 0.15-0.89, p = 0.03; and aHR = 0.47, 95% CI = 0.29-0.75, p = 0.002, respectively). In vitro luciferase assay demonstrated that the promoter activity of ACADSB was significantly increased in the rs12220683 variant C allele compared with that in the wild G allele (p = 3 × 10-5). CONCLUSION: These results suggest that ACADSB rs12220683G>C increases promoter activity and that increased ACADSB expression may result in better OS in patients with surgically resected NSCLC.

The effect of inhaler prescription on the development of lung cancer in COPD: a nationwide population-based study.

BACKGROUND: COPD is associated with the development of lung cancer. A protective effect of inhaled corticosteroids (ICS) on lung cancer is still controversial. Hence, this study investigated the development of lung cancer according to inhaler prescription and comorbidties in COPD. METHODS: A retrospective cohort study was conducted based on the Korean Health Insurance Review and Assessment Service database. The development of lung cancer was investigated from the index date to December 31, 2020. This cohort included COPD patients (≥ 40 years) with new prescription of inhalers. Patients with a previous history of any cancer during screening period or a switch of inhaler after the index date were excluded. RESULTS: Of the 63,442 eligible patients, 39,588 patients (62.4%) were in the long-acting muscarinic antagonist (LAMA) and long-acting ß2-agonist (LABA) group, 22,718 (35.8%) in the ICS/LABA group, and 1,136 (1.8%) in the LABA group. Multivariate analysis showed no significant difference in the development of lung cancer according to inhaler prescription. Multivariate analysis, adjusted for age, sex, and significant factors in the univariate analysis, demonstrated that diffuse interstitial lung disease (DILD) (HR = 2.68; 95%CI = 1.86-3.85), a higher Charlson Comorbidity Index score (HR = 1.05; 95%CI = 1.01-1.08), and two or more hospitalizations during screening period (HR = 1.19; 95%CI = 1.01-1.39), along with older age and male sex, were independently associated with the development of lung cancer. CONCLUSION: Our data suggest that the development of lung cancer is not independently associated with inhaler prescription, but with coexisting DILD, a higher Charlson Comorbidity Index score, and frequent hospitalization.

Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.

OBJECTIVES: We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs. METHODS: The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated. RESULTS: Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk. CONCLUSION: DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs. CLINICAL RELEVANCE STATEMENT: For patients with newly diagnosed brain metastasis, deep learning-based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes. KEY POINTS: • Deep learning-based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.

Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.

OBJECTIVES: The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. METHODS: To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). RESULTS: Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). CONCLUSION: Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. CLINICAL RELEVANCE STATEMENT: By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. KEY POINTS: • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.

Clinical evaluation of noninvasive prenatal testing for sex chromosome aneuploidies in 9,176 Korean pregnant women: a single-center retrospective study.

BACKGROUND: To evaluate the clinical significance of noninvasive prenatal testing (NIPT) for detecting fetal sex chromosome aneuploidies (SCAs) in Korean pregnant women. METHODS: We retrospectively analyzed NIPT data from 9,176 women with singleton pregnancies referred to the CHA Biotech genome diagnostics center. Cell-free fetal DNA (cffDNA) was extracted from maternal peripheral blood, and high-throughput massively parallel sequencing was conducted. Subsequently, the positive NIPT results for SCA were validated via karyotype and chromosomal microarray analyses. RESULTS: Overall, 46 cases were SCA positive after NIPT, including 20, 12, 8, and 6 for Turner, triple X, Klinefelter, and Jacob syndromes, respectively. Among 37 women with invasive prenatal diagnosis, 19 had true positive NIPT results. The overall positive predictive value (PPV) of NIPT for detecting SCAs was 51.35%. The PPV was 18.75% for Turner, 88.89% for triple X, 71.43% for Klinefelter, and 60.00% for Jacob's syndromes. NIPT accuracy for detecting sex chromosome trisomies was higher than that for sex chromosome monosomy (P = 0.002). No significant correlation was observed between fetal SCA incidence and maternal age (P = 0.914), except for the borderline significance of Jacob's syndrome (P = 0.048). No significant differences were observed when comparing NIPT and karyotyping validation for fetal SCA according to pregnancy characteristics. CONCLUSION: Our data suggest that NIPT can reliably screen for SCAs, and it performed better in predicting sex chromosome trisomies compared with monosomy X. No correlation was observed between maternal age and fetal SCA incidence, and no association was observed between different pregnancy characteristics. The accuracy of these findings requires improvements; however, our study provides an important reference for clinical genetic counseling and further management. Larger scale studies, considering confounding factors, are required for accurate evaluation.

