Pancreatic cysts: can surveillance interval for small low-risk lesions be lengthened?

BACKGROUND: Surveillance of pancreatic cysts are necessary due to risk of malignant transformation. However, reported progression rates to advanced neoplasia are variable and the high frequency of surveillance scans may pose a considerable burden on healthcare resources. PURPOSE: To validate the effectiveness of the Fukuoka Guidelines surveillance regime and determine if a longer surveillance interval can be established. MATERIAL AND METHODS: All magnetic resonance imaging (MRI) studies of the pancreas performed at our institution between January 2014 and December 2016 with at least one pancreatic cystic lesion and follow-up MRI or computed tomography (CT) over at least two years were reviewed for size, worrisome feature (WF), and high-risk stigmata (HRS) at diagnosis and follow-up imaging (up to year 6). Reference standards for advanced neoplasia were based on endoscopic ultrasound, fine needle aspiration cytology, or the presence of ≥2 WF or ≥1 HRS on imaging. Comparison of MRI features of progression and outcomes of diagnostic endpoints between lesions <20 mm and ≥20 mm was performed. RESULTS: A total of 270 patients were included (201 cysts <20 mm, 69 cysts ≥20 mm). Compared with cysts <20 mm, cysts ≥20 mm were more likely to be associated with WF or HRS (40.6% vs. 12.4%; P ≤0.00001), demonstrate increase in size of ≥5 mm in two years (20.3% vs. 10.9%; P = 0.049), and develop advanced neoplasia (24.6% vs. 0.5%; P <0.00001). CONCLUSION: Pancreatic cysts <20 mm have a low risk of developing WF and HRS and surveillance interval may be lengthened.

Variant Hepatocellular Carcinoma Subtypes According to the 2019 WHO Classification: An Imaging-Focused Review.

The 2019 5th edition of the WHO classification of digestive system tumors estimates that up to 35% of hepatocellular carcinomas (HCCs) can be classified as one of eight subtypes defined by molecular characteristics: steatohepatitic, clear cell, macrotrabecular-massive, scirrhous, chromophobe, fibrolamellar, neutrophil-rich, and lymphocyte-rich HCCs. Due to their distinct cellular and architectural characteristics, these subtypes may not display arterial phase hyperenhancement and washout appearance, which are the classic MRI features of HCC, creating challenges in noninvasively diagnosing such lesions as HCC. Moreover, certain subtypes with atypical imaging features have a worse prognosis than other HCCs. A range of distinguishing imaging features may help raise suspicion that a liver lesion represents one of these HCC subtypes. In this review, we describe the MRI features that have been reported in association with various HCC subtypes according to the 2019 WHO classification, with attention given to the current understanding of these subtypes' pathologic and molecular bases and relevance to clinical practice. Imaging findings that differentiate the subtypes from benign liver lesions and non-HCC malignancies are highlighted. Familiarity with these sub-types and their imaging features may allow the radiologist to suggest their presence, though histologic analysis remains needed to establish the diagnosis.

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of obesity is crucial for profiling and management of obesity, as different fat-subcutaneous adipose tissue depots (SAT) and visceral adipose tissue depots (VAT) have various degrees of influence on metabolic syndrome and morbidities. Manual segmentation is time consuming and laborious. Study focuses on the development of a deep learning-based, complete data processing pipeline for MRI-based fat analysis, for large cohort studies which include (1) data augmentation and preprocessing (2) model zoo (3) visualization dashboard, and (4) correction tool, for automated quantification of fat compartments SAT and VAT. METHODS: Our sample comprised 190 healthy community-dwelling older adults from the Geri-LABS study with mean age of 67.85 ± 7.90 years, BMI 23.75 ± 3.65 kg/m2, 132 (69.5%) female, and mainly Chinese ethnicity. 3D-modified Dixon T1-weighted gradient-echo MR images were acquired. Residual global aggregation-based 3D U-Net (RGA-U-Net) and standard 3D U-Net were trained to segment SAT, VAT, superficial and deep subcutaneous adipose tissue depots (SSAT and DSAT). Manual segmentation from 26 subjects was used as ground truth during training. Data augmentations, random bias, noise and ghosting were carried out to increase the number of training datasets to 130. Segmentation accuracy was evaluated using Dice and Hausdorff metrics. RESULTS: The accuracy of segmentation was SSAT:0.92, DSAT:0.88 and VAT:0.9. Average Hausdorff distance was less than 5 mm. Automated segmentation significantly correlated R2 > 0.99 (p < 0.001) with ground truth for all 3-fat compartments. Predicted volumes were within ± 1.96SD from Bland-Altman analysis. CONCLUSIONS: DL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10 s.

