Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

Side experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957-0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862-0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study

OBJECTIVES

To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma.

METHODS

Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve.

RESULTS

The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909-0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice.

CONCLUSIONS

T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

Atypical presentation of rotational vertebral artery insufficiency: illustrative case

BACKGROUND

Rotational vertebral artery insufficiency (RVAI), also known as bow hunter’s syndrome, is an uncommon cause of vertebrobasilar insufficiency that leads to signs of posterior circulation ischemia during head rotation. RVAI can be subdivided on the basis of the anatomical location of vertebral artery compression into atlantoaxial RVAI (pathology at C1-C2) or subaxial RVAI (pathology below C2). Typically, RVAI is only seen with contralateral vertebral artery pathologies, such as atherosclerosis, hypoplasia, or morphological atypia.

OBSERVATIONS

The authors present a unique case of atlantoaxial RVAI due to rotational instability, causing marked subluxation of the C1-C2 facet joints. This case is unique in both the mechanism of compression and the lack of contralateral vertebral artery pathology. The patient was successfully treated with posterior C1-C2 instrumentation and fusion.

LESSONS

When evaluating patients for RVAI, neurosurgeons should be aware of the variety of pathological causes, including rotational instability from facet joint subluxation. Due to the heterogeneous nature of the pathologies causing RVAI, care must be taken to decide if conservative management or surgical correction is the right course of action. Because of this heterogeneous nature, there is no set guideline for the treatment or management of RVAI.

Antimicrobial activity and cytotoxicity trait of a bioactive peptide purified from Lactococcus garvieae subsp. bovis BSN307T

A bioactive peptide of 8595 Da was purified from the cell free supernatant of Lactococcus garvieae subsp. bovis BSN307^T . MALDI MS/MS peptide mapping and the data base search displayed no significant similarity to any reported antimicrobial peptide of LAB. This peptide at a dose concentration of 200 µg ml^-1 inhibited the growth of both Gram-positive and Gram-negative bacteria by 58-89% and a dose of 500 µg ml^-1 scavenged 50% of DPPH-free radicals generated. Interestingly, cytotoxicity assay demonstrated that 17 µg ml^-1 of peptide selectively inhibited 50% proliferation of mammalian cancer cell lines HeLa and MCF-7 whereas normal H9c2 cells remained unaffected. Fluorescent microscopic analysis after DAPI nuclear staining of HeLa cells showed characteristics of apoptosis and activation of caspase-3 was ascertained by caspase-3 fluorescence assay.

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

OBJECTIVE

Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis.

METHODS

We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance.

RESULTS

The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4.

CONCLUSIONS

First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

SVEP1 is important for morphogenesis of lymphatic system: Possible implications in lymphedema

SVEP1, also known as Polydom, is a large extracellular mosaic protein with functions in protein interactions and adhesion. Since Svep1 knockout animals show severe edema and lymphatic system malformations, the aim of this study is to evaluate the presence of SVEP1 variants in patients with lymphedema. We analyzed DNA from 246 lymphedema patients for variants in known lymphedema genes, 235 of whom tested negative and underwent a second testing for new candidate genes, including SVEP1, as reported here. We found three samples with rare heterozygous missense single-nucleotide variants in the SVEP1 gene. In one family, healthy members were found to carry the same variants and reported some subclinical edema. Based on our findings and a review of the literature, we propose SVEP1 as a candidate gene that should be sequenced in patients with lymphatic malformations, with or without lymphedema, in order to investigate and add evidence on its possible involvement in the development of lymphedema.