Pectoralis Muscle Area as a Predictor of Mortality in Patients Hospitalized with Bronchiectasis Exacerbation.

INTRODUCTION: Data on factors related to mortality in patients with bronchiectasis exacerbation are insufficient. Computed tomography (CT) can measure the pectoralis muscle area (PMA) and is a useful tool to diagnose sarcopenia. This study aimed to evaluate whether PMA can predict mortality in patients with bronchiectasis exacerbation. METHODS: Patients hospitalized due to bronchiectasis exacerbation at a single center were retrospectively divided into survivors and non-survivors based on 1-year mortality. Thereafter, a comparison of the clinical and radiologic characteristics was conducted between the two groups. RESULTS: A total of 66 (14%) patients died at 1 year. In the multivariate analysis, age, BMI <18.4 kg/m2, sex-specific PMA quartile, ≥3 exacerbations in the previous year, serum albumin <3.5 g/dL, cystic bronchiectasis, tuberculosis-destroyed lung, and diabetes mellitus were independent predictors for the 1-year mortality in patients hospitalized with bronchiectasis exacerbation. A lower PMA was associated with a lower overall survival rate in the survival analysis according to sex-specific quartiles of PMA. PMA had the highest area under the curve during assessment of prognostic performance in predicting the 1-year mortality. The lowest sex-specific PMA quartile group exhibited higher disease severity than the highest quartile group. CONCLUSIONS: CT-derived PMA was an independent predictor of 1-year mortality in patients hospitalized with bronchiectasis exacerbation. Patients with lower PMA exhibited higher disease severity. These findings suggest that PMA might be a useful marker for providing additional information regarding prognosis of patients with bronchiectasis exacerbation.

Toxicology and safety study of L-tryptophan and its impurities for use in broiler feed.

L-tryptophan has been utilized as a feed additive in animal nutrition to improve growth performance, as well as a dietary supplement to alleviate various emotional symptoms in humans. Despite its benefits, concerns regarding its safety arose following the outbreak of eosinophilia-myalgia syndrome (EMS) among individuals who consumed L-tryptophan. The causative material of EMS was determined to be not L-tryptophan itself, but rather L-tryptophan impurities resulting from a specific manufacturing process. To investigate the effect of L-tryptophan and its impurities on humans who consume meat products derived from animals that were fed L-tryptophan and its impurities, an animal study involving broiler chickens was conducted. The animals in test groups were fed diet containing 0.065%-0.073% of L-tryptophan for 27 days. This study aimed to observe the occurrence of toxicological or EMS-related symptoms and analyze the residues of L-tryptophan impurities in meat products. The results indicated that there was no evidence of adverse effects associated with the test substance in the investigated parameters. Furthermore, most of the consumed EMS-causing L-tryptophan impurities did not remain in the meat of broiler chickens. Thus, this study demonstrated the safety of L-tryptophan and some of its impurities as a feed additive.

Toxicology and safety study of L-tryptophan and its impurities for use in swine.

L-tryptophan, an essential amino acid for physiological processes, metabolism, development, and growth of organisms, is widely utilized in animal nutrition and human health as a feed additive and nutritional supplement, respectively. Despite its known benefits, safety concerns have arisen due to an eosinophilia-myalgia syndrome (EMS) outbreak linked to L-tryptophan consumed by humans. Extensive research has established that the EMS outbreak was caused by an L-tryptophan product that contained certain impurities. Therefore, safety validations are imperative to endorse the use of L-tryptophan as a supplement or a feed additive. This study was conducted in tertiary hybrid [(Landrace × Yorkshire) × Duroc] pigs to assess general toxicity and potential risks for EMS-related symptoms associated with L-tryptophan used as a feed additive. Our investigation elucidated the relationship between L-tryptophan and EMS in swine. No mortalities or clinical signs were observed in any animals during the administration period, and the test substance did not induce toxic effects. Hematological analysis and histopathological examination revealed no changes in EMS-related parameters, such as eosinophil counts, lung lesions, skin lesions, or muscle atrophy. Furthermore, no test substance-related changes occurred in other general toxicological parameters. Through analyzing the tissues and organs of swine, most of the L-tryptophan impurities that may cause EMS were not retained. Based on these findings, we concluded that incorporating L-tryptophan and its impurities into the diet does not induce EMS in swine. Consequently, L-tryptophan may be used as a feed additive throughout all growth stages of swine without safety concerns.