Consensus report from the 9th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging.

OBJECTIVES: The 9th International Forum for Liver Magnetic Resonance Imaging (MRI) was held in Singapore in September 2019, bringing together radiologists and allied specialists to discuss the latest developments in and formulate consensus statements for liver MRI, including the applications of gadoxetic acid-enhanced imaging. METHODS: As at previous Liver Forums, the meeting was held over 2 days. Presentations by the faculty on days 1 and 2 and breakout group discussions on day 1 were followed by delegate voting on consensus statements presented on day 2. Presentations and discussions centered on two main meeting themes relating to the use of gadoxetic acid-enhanced MRI in primary liver cancer and metastatic liver disease. RESULTS AND CONCLUSIONS: Gadoxetic acid-enhanced MRI offers the ability to monitor response to systemic therapy and to assist in pre-surgical/pre-interventional planning in liver metastases. In hepatocellular carcinoma, gadoxetic acid-enhanced MRI provides precise staging information for accurate treatment decision-making and follow-up post therapy. Gadoxetic acid-enhanced MRI also has potential, currently investigational, indications for the functional assessment of the liver and the biliary system. Additional voting sessions at the Liver Forum debated the role of multidisciplinary care in the management of patients with liver disease, evidence to support the use of abbreviated imaging protocols, and the importance of standardizing nomenclature in international guidelines in order to increase the sharing of scientific data and improve the communication between centers. KEY POINTS: • Gadoxetic acid-enhanced MRI is the preferred imaging method for pre-surgical or pre-interventional planning for liver metastases after systemic therapy. • Gadoxetic acid-enhanced MRI provides accurate staging of HCC before and after treatment with locoregional/biologic therapies. • Abbreviated protocols for gadoxetic acid-enhanced MRI offer potential time and cost savings, but more evidence is necessary. The use of gadoxetic acid-enhanced MRI for the assessment of liver and biliary function is under active investigation.

Cost-effectiveness of MRI targeted biopsy strategies for diagnosing prostate cancer in Singapore.

BACKGROUND: To evaluate the cost-effectiveness of six diagnostic strategies involving magnetic resonance imaging (MRI) targeted biopsy for diagnosing prostate cancer in initial and repeat biopsy settings from the Singapore healthcare system perspective. METHODS: A combined decision tree and Markov model was developed. The starting model population was men with mean age of 65 years referred for a first prostate biopsy due to clinical suspicion of prostate cancer. The six diagnostic strategies were selected for their relevance to local clinical practice. They comprised MRI targeted biopsy following a positive pre-biopsy multiparametric MRI (mpMRI) [Prostate Imaging - Reporting and Data System (PI-RADS) score ≥ 3], systematic biopsy, or saturation biopsy employed in different testing combinations and sequences. Deterministic base case analyses with sensitivity analyses were performed using costs from the healthcare system perspective and quality-adjusted life years (QALY) gained as the outcome measure to yield incremental cost-effectiveness ratios (ICERs). RESULTS: Deterministic base case analyses showed that Strategy 1 (MRI targeted biopsy alone), Strategy 2 (MRI targeted biopsy ➔ systematic biopsy), and Strategy 4 (MRI targeted biopsy ➔ systematic biopsy ➔ saturation biopsy) were cost-effective options at a willingness-to-pay (WTP) threshold of US$20,000, with ICERs ranging from US$18,975 to US$19,458. Strategies involving MRI targeted biopsy in the repeat biopsy setting were dominated. Sensitivity analyses found the ICERs were affected mostly by changes to the annual discounting rate and prevalence of prostate cancer in men referred for first biopsy, ranging between US$15,755 to US$23,022. Probabilistic sensitivity analyses confirmed Strategy 1 to be the least costly, and Strategies 2 and 4 being the preferred strategies when WTP thresholds were US$20,000 and US$30,000, respectively. LIMITATIONS AND CONCLUSIONS: This study found MRI targeted biopsy to be cost-effective in diagnosing prostate cancer in the biopsy-naïve setting in Singapore.

