Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas

BACKGROUND AND PURPOSE

Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types.

MATERIALS AND METHODS

Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio.

RESULTS

The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively.

CONCLUSIONS

In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.

A dose-response meta-analysis to evaluate the relationship between high-density lipoprotein cholesterol and all-cause and cardiovascular disease mortality

PURPOSE

Previous studies have not fully described the relationship between high-density lipoprotein cholesterol (HDL-C) and death risks from all cause and cardiovascular disease (CVD). This study quantitatively evaluates HDL-C-mortality associations.

METHODS

Embase and PubMed databases were searched for relevant articles published up to 1 June 2019. Random-effects models were used to pool relative risks (RRs) and 95% confidence intervals (CIs). We used restricted cubic splines to model the dose-response association.

RESULTS

We identified 32 prospective cohort studies including 369,904 participants and 33,473 total deaths (9426 CVD deaths). Compared to the lowest HDL-C levels, all cause and CVD mortality risks were reduced by 18% (RR 0.82; 95% CI, 0.73-0.93) and 36% (0.64, 0.46-0.89), respectively, for the highest HDL-C levels. All cause and CVD mortality risks were reduced by 15% (0.85, 0.79-0.92) and 23% (0.77, 0.69-0.87), respectively, with each 1 mmol/L increment of HDL-C. We found evidence of nonlinear and negative dose-response associations of HDL-C with all cause and CVD mortality (P_nonlinearity < 0.001), and the lowest death risks from all cause and CVD were observed at approximately 1.34 and 1.55 mmol/L, respectively.

CONCLUSION

HDL-C is inversely associated with all cause and CVD mortality risks under approximately 2.05 and 2.33 mmol/L, respectively. Optimal doses require investigation via clinical practice or high-quality research.

Examining the role of COVID-19 testing availability on intention to isolate: A Randomized hypothetical scenario

Background

Little information exists on how COVID-19 testing influences intentions to engage in risky behavior. Understanding the behavioral effects of diagnostic testing may highlight the role of adequate testing on controlling viral transmission. In order to evaluate these effects, simulated scenarios were conducted evaluating participant intentions to self-isolate based on COVID-19 diagnostic testing availability and results.

Methods

Participants from the United States were recruited through an online survey platform (Amazon Mechanical Turk) and randomized to one of three hypothetical scenarios. Each scenario asked participants to imagine having symptoms consistent with COVID-19 along with a clinical diagnosis from their physician. However, scenarios differed in either testing availability (testing available v. unavailable) or testing result (positive v. negative test). The primary outcome was intention to engage in high-risk COVID-19 behaviors, measured using an 11-item mean score (range 1–7) that was pre-registered prior to data collection. Multi-variable linear regression was used to compare the mean composite scores between conditions. The randomized survey was conducted between July 23^rd to July 29^th, 2020.

Results

A total of 1400 participants were recruited through a national, online, opt-in survey. Out of 1194 respondents (41.6% male, 58.4% female) with a median age of 38.5 years, participants who had no testing available in their clinical scenario showed significantly greater intentions to engage in behavior facilitating COVID-19 transmission compared to those who received a positive confirmatory test result scenario (mean absolute difference (SE): 0.14 (0.06), P = 0.016), equating to an 11.1% increase in mean score risky behavior intentions. Intention to engage in behaviors that can spread COVID-19 were also positively associated with male gender, poor health status, and Republican party affiliation.

Conclusion

Testing availability appears to play an independent role in influencing behaviors facilitating COVID-19 transmission. Such findings shed light on the possible negative externalities of testing unavailability.

Trial registration

Effect of Availability of COVID-19 Testing on Choice to Isolate and Socially Distance, NCT04459520, https://clinicaltrials.gov/ct2/show/NCT04459520.

Patient Navigation After Positive Fecal Immunochemical Test Results Increases Diagnostic Colonoscopy and Highlights Multilevel Barriers to Follow-Up

BACKGROUND

The fecal immunochemical test (FIT) is a common colorectal cancer screening modality in the USA but often is not followed by diagnostic colonoscopy.