Novel Amidine Derivative K1586 Sensitizes Colorectal Cancer Cells to Ionizing Radiation by Inducing Chk1 Instability.

Checkpoint kinase 1 (Chk1) is a key mediator of the DNA damage response that regulates cell cycle progression, DNA damage repair, and DNA replication. Small-molecule Chk1 inhibitors sensitize cancer cells to genotoxic agents and have shown preclinical activity as single agents in cancers characterized by high levels of replication stress. However, the underlying genetic determinants of Chk1-inhibitor sensitivity remain unclear. Although treatment options for advanced colorectal cancer are limited, radiotherapy is effective. Here, we report that exposure to a novel amidine derivative, K1586, leads to an initial reduction in the proliferative potential of colorectal cancer cells. Cell cycle analysis revealed that the length of the G2/M phase increased with K1586 exposure as a result of Chk1 instability. Exposure to K1586 enhanced the degradation of Chk1 in a time- and dose-dependent manner, increasing replication stress and sensitizing colorectal cancer cells to radiation. Taken together, the results suggest that a novel amidine derivative may have potential as a radiotherapy-sensitization agent that targets Chk1.

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis.

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.

Proteomic analysis predicts anti-angiogenic resistance in recurred glioblastoma.

BACKGROUND: Recurrence is common in glioblastoma multiforme (GBM) because of the infiltrative, residual cells in the tumor margin. Standard therapy for GBM consists of surgical resection followed by chemotherapy and radiotherapy, but the median survival of GBM patients remains poor (~ 1.5 years). For recurrent GBM, anti-angiogenic treatment is one of the common treatment approaches. However, current anti-angiogenic treatment modalities are not satisfactory because of the resistance to anti-angiogenic agents in some patients. Therefore, we sought to identify novel prognostic biomarkers that can predict the therapeutic response to anti-angiogenic agents in patients with recurrent glioblastoma. METHODS: We selected patients with recurrent GBM who were treated with anti-angiogenic agents and classified them into responders and non-responders to anti-angiogenic therapy. Then, we performed proteomic analysis using liquid-chromatography mass spectrometry (LC-MS) with formalin-fixed paraffin-embedded (FFPE) tissues obtained from surgical specimens. We conducted a gene-ontology (GO) analysis based on protein abundance in the responder and non-responder groups. Based on the LC-MS and GO analysis results, we identified potential predictive biomarkers for anti-angiogenic therapy and validated them in recurrent glioblastoma patients. RESULTS: In the mass spectrometry-based approach, 4957 unique proteins were quantified with high confidence across clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic patterns (n = 269 proteins) between responders and non-responders. The GO term enrichment analysis revealed a cluster of genes related to immune cell-related pathways (e.g., TMEM173, FADD, CD99) in the responder group, whereas the non-responder group had a high expression of genes related to nuclear replisome (POLD) and damaged DNA binding (ERCC2). Immunohistochemistry of these biomarkers showed that the expression levels of TMEM173 and FADD were significantly associated with the overall survival and progression-free survival of patients with recurrent GBM. CONCLUSIONS: The candidate biomarkers identified in our protein analysis may be useful for predicting the clinical response to anti-angiogenic agents in patients with recurred GBM.

Genetic Variants in Histone Modification Regions Predict Clinical Outcomes of Pemetrexed Chemotherapy in Lung Adenocarcinoma.