Bartonella osteomyelitis versus vertebral sarcoidosis: A tale of two cases

BACKGROUND

Osteomyelitis is an uncommon manifestation of Bartonella henselae. Similarly, bony involvement may occur with sarcoidosis. Even though these are pathologically distinct entities, they can have overlapping imaging manifestations and therefore mimic one another. This is further complicated by the fact that both entities show non-caseating granulomatous inflammation on histopathology. We present two cases with similar imaging findings, with one case eventually diagnosed as Bartonella osteomyelitis, while the other proved to be vertebral sarcoidosis. Both patients exhibited vertebral involvement in common, and improved clinically and radiographically following antibiotics and steroids treatment, respectively. Given the overlapping pathological and imaging manifestations, and the non-specific clinical presentation, these entities may be considered in the differential consideration of each other. The presence of associated findings in such cases may be helpful.

Virtual modeling and interactive virtual reality display of unusual high-riding cervical aortic arch

A cervical aortic arch is a rare vascular malformation that is characterized as a high positioned aortic arch, above the clavicle. The knowledge of its branching pattern is essential to characterize the entity further accurately. Noninvasive cross-sectional imaging, including computed tomography angiography or magnetic resonance angiography, is the imaging methods of choice. Due to highly complex anatomy, three-dimensional (3D) images help in providing improved anatomical visualization. Virtual reality is a relatively new computer-generated simulation technique that allows the interactive display of high-resolution models using a wearable headset and interactive controllers. We describe a rare form of a cervical arch and briefly discuss the latest methods of improved visualization using 3D virtual reality displays and smartphones.

Factors Affecting Radiation Dose in Computed Tomography Angiograms for Pulmonary Embolism: A Retrospective Cohort Study

Objectives

Computed tomography pulmonary angiogram (CTPA) is one of the most commonly ordered and frequently overused tests. The purpose of this study was to evaluate the mean radiation dose to patients getting CTPA and to identify factors that are associated with higher dose.

Material and Methods

This institutionally approved retrospective study included all patients who had a CTPA to rule out acute pulmonary embolism between 2016 and 2018 in a tertiary care center. Patient data (age, sex, body mass index [BMI], and patient location), CT scanner type, image reconstruction methodology, and radiation dose parameters (dose-length product [DLP]) were recorded. Effective dose estimates were obtained by multiplying DLP by conversion coefficient (0.014 mSv•mGy^-1•cm^-1). Multivariate logistic regression analysis was performed to determine the factors affecting the radiation dose.

Results

There were 2342 patients (1099 men and 1243 women) with a mean age of 58.1 years (range 0.2-104.4 years) and BMI of 31.3 kg/m² (range 12-91.5 kg/m²). The mean effective radiation dose was 5.512 mSv (median - 4.27 mSv; range 0.1-43.0 mSv). Patient factors, including BMI >25 kg/m², male sex, age >18 years, and intensive care unit (ICU) location, were associated with significantly higher dose (P < 0.05). CT scanning using third generation dual-source scanner with model-based iterative reconstruction (IR) had significantly lower dose (mean: 4.90 mSv) versus single-source (64-slice) scanner with filtered back projection (mean: 9.29 mSv, P < 0.001).

Conclusion

Patients with high BMI and ICU referrals are associated with high CT radiation dose. They are most likely to benefit by scanning on newer generation scanner using advance model-based IR techniques.

REVIEW OF THE FUNCTION OF SEMA3A IN LYMPHATIC VESSEL MATURATION AND ITS POTENTIAL AS A CANDIDATE GENE FOR LYMPHEDEMA: ANALYSIS OF THREE FAMILIES WITH RARE CAUSATIVE VARIANTS

SEMA3A is a semaphorin involved in cell signaling with PlexinA1 and Neuropilin-1 (NRP1) receptors and it is responsible for recruiting dendritic cells into lymphatics. Mutations in the SEMA3A gene result in abnormalities in lymphatic vessel development and maturation. We investigated the association of SEMA3A variants detected in lymphedema patients with lymphatic maturation and lymphatic system malfunction. First, we used NGS technology to sequence the SEMA3A gene in 235 lymphedema patients who carry wild type alleles for known lymphedema genes. We detected three different missense variants in three families. Bioinformatic results showed that some protein interactions could be altered by these variants. Other unaffected family members of the probands also reported different episodes of subclinical edema. We then evaluated the importance of the SEMA3A gene in the formation and maturation of lymphatic vessels. Our results determined that SEMA3A variants segregate in families with lymphatic system malformations and recommend the inclusion of SEMA3A in the gene panel for testing of patients with lymphedema.

Atretic Double Aortic Arch: Imaging Appearance of a Rare Anomaly and Differentiation From Its Mimics

A double aortic arch (DAA) with atresia is an uncommon cause of a symptomatic vascular ring resulting in trachea-esophageal compression. An atretic double aortic arch can resemble the right aortic arch with a mirror image branching pattern or the right arch with an aberrant left subclavian artery depending upon the level of atresia. The double aortic arch with atresia is difficult to detect on pre-surgical computed tomography angiography or magnetic resonance angiography due to a lack of contrast in the obliterated arch segment. Differentiation of a double arch with atresia from the right aortic arch is vital as an atretic double arch is a form of the complete vascular ring while the right aortic arch may or may not be symptomatic. Knowledge of some key imaging features can help distinguish these entities. In this case report, we discuss an uncommon case of a double aortic arch with atresia between the left common carotid and left subclavian artery. We also describe its close mimics, their embryological basis, and ways to differentiate it from the right aortic arch.

Evaluation of Transcatheter Pulmonary Valve Endocarditis by Dual-Energy Computed Tomography

Transcatheter pulmonary valve implantation (TPVI) is now an established alternative to surgery in patients with congenital heart disease and dysfunctional right ventricular outflow tract (RVOT) conduit. However, there is recognition of a higher incidence of infective endocarditis in the patients after TPVI. Transthoracic and transesophageal echocardiography is limited in the evaluation of prosthetic pulmonary valve endocarditis secondary to a metallic artifact and degenerative calcified conduit. Additionally, the anterior position of RVOT also limits evaluation by echocardiography. Conventional single-energy CTA can also be sub-optimal in evaluating prosthetic pulmonary valve stent frame due to streak artifacts from the metallic cage and poor contrast to noise ratio if higher kV is used for single-energy CTA to avoid metallic artifacts. Dual-energy CTA can overcome these limitations using reconstructed virtual monoenergetic and iodine-only images for metal artifact reduction and improve intra-stent luminal visualization. Reconstructed iodine perfusion maps may also help differentiate vegetation from a thrombus. In this case report, we discuss the diagnostic utility of dual-energy cardiac CT in the evaluation of endocarditis after TPVI and discuss the imaging protocol.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose

PURPOSE

We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA).

METHODS

Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values.

RESULTS

Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001).

CONCLUSION

Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.

Imaging of the aortic root on high-pitch non-gated and ECG-gated CT: awareness is the key!

The aortic pathologies are well recognized on imaging. However, conventionally cardiac and proximal aortic abnormalities were only seen on dedicated cardiac or aortic studies due to need for ECG gating. Advances in CT technology have allowed motionless imaging of the chest and abdomen, leading to an increased visualization of cardiac and aortic root diseases on non-ECG-gated imaging. The advances are mostly driven by high pitch due to faster gantry rotation and table speed. The high-pitch scans are being increasingly used for variety of clinical indications because the images are free of motion artifact (both breathing and pulsation) as well as decreased radiation dose. Recognition of aortic root pathologies may be challenging due to lack of familiarity of radiologists with disease spectrum and their imaging appearance. It is important to recognize some of these conditions as early diagnosis and intervention is key to improving prognosis. We present a comprehensive review of proximal aortic anatomy, pathologies commonly seen at the aortic root, and their imaging appearances to familiarize radiologists with the diseases of this location.

Imaging Spectrum of Double-Outlet Right Ventricle on Multislice Computed Tomography

Double-outlet right ventricle is a complex congenital heart disease that encompasses various common and rare subtypes. Surgical management of these patients needs to be individualized owing to extremely variable morphology and hemodynamics. Imaging plays a crucial role in determination and characterization of outflow tract morphology. The assessment of ventricular septal defect routability with identification of associated anomalies has therapeutic implications in these patients. Multislice computed tomography with advanced 3-dimensional postprocessing techniques and dose-reduction strategies is invaluable in defining the anatomy and morphology of double-outlet right ventricle with simultaneous assessment of associated anomalies.

Abstract WP72: Low kV Computed Tomography Cerebral Angiogram Using Third-Generation Reconstruction Algorithm Provides Significantly Improved Image Quality With Lower Contrast and Radiation Exposure

Introduction/Aim: We aimed to determine if CTA imaging using third generation reconstruction algorithm and lower contrast dose-low kVp technique (LD-CTA) was comparable to regular contrast dose CTA at 120 kVp using a sinogram affirmed iterative reconstruction algorithm (ND-CTA).

Methods: Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Contrast- and signal-to-noise ratios (CNR/ SNR) were compared for the mid-M1-MCA vessels bilaterally and the mid-basilar artery. Fisher’s exact test was used to compare subjective image quality. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. Finally differences in contrast dose, CT-dose index (CTDI) and dose length product (DLP) were compared using Mann-Whitney U test.

Results: Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% (84-100%) for group-1 versus 89.2% (82%-98%) for group-2). The subjective scores were not statistically different in the anterior circulation but showed significantly better image quality for the basilar artery. LD-CTA group showed significantly better SNR and CNR (p < 0.0001) for both MCA vessels and the basilar artery. ICC showed moderate correlation (0.51-0.63) between the observers. Student paired t-test did not show any significant difference between the observers. LD-CTA group also used lower contrast (49 cc versus 103 cc in ND-CTA) and had lower radiation exposure (DLP/ CTDI for both groups 268.3/12.42 vs 519.5/ 25.15, both < 0.0001).

Conclusion: Next-generation reconstruction algorithm and low-Kv scanning significantly improved image quality on cerebral CTA images despite lower contrast dose, and in addition, have lower radiation exposure.

Pulmonary embolism rule out: positivity and factors affecting the yield of CT angiography

OBJECTIVE

CT pulmonary angiography (CTPA) is one of the most commonly ordered CT imaging tests. It is often believed to be overutilised with few recent studies showing a yield of less than 2%. This study aimed to determine the overall positivity rate of CTPA examinations and understand the factors that affect the yield of the CTPA examination.

METHODS

We retrospectively analysed 2713 patients who received the CTPA exam between 2016 and 2018. Type of study ordered (CTPA chest or CTPA chest with abdomen and pelvis CT), patient location (emergency department (ED), outpatient, inpatient, intensive care unit (ICU)) and patient characteristics-age, sex and body mass index (BMI) were recorded. A logistic regression analysis was performed to determine what factors affect the positivity rate of CT scans for pulmonary embolism (PE).

RESULTS

With 296 positive test results, the overall CTPA positivity was 10.9%. Male sex was associated with higher CTPA positivity, gender difference was maximum in 18-year to 35-year age group. Overweight and obese patients had significantly higher positivity as compared with BMI<25 (p<0.05). Higher positivity rate was seen in the BMI 25-40 group (11.9%) as compared with BMI>40 (10.1%) (p<0.05). Significant difference (p<0.001) was also found in CTPA examination yield from ICU (15.3%) versus inpatients (other than ICU) (12.4%) versus ED (9.6%), and outpatients (8.5%). The difference in CTPA yield based on the type of CT order (CTPA chest vs CTPA chest with CT abdomen and pelvis), patient's age and sex was not significant.

CONCLUSION

CTPA yield of 10.9% in this study is comparable to acceptable positivity rate for the USA and is higher than recent studies showing positivity of <2%. Patient characteristics like obesity and ICU or inpatient location are associated with higher rate of CT positivity.