AIMS

We investigated the efficacy of patient navigation to increase diagnostic colonoscopy after positive FIT results and determined persistent barriers to follow-up despite navigation in a large, academic healthcare system.

METHODS

The study cohort included all health system outpatients with an assigned primary care provider, a positive FIT result between 12/01/2016 and 06/01/2019, and no documentation of colonoscopy after positive FIT. Two non-clinical patient navigators engaged patients and providers to encourage follow-up, offer solutions to barriers, and assist with colonoscopy scheduling. The primary intervention endpoint was completion of colonoscopy within 6 months of navigation. We documented reasons for persistent barriers to colonoscopy despite navigation and determined predictors of successful follow-up after navigation.

RESULTS

There were 119 patients who received intervention. Of these, 37 (31.1%) patients completed colonoscopy at 6 months. In 41/119 (34.5%) cases, the PCP did not recommend colonoscopy, most commonly due to a normal colonoscopy prior to the positive FIT (19, 46.3%). There were 41/119 patients (34.5%) that declined colonoscopy despite the patient navigator and the PCP order. Male sex and younger age were significant predictors of follow-up (aOR = 2.91, 95%CI, 1.18-7.13; aOR = 0.92, 95%CI, 0.87-0.99).

CONCLUSIONS

After implementation of patient navigation, diagnostic colonoscopy was completed for 31.1% of patients with a positive FIT result. However, navigation also highlighted persistent multilevel barriers to follow-up. Future work will develop targeted solutions for these barriers to further increase FIT follow-up rates in our health system.

The Association of Broadband Internet Access and Telemedicine Utilization in rural Western Tennessee: an observational study

Background

Broadband access has been highlighted as a national policy priority to improve access to care in rural communities.

Objective

To determine whether broadband internet availability was associated with telemedicine adoption among a rural patient population in western Tennessee.

Methods

Observational study using electronic medical record data from March 13th, 2019 to March 13th, 2021. Multivariable logistic regression incorporating individual-level characteristics with broadband availability, income, educational attainment, and primary care physician supply at the zip code level, and rural status as determined at the county level.

Setting

Single health system in western Tennessee.

Participants

Adult patients with one or more in-person or remote encounter in a health system in western Tennessee and residing in western Tennessee between March 13th, 2019 and March 13th, 2021 (N = 54,688).

Outcome measures

Completion of one or more video encounters in the year following March 13th, 2020 (N = 3199; 7%). Our primary characteristic of interest was the proportion of residents in each zip code with access to the internet meeting the Federal Communications Commission definition of broadband access, adjusting for age, gender, race, income, educational attainment, insurance type, rural status, and primary care provider supply.

Results

Patients in a rural western Tennessee health system were predominantly white (79%), residing in rural zip codes (73%) with median household incomes ($52,085) less than state and national averages. Patients residing in a zip code where there is 80 to 100% broadband access compared to 0 to 20% were more likely in the year following March 13th, 2020 to have completed both telemedicine and in-person visits ([OR; 95% CI] 1.57; 1.29, 1.94), completed only telemedicine visits (2.26; 1.71, 2.97), less likely to have only completed in-person visits (0.81; 0.74, 0.89), but no more or less likely to have accessed no care (1.07; 0.97, 1.18).

Discussion

The availability of broadband internet was shown to be one of many factors associated with the utilization of telemedicine for a rural, working-class community after March 13th, 2020.

Conclusions

Access to broadband internet is a determinant of access to telemedicine for patients in rural communities and should be a priority for policymakers interested in improving health and access to care for rural patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06746-0.

Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma

BACKGROUND AND PURPOSE

Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes.

MATERIALS AND METHODS

We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features.

RESULTS

From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI.

CONCLUSIONS

Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.

Adherence to antihypertensive medications for secondary prevention of cardiovascular disease events: a dose-response meta-analysis

OBJECTIVES

The aim of the study was to explore the association between adherence to antihypertensive medications (AHMs) and the risk of recurrence of cardiovascular disease (CVD) events in patients with a history of CVD events from cohort studies.