OBJECTIVE: This study was conducted to investigate the association between genetic variants in histone modification regions and clinical outcomes of PEM chemotherapy in patients with lung adenocarcinoma. METHODS: Potentially functional SNPs were selected using integrated analysis of ChIP-seq and RNA-seq. The associations of 279 SNPs with chemotherapy response and overall survival (OS) were analyzed in 314 lung adenocarcinoma patients who underwent PEM chemotherapy. RESULTS: Among the SNPs investigated, 18 were significantly associated with response to chemotherapy, while 28 with OS. Of these SNPs, rs549794A>G in an enhancer which is expected to regulate the expression of ribosomal protein S3 (RPS3) gene was significantly associated with both worse response to chemotherapy and worse OS (adjusted odds ratio = 0.59, 95% CI = 0.36-0.97, p = 0.04; adjusted hazard ratio = 1.44, 95% CI = 1.09-1.91, p = 0.01, respectively). Previous studies suggested that RPS3, a multi-functional protein with various extraribosomal activities, may play a role in chemotherapy resistance. Therefore, it is postulated that rs549794-induced change in the expression level of RPS3 may affect the response to PEM chemotherapy and consequently the survival outcomes in lung adenocarcinoma patients. CONCLUSION: This study suggests that genetic variants in the histone modification regions may be useful for the prediction of clinical outcomes of PEM chemotherapy in advanced lung adenocarcinoma.

Enhancing the interferon-γ release assay through omission of nil and mitogen values.

PURPOSE: To address the limited utility of the interferon (IFN)-γ release assay (IGRA) caused by its variability and inconsistency. METHODS: This retrospective cohort study was based on data obtained between 2011 and 2019. QuantiFERON-TB Gold-In-Tube was used to measure IFN-γ levels in nil, tuberculosis (TB) antigen, and mitogen tubes. RESULTS: Of 9,378 cases, 431 had active TB. The non-TB group comprised 1,513 IGRA-positive, 7,202 IGRA-negative, and 232 IGRA-indeterminate cases. Nil-tube IFN-γ levels were significantly higher in the active TB group (median = 0.18 IU/mL; interquartile range: 0.09-0.45 IU/mL) than in the IGRA-positive non-TB (0.11 IU/mL; 0.06-0.23 IU/mL) and IGRA-negative non-TB (0.09 IU/mL; 0.05-0.15 IU/mL) groups (P < 0.0001). From receiver operating characteristic analysis, TB antigen tube IFN-γ levels had higher diagnostic utility for active TB than TB antigen minus nil values. In a logistic regression analysis, active TB was the main driver of higher nil values. In the active TB group, after reclassifying the results based on a TB antigen tube IFN-γ level of 0.48 IU/mL, 14/36 cases with negative results and 15/19 cases with indeterminate results became positive, while 1/376 cases with positive results became negative. Overall, the sensitivity for detecting active TB improved from 87.2 to 93.7%. CONCLUSION: The results of our comprehensive assessment can aid in IGRA interpretation. Since nil values are governed by TB infection rather than reflecting background noise, TB antigen tube IFN-γ levels should be used without subtracting nil values. Despite indeterminate results, TB antigen tube IFN-γ levels can be informative.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors.

The fifth edition of the World Health Organization classification of central nervous system tumors published in 2021 reflects the current transitional state between traditional classification system based on histopathology and the state-of-the-art molecular diagnostics. This Part 3 Review focuses on the molecular diagnostics and imaging findings of glioneuronal and neuronal tumors. Histological and molecular features in glioneuronal and neuronal tumors often overlap with pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas (discussed in the Part 2 Review). Due to this overlap, in several tumor types of glioneuronal and neuronal tumors the diagnosis may be inconclusive with histopathology and genetic alterations, and imaging features may be helpful to distinguish difficult cases. Thus, it is crucial for radiologists to understand the underlying molecular diagnostics as well as imaging findings for application on clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Concomitant induction of SLIT3 and microRNA-218-2 in macrophages by toll-like receptor 4 activation limits osteoclast commitment.