Care of the pregnant woman with coronavirus disease 2019 in labor and delivery: anesthesia, emergency cesarean delivery, differential diagnosis in the acutely ill parturient, care of the newborn, and protection of the healthcare personnel.

Coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2, has been declared a pandemic by the World Health Organization. As the pandemic evolves rapidly, there are data emerging to suggest that pregnant women diagnosed as having coronavirus disease 2019 can have severe morbidities (up to 9%). This is in contrast to earlier data that showed good maternal and neonatal outcomes. Clinical manifestations of coronavirus disease 2019 include features of acute respiratory illnesses. Typical radiologic findings consists of patchy infiltrates on chest radiograph and ground glass opacities on computed tomography scan of the chest. Patients who are pregnant may present with atypical features such as the absence of fever as well as leukocytosis. Confirmation of coronavirus disease 2019 is by reverse transcriptase-polymerized chain reaction from upper airway swabs. When the reverse transcriptase-polymerized chain reaction test result is negative in suspect cases, chest imaging should be considered. A pregnant woman with coronavirus disease 2019 is at the greatest risk when she is in labor, especially if she is acutely ill. We present an algorithm of care for the acutely ill parturient and guidelines for the protection of the healthcare team who is caring for the patient. Key decisions are made based on the presence of maternal and/or fetal compromise, adequacy of maternal oxygenation (SpO2 >93%) and stability of maternal blood pressure. Although vertical transmission is unlikely, there must be measures in place to prevent neonatal infections. Routine birth processes such as delayed cord clamping and skin-to-skin bonding between mother and newborn need to be revised. Considerations can be made to allow the use of screened donated breast milk from mothers who are free of coronavirus disease 2019. We present management strategies derived from best available evidence to provide guidance in caring for the high-risk and acutely ill parturient. These include protection of the healthcare workers caring for the coronavirus disease 2019 gravida, establishing a diagnosis in symptomatic cases, deciding between reverse transcriptase-polymerized chain reaction and chest imaging, and management of the unwell parturient.

Role of Imaging in the Local Staging of Urothelial Carcinoma of the Bladder.

OBJECTIVE: The purpose of this article is to review imaging modalities used in local staging of bladder urothelial carcinoma. CONCLUSION: Urothelial carcinoma is the most common histologic subtype of bladder cancer, and accurate local staging of this tumor is crucial for management. Traditionally, local staging relied on biopsy. With increasing accuracy of imaging modalities and techniques, imaging also plays an important role in the multidisciplinary care of patients with this disease.

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy. MATERIALS AND METHODS: The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects. RESULTS: The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively. CONCLUSION: Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

Isotropic reconstruction of a 4-D MRI thoracic sequence using super-resolution.

PURPOSE: Four-dimensional (4D) thoracic magnetic resonance imaging (MRI) sequences have been shown to successfully monitor both tumor and lungs anatomy. However, a high temporal resolution is required to avoid motion artifacts, which leads to volumes with poor spatial resolution. This article proposes to reconstruct an isotropic 4D MRI thoracic sequence with minimum modifications to the acquisition protocols. This could be an important step toward the use of 4D MRI for thoracic radiotherapy applications. METHODS: In a postacquisition step, three orthogonal 4D anisotropic acquisitions are combined using super-resolution to reconstruct a series of isotropic volumes. A new phantom that simulates lung tumor motion is developed to evaluate the performance of the algorithm. The proposed framework is also applied to real data of a lung cancer patient. RESULTS: Subjective and objective evaluations show clear resolution enhancement and partial volume effect diminution. The isotropic reconstruction of patient data significantly improves both the visualization and segmentation of thoracic structures. CONCLUSIONS: The results presented here are encouraging and suggest that super-resolution can be regarded as an efficient method to improve the resolution of 4D MRI sequences. It produces an isotropic 4D sequence that would be impossible to acquire in practice. Further investigations will be required to evaluate its reproducibility in various clinical applications.