STUDY DESIGN

This is a dose-response meta-analysis.

METHODS

PubMed and Embase databases were searched up to March 4, 2021, to identify English-language reports of cohort studies that assessed the association of AHM adherence with risk of recurrence of CVD events. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were estimated by using a fixed- or random-effects model. Restricted cubic splines were used to evaluate the possible linear or non-linear association.

RESULTS

We included nine cohort studies (54,349 patients) in the present meta-analysis. The pooled RR of CVD events was 0.66 (95% CI, 0.54-0.78) for the highest versus lowest AHM adherence category. We did not find any evidence of non-linearity association between AHM adherence and risk of CVD events (P_{non-linearity} = 0.534); for patients with a history of CVD events, the risk of CVD events was reduced by 9% for each 20% increase in AHM adherence (RR, 0.91; 95% CI, 0.85-0.97). The results of sensitivity analysis and subgroup analysis were virtually unchanged.

CONCLUSIONS

The high level of adherence to AHM is an effective strategy for preventing recurrence of CVD events. Patients with a history of CVD events should adhere to AHM.

Bone marrow-derived mesenchymal stem cells inhibit CD8+ T cell immune responses via PD-1/PD-L1 pathway in multiple myeloma

High expression of the inhibitory receptor programmed cell death ligand 1 (PD-L1) on tumor cells and tumor stromal cells have been found to play a key role in tumor immune evasion in several human malignancies. However, the expression of PD-L1 on bone marrow mesenchymal stem cells (BMSCs) and whether the programmed cell death 1 (PD-1)/PD-L1 signal pathway is involved in the BMSCs versus T cell immune response in multiple myeloma (MM) remains poorly defined. In this study, we explored the expression of PD-L1 on BMSCs from newly diagnosed MM (NDMM) patients and the role of PD-1/PD-L1 pathway in BMSC-mediated regulation of CD8⁺ T cells. The data showed that the expression of PD-L1 on BMSCs in NDMM patients was significantly increased compared to that in normal controls (NC) (18·81 ± 1·61 versus 2·78± 0·70%; P < 0·001). Furthermore, the PD-1 expression on CD8⁺ T cells with NDMM patients was significantly higher than that in normal controls (43·22 ± 2·98 versus 20·71 ± 1·08%; P < 0·001). However, there was no significant difference in PD-1 expression of CD4⁺ T cells and natural killer (NK) cells between the NDMM and NC groups. Additionally, the co-culture assays revealed that BMSCs significantly suppressed CD8⁺ T cell function. However, the PD-L1 inhibitor effectively reversed BMSC-mediated suppression in CD8⁺ T cells. We also found that the combination of PD-L1 inhibitor and pomalidomide can further enhance the killing effect of CD8⁺ T cells on MM cells. In summary, our findings demonstrated that BMSCs in patients with MM may induce apoptosis of CD8⁺ T cells through the PD-1/PD-L1 axis and inhibit the release of perforin and granzyme B from CD8⁺ T cells to promote the immune escape of MM.

Transcriptional analysis identifies potential biomarkers and molecular regulators in acute malaria infection

AIMS

Malaria is a serious health threat in tropical countries. The causative parasite of Malaria tropica, the severe form, is the protozoan Plasmodium falciparum. In humans, it infects red blood cells, compromising blood flow and tissue perfusion. This study aims to identify potential biomarkers and RNA networks in leukocyte transcriptomes from patients suffering from Malaria tropica.

MATERIALS AND METHODS

We identified differentially regulated mRNAs and microRNAs in peripheral blood leukocytes of healthy donors and Malaria patients. Genes whose expression changes were not attributable to changes in leukocyte composition were used for bioinformatics analysis and network construction. Using a previously published cohort of community-acquired pneumonia (CAP) patients, we established discriminating transcriptomic features versus Malaria. We aimed to establish differences between the patient groups by principal component (PCA) and receiving operator characteristic (ROC) analyses and in silico cell type deconvolution.

KEY FINDINGS

We found 870 genes that were significantly differentially expressed between healthy donors and Malaria patients. E2F1, BIRC5 and CCNB1 were identified to be primarily responsible for PCA separation of these two groups. We searched for biological function and found that cell cycle processes were strongly activated. By in silico cell type deconvolution, we attribute this to an expansion of γδ T cells. Additional discrimination between CAP and Malaria yielded 445 differentially expressed genes, among which immune proteasome transcripts PSMB8, PSMB9 and PSMB10 were significantly induced in Malaria.

SIGNIFICANCE

We identified transcripts from patient leukocytes that differentiate between healthy, Malaria and CAP, and indicate a biological context with potential pathophysiological relevance.

Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes

BACKGROUND AND PURPOSE

Artificial intelligence-based computer-aided diagnostic systems have been introduced for thyroid cancer diagnosis. Our aim was to compare the diagnostic performance of a commercially available computer-aided diagnostic system and radiologist-based assessment for the detection of thyroid cancer based on the Thyroid Imaging Reporting and Data Systems (TIRADS) and dichotomous outcomes.

MATERIALS AND METHODS

In total, 372 consecutive patients with 454 thyroid nodules were enrolled. The computer-aided diagnostic system was set up to render a possible diagnosis in 2 formats, the Korean Society of Thyroid Radiology (K)-TIRADS and the American Thyroid Association (ATA)-TIRADS-classifications, and dichotomous outcomes (possibly benign or possibly malignant).

RESULTS

The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the computer-aided diagnostic system for thyroid cancer were, respectively, 97.6%, 21.6%, 42.0%, 93.9%, and 49.6% for K-TIRADS; 94.6%, 29.6%, 43.9%, 90.4%, and 53.5% for ATA-TIRADS; and 81.4%, 81.9%, 72.3%, 88.3%, and 81.7% for dichotomous outcomes. The sensitivities of the computer-aided diagnostic system did not differ significantly from those of the radiologist (all P > .05); the specificities and accuracies were significantly lower than those of the radiologist (all P < .001). Unnecessary fine-needle aspiration rates were lower for the dichotomous outcome characterizations, particularly for those performed by the radiologist. The interobserver agreement for the description of K-TIRADS and ATA-TIRADS classifications was fair-to-moderate, but the dichotomous outcomes were in substantial agreement.

CONCLUSIONS

The diagnostic performance of the computer-aided diagnostic system varies in terms of TIRADS classification and dichotomous outcomes and relative to radiologist-based assessments. Clinicians should know about the strengths and weaknesses associated with the diagnosis of thyroid cancer using computer-aided diagnostic systems.

Exogenous 6-benzyladenine application affects root morphology by altering hormone status and gene expression of developing lateral roots in Malus hupehensis

Malus hupehensis is an extensively used apple rootstock in China. In the current study, M. hupehensis seedlings were treated with exogenous 2.2 µm 6-benzyladenine (6-BA) so as to investigate the mechanism by which 6-BA affects lateral root development. The results indicate that 6-BA treatment promotes elongation and thickening of both root and shoot in M. hupehensis, but reduces the number of lateral roots, as well as reducing the auxin level after 6-BA treatment. Moreover, MhAHK4, MhRR1 and MhRR2 were also significantly up-regulated in response to 6-BA treatment. Expression levels of auxin synthesis- and transport-related genes, such as MhYUCCA6, MhYUCCA10, MhPIN1 and MhPIN2, were down-regulated, which corresponds with lower auxin levels in the 6-BA-treated seedlings. A negative regulator of auxin, MhIAA3, was induced by 6-BA treatment, leading to reduced expression of MhARF7 and MhARF19 in 6-BA-treated seedlings. As a result, expression of MhWOX11, MhWOX5, MhLBD16 and MhLBD29 was blocked, which in turn inhibited lateral root initiation. In addition, a lower auxin level decreased expression of MhRR7 and MhRR15, which repressed expression of key transcription factors associated with root development, thus inhibiting lateral root development. In contrast, 6-BA treatment promoted secondary growth (thickening) of the root by inducing expression of MhCYCD3;1 and MhCYCD3;2. Collectively, the changes in hormone levels and gene expression resulted in a reduced number of lateral roots and thicker roots in 6-BA-treated plants.

Downregulation of lncRNA SNHG7 inhibits proliferation and invasion of nasopharyngeal carcinoma cells through repressing ROCK1

OBJECTIVE

Recent studies have revealed the important role of long noncoding RNAs (lncRNAs) in the progression of tumorigenesis. This study aimed to identify the biological function of lncRNA small nucleolar RNA host gene 7 (SNHG7) in the progression of nasopharyngeal carcinoma (NPC).

PATIENTS AND METHODS

LncRNA SNHG7 expressions in NPC cell lines and 50 paired NPC tissue samples were detected by Real-time quantitative polymerase chain reaction (RT-qPCR). Transwell assay, wound healing assay and proliferation assay were conducted to evaluate the in vitro function of SNHG7 in NPC cells. Xenograft model was established for determining the in vivo effect of SNHG7 on tumor formation and metastasis of NPC. The underlying mechanism of SNHG7 in mediating the progression of NPC was explored by RT-qPCR and Western blot.

RESULTS

SNHG7 expression was remarkably downregulated in NPC tissues compared with that in adjacent normal samples. Knockdown of SNHG7 attenuated proliferation, invasion and migration of NPC cells. Moreover, tumor size and the number of metastatic nodules were reduced in mice administrated with NPC cells transfected with sh-SNHG7. Knockdown of SNHG7 downregulated ROCK1 at mRNA and protein level. Besides, the expression of ROCK1 in tumor tissues was positively correlated to SNHG7 expression.

CONCLUSIONS

Knockdown of SNHG7 inhibits migration, invasion and proliferation of NPC cells through downregulating ROCK1, which may offer a new therapeutic intervention for NPC patients.

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study

BACKGROUND AND PURPOSE

Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification.

MATERIALS AND METHODS

The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists.

RESULTS

Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F₁ score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F₁ score were higher than those of 2 of the 4 radiologists.

CONCLUSIONS

We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.

Downregulation of lncRNA SNHG7 inhibits proliferation and invasion of nasopharyngeal carcinoma cells through repressing ROCK1

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "Downregulation of lncRNA SNHG7 inhibits proliferation and invasion of nasopharyngeal carcinoma cells through repressing ROCK1, by L. Wang, T. Xu, X. Cui, M. Han, L.-H. Zhou, Z.-X. Wei, Z.-J. Xu, Y. Jiang, published in Eur Rev Med Pharmacol Sci 2019; 23 (14): 6186-6193-DOI: 10.26355/eurrev_201907_18432-PMID: 31364118" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/18432.

Transcriptional analysis identifies potential biomarkers and molecular regulators in pneumonia and COPD exacerbation

Lower respiratory infections, such as community-acquired pneumonia (CAP), and chronic obstructive pulmonary disease (COPD) rank among the most frequent causes of death worldwide. Improved diagnostics and profound pathophysiological insights are urgent clinical needs. In our cohort, we analysed transcriptional networks of peripheral blood mononuclear cells (PBMCs) to identify central regulators and potential biomarkers. We investigated the mRNA- and miRNA-transcriptome of PBMCs of healthy subjects and patients suffering from CAP or AECOPD by microarray and Taqman Low Density Array. Genes that correlated with PBMC composition were eliminated, and remaining differentially expressed genes were grouped into modules. One selected module (120 genes) was particularly suitable to discriminate AECOPD and CAP and most notably contained a subset of five biologically relevant mRNAs that differentiated between CAP and AECOPD with an AUC of 86.1%. Likewise, we identified several microRNAs, e.g. miR-545-3p and miR-519c-3p, which separated AECOPD and CAP. We furthermore retrieved an integrated network of differentially regulated mRNAs and microRNAs and identified HNF4A, MCC and MUC1 as central network regulators or most important discriminatory markers. In summary, transcriptional analysis retrieved potential biomarkers and central molecular features of CAP and AECOPD.