BACKGROUND: Toll-like receptor 4 (TLR4) conducts a highly regulated inflammatory process by limiting the extent of inflammation to avoid toxicity and tissue damage, even in bone tissues. Thus, it is plausible that strategies for the maintenance of normal bone-immunity to prevent undesirable bone damage by TLR4 activation can exist, but direct evidence is still lacking. METHODS: Osteoclast precursors (OCPs) obtained from WT or Slit3-deficient mice were differentiated into osteoclast (OC) with macrophage colony-stimulating factor (M-CSF), RANK ligand (RANKL) and lipopolysaccharide (LPS) by determining the number of TRAP-positive multinuclear cells (TRAP+ MNCs). To determine the alteration of OCPs population, fluorescence-activated cell sorting (FACS) was conducted in bone marrow cells in mice after LPS injection. The severity of bone loss in LPS injected WT or Slit3-deficient mice was evaluated by micro-CT analysis. RESULT: We demonstrate that TLR4 activation by LPS inhibits OC commitment by inducing the concomitant expression of miR-218-2-3p and its host gene, Slit3, in mouse OCPs. TLR4 activation by LPS induced SLIT3 and its receptor ROBO1 in BMMs, and this SLIT3-ROBO1 axis hinders RANKL-induced OC differentiation by switching the protein levels of C/EBP-ß isoforms. A deficiency of SLIT3 resulted in increased RANKL-induced OC differentiation, and the elevated expression of OC marker genes including Pu.1, Nfatc1, and Ctsk. Notably, Slit3-deficient mice showed expanded OCP populations in the bone marrow. We also found that miR-218-2 was concomitantly induced with SLIT3 expression after LPS treatment, and that this miRNA directly suppressed Tnfrsf11a (RANK) expression at both gene and protein levels, linking it to a decrease in OC differentiation. An endogenous miR-218-2 block rescued the expression of RANK and subsequent OC formation in LPS-stimulated OCPs. Aligned with these results, SLIT3-deficient mice displayed increased OC formation and reduced bone density after LPS challenge. CONCLUSION: Our findings suggest that the TLR4-dependent concomitant induction of Slit3 and miR-218-2 targets RANK in OCPs to restrain OC commitment, thereby avoiding an uncoordinated loss of bone through inflammatory processes. These observations provide a mechanistic explanation for the role of TLR4 in controlling the commitment phase of OC differentiation. Video Abstract.

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.

OBJECTIVES: To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL). MATERIALS AND METHODS: DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists. RESULTS: Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913). CONCLUSION: The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists. CLINICAL RELEVANCE STATEMENT: The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed. KEY POINTS: •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.

Vessel size and perfusion-derived vascular habitat refines prediction of treatment failure to bevacizumab in recurrent glioblastomas: validation in a prospective cohort.

OBJECTIVES: Anti-angiogenic therapy may not benefit all patients with recurrent glioblastomas, and imaging biomarker predicting treatment response to anti-angiogenic therapy is currently limited. We aimed to develop and validate vascular habitats based on perfusion and vessel size to predict time to progression (TTP) in patients with recurrent glioblastomas treated with bevacizumab. METHODS: Sixty-nine patients with recurrent glioblastomas treated with bevacizumab who underwent pretreatment MRI with dynamic susceptibility contrast imaging and vessel architectural imaging were enrolled. Vascular habitats were constructed using vessel size index (VSI) and relative cerebral blood volume (rCBV). Associations with vascular habitats and TTP were analyzed using Cox proportional hazard regression analysis. In a prospectively enrolled validation cohort consisting of 15 patients ( ClinicalTrials.gov identifier; NCT04143425), stratification of TTP was demonstrated by the Kaplan-Meier method (log-rank test) using vascular habitats. RESULTS: Three vascular habitats consisting of high, intermediate, and low angiogenic habitats were identified with rCBV and VSI. Both high angiogenic and intermediate angiogenic habitats were significantly associated with a shorter TTP (hazard ratio [HR], 2.78 and 1.82, respectively; largest p = .003) and so was rCBV (HR, 2.15; p = .02). Concordance probability index of vascular habitat combining high and intermediate angiogenic habitats was 0.74. Vascular habitats stratified patients as good or poor responder in a prospective cohort (p = .059). CONCLUSIONS: Perfusion- and vessel size-derived vascular habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy and aided patient stratification in a prospective validation cohort. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04143425 KEY POINTS: • High and intermediate angiogenic habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy. • Vascular habitats combining high and intermediate angiogenic habitats aided patient stratification for anti-angiogenic therapy in recurrent glioblastomas.

Defining subventricular zone involvement to predict the survival of patients in isocitrate dehydrogenase-wild type glioblastoma: validation in a prospective registry.

OBJECTIVES: The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. METHODS: This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. RESULTS: SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94-0.99; p = .011) and definition 2 (HR: 0.96, 0.94-0.98, p < .001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94-0.99, p = .008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735-0.738 vs. 0.687-0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). CONCLUSION: SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. KEY POINTS: • Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry.