Tenascin-C potentiates Wnt signaling in thyroid cancer.

Tenascin-C (TNC) is a secreted extracellular matrix protein that is highly expressed during embryonic development and re-expressed during wound healing, inflammation, and neoplasia. Studies in developmental models suggest that TNC may regulate the Wnt signaling pathway. Our lab has shown high levels of Wnt signaling and TNC expression in anaplastic thyroid cancer (ATC), a highly lethal cancer with an abysmal ~3-5 month median survival.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Molecular signature incorporating the immune microenvironment enhances thyroid cancer outcome prediction.

Genomic and transcriptomic analysis has furthered our understanding of many tumors. Yet, thyroid cancer management is largely guided by staging and histology, with few molecular prognostic and treatment biomarkers. Here, we utilize a large cohort of 251 patients with 312 samples from two tertiary medical centers and perform DNA/RNA sequencing, spatial transcriptomics, and multiplex immunofluorescence to identify biomarkers of aggressive thyroid malignancy.

USP47 deubiquitylates Groucho/TLE to promote Wnt-β-catenin signaling.

The Wnt-β-catenin signal transduction pathway is essential for embryonic development and adult tissue homeostasis. Wnt signaling converts TCF from a transcriptional repressor to an activator in a process facilitated by the E3 ligase XIAP. XIAP-mediated monoubiquitylation of the transcriptional corepressor Groucho (also known as TLE) decreases its affinity for TCF, thereby allowing the transcriptional coactivator β-catenin to displace it on TCF.

ESTIMATION OF FETAL AND PEDIATRIC DOSES FROM CHEST CT EXAMINATIONS USING VIRTUALDOSE SOFTWARE.

Pregnant women and children sometimes had to undergo chest computed tomography (CT) scans during the Corona Virus Disease 2019 (COVID-19) pandemic. This study estimated the fetal and pediatric doses from chest CT scans. Organ doses and effective doses were calculated using the VirtualDose-CT software.

Geographical clusters of dengue outbreak in Singapore during the Covid-19 nationwide lockdown of 2020.

Dengue, a mosquito-transmitted viral disease, has posed a public health challenge to Singaporean residents over the years. In 2020, Singapore experienced an unprecedented dengue outbreak. We collected a dataset of geographical dengue clusters reported by the National Environment Agency (NEA) from 15 February to 9 July in 2020, covering the nationwide lockdown associated with Covid-19 during the period from 7 April to 1 June.

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure.

This paper describes the development and validation of a GPU-accelerated Monte Carlo (MC) dose computing module dedicated to organ dose calculations of individual patients undergoing nuclear medicine (NM) internal radiation exposures involving PET/CT examination.. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features.

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

Purpose: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving Alzheimer's Disease (AD) patients.

Methods: PET datasets were collected for 25 patients injected with F-AV45 radiopharmaceutical. To generate necessary training data, PET images from both normal-scanning-time (20-min) as well as so-called "shortened-scanning-time" (1-min, 2-min, 5-min, and 10-min) were reconstructed for each patient.

Clinical application and improvement of a CNN-based autosegmentation model for clinical target volumes in cervical cancer radiotherapy.

Objective: Clinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN-based autosegmentation of CTV contours in cervical cancer.

Risk assessment of airborne COVID-19 exposure in social settings.

The COVID-19 pandemic has led to many countries oscillating between various states of lock-down as they seek to balance keeping the economy and essential services running and minimizing the risk of further transmission. Decisions are made about which activities to keep open across a range of social settings and venues guided only by heuristics regarding social distancing and personal hygiene. Hence, we propose the dual use of computational fluid dynamic simulations and surrogate aerosol measurements for location-specific assessment of risk of infection across different real-world settings.

Airborne dispersion of droplets during coughing: a physical model of viral transmission.

The Covid-19 pandemic has focused attention on airborne transmission of viruses. Using realistic air flow simulation, we model droplet dispersion from coughing and study the transmission risk related to SARS-CoV-2. Although this model defines most airborne droplets as 8-16 µm in diameter, we infer that larger droplets of 32-40 µm in diameter may potentially be more infectious due to higher viral content.

Dispersion of evaporating cough droplets in tropical outdoor environment.

The ongoing Covid-19 pandemic has focused our attention on airborne droplet transmission. In this study, we simulate the dispersion of cough droplets in a tropical outdoor environment, accounting for the effects of non-volatile components on droplet evaporation. The effects of relative humidity, wind speed, and social distancing on evaporative droplet transport are investigated.

Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

Objective: To evaluate the accuracy of a deep learning-based auto-segmentation mode to that of manual contouring by one medical resident, where both entities tried to mimic the delineation "habits" of the same clinical senior physician.

Methods: This study included 125 cervical cancer patients whose clinical target volumes (CTVs) and organs at risk (OARs) were delineated by the same senior physician. Of these 125 cases, 100 were used for model training and the remaining 25 for model testing.

INCIDENTAL PULMONARY METASTASES REVEALING SUBCENTIMETER PAPILLARY THYROID CARCINOMA.

Objective: Here we present 2 cases of papillary thyroid microcarcinomas (PMCs) that had metastasized at presentation. The 2015 American Thyroid Association and the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) criteria do not recommend biopsy of the majority of subcentimeter thyroid nodules, as PMCs are mostly indolent with excellent prognosis. However, the paradigm of active surveillance presents a conundrum on how to identify the rare patient with distant metastatic disease while avoiding unnecessary intervention in the majority.

End-to-end unsupervised cycle-consistent fully convolutional network for 3D pelvic CT-MR deformable registration.

Objective: To improve the efficiency of computed tomography (CT)-magnetic resonance (MR) deformable image registration while ensuring the registration accuracy.

Methods: Two fully convolutional networks (FCNs) for generating spatial deformable grids were proposed using the Cycle-Consistent method to ensure the deformed image consistency with the reference image data. In all, 74 pelvic cases consisting of both MR and CT images were studied, among which 64 cases were used as training data and 10 cases as the testing data.

CONCEPTUAL DESIGN AND PRELIMINARY RESULTS OF A VR-BASED RADIATION SAFETY TRAINING SYSTEM FOR INTERVENTIONAL RADIOLOGISTS.

To help minimise occupational radiation exposure in interventional radiology, we conceptualised a virtual reality-based radiation safety training system to help operators understand complex radiation fields and to avoid high radiation areas through game-like interactive simulations. The preliminary development of the system has yielded results suggesting that the training system can calculate and report the radiation exposure after each training session based on a database precalculated from computational phantoms and Monte Carlo simulations and the position information provided by the Microsoft HoloLens headset. In addition, real-time dose rate and cumulative dose will be displayed to the trainee to help them adjust their practice.

A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.

Purpose: To develop and test a three-dimensional (3D) deep learning model for predicting 3D voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).

Methods: A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net_B was constructed to predict 3D dose distributions.

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017.

Dengue has been as an endemic with year-round presence in Singapore. In the recent years 2013, 2014, and 2016, there were several severe dengue outbreaks, posing serious threat to the public health. To proactively control and mitigate the disease spread, early warnings of dengue outbreaks, at which there are rapid and large-scale spread of dengue incidences, are extremely helpful.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

Purpose: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation.

Patient-Specific Organ and Effective Dose Estimates in Adult Oncologic CT.

Patient-specific organ and effective dose provides essential information for CT protocol optimization. However, such information is not readily available in the scan records. The purpose of this study was to develop a method to obtain accurate examination- and patient-specific organ and effective dose estimates by use of available scan data and patient body size information for a large cohort of patients.

Monte carlo study of organ doses and related risk for cancer in Tanzania from scattered photons in cervical radiation treatment involving Co-60 source.

Purpose: The present work aimed to evaluate organ doses and related risk for cancer from external beam radiation treatment (EBRT) and high-dose-rate (HDR) brachytherapy (BT) involving Co-60 source for patients with cervical carcinoma in Tanzania based on Monte Carlo methods and to evaluate the secondary cancer risks in their lifetime.

Methods: EBRT and HDR-BR were modelled by using the MCNPX Monte Carlo (MC) code. The MC simulations were performed by using validated models and isocentric irradiation of an adult female computational phantom.

Influences of operator head posture and protective eyewear on eye lens doses in interventional radiology: A Monte Carlo Study.

Purpose: To quantify the effects of operator head posture and different types of protective eyewear on the eye lens dose to operators in interventional radiology (IR).

Methods: A deformable computational human phantom, Rensselaer Polytechnic Institute (RPI) Adult Male, consisting of a high-resolution eye model, was used to simulate a radiologist who is performing an interventional radiology procedure. The radiologist phantom was deformed to a set of different head postures.