An in-vivo evaluation of a MEMS drug delivery device using Kunming mice model.

The use of MEMS implantable drug delivery pump device enables one to program the desired drug delivery profile in the device for individualized medicine treatment to patients. In this study, a MEMS drug delivery device is prepared and employed for in vivo applications. 12 devices are implanted subcutaneously into Kunming mice for evaluating their long term biocompatibility and drug-delivery efficiency in vivo. All the mice survived after device implantation surgery procedures. Histological analysis result reveals a normal wound healing progression within the tissues-to-device contact areas. Serum analysis shows that all measured factors are within normal ranges and do not indicate any adverse responses associated with the implanted device. Phenylephrine formulation is chosen and delivered to the abdominal cavity of the mice by using either the implanted MEMS device (experimental group) or the syringe injection method (control group). Both groups show that they are able to precisely control and manipulate the increment rate of blood pressure in the small animals. Our result strongly suggests that the developed refillable implantable MEMS devices will serve as a viable option for future individualized medicine applications such as glaucoma, HIV-dementia and diabetes therapy.

Revisiting renovascular imaging for renal sympathetic denervation: current techniques and applications.

UNLABELLED: Renal sympathetic denervation (RDN) is an emerging technique in the treatment of resistant hypertension, most commonly performed using an endovascular approach. Clinical and anatomical criteria for RDN are well established and imaging plays an integral role in selecting patients with suitable anatomy, procedural planning and device selection. Nevertheless, the current body of literature surrounding imaging related to RDN remains limited. The purpose of this article is to illustrate the expectations and limitations of various imaging techniques, including Doppler ultrasound, CT angiography, MR angiography and newer techniques such as non-contrast MR angiography, in the context of RDN. KEY POINTS: • To understand the role of imaging in renal denervation • To understand strengths and weaknesses of current imaging techniques • To understand the relevant imaging findings in the context of renal denervation.

Dynamic contrast-enhanced MRI for the detection of prostate cancer: meta-analysis.

OBJECTIVE: The purpose of this study was to systematically review and meta-analyze dynamic contrast-enhanced MRI (DCE-MRI) for the detection of prostate cancer in comparison with standard evaluation with T2-weighted imaging. MATERIALS AND METHODS: A PubMed electronic database search for the terms "dynamic contrast-enhanced," "prostate," and "MRI" was completed for articles up to September 17, 2013. All included studies had histopathologic correlation. Two by two contingency data were constructed for each study. A binormal bayesian ROC model was used to estimate and compare sensitivity, specificity, and AUC among eligible modalities. RESULTS: Both DCE-MRI (0.82-0.86) and diffusion-weighted MRI (DWI) (0.84-0.88) yielded significantly better AUC than T2-weighted imaging (0.68-0.77). Moreover, partial AUC for the combination of DCE-MRI, DWI, and T2-weighted imaging was improved significantly (0.111; 0.103-0.119) when compared with DCE-MRI alone (0.079; 0.072-0.085) and T2-weighted imaging alone (0.079; 0.074-0.084) but not DWI alone (0.099; 0.091-0.108). Sensitivity and specificity were similar among the four modalities. CONCLUSION: DCE-MRI improves AUC of tumor detection overall compared with T2-weighted imaging alone. Methods for DCE-MRI analysis require standardization, but visual analysis performs similar to semiquantitative methods. A two-parameter approach using DCE-MRI and T2-weighted imaging or DWI and T2-weighted imaging may be sufficient, and the latter may be more favorable for most routine prostate cancer imaging.

TransMVAN: Multi-view Aggregation Network with Transformer for Pneumonia Diagnosis.

Automated and accurate classification of pneumonia plays a crucial role in improving the performance of computer-aided diagnosis systems for chest X-ray images. Nevertheless, it is a challenging task due to the difficulty of learning the complex structure information of lung abnormality from chest X-ray images. In this paper, we propose a multi-view aggregation network with Transformer (TransMVAN) for pneumonia classification in chest X-ray images. Specifically, we propose to incorporate the knowledge from glance and focus views to enrich the feature representation of lung abnormality. Moreover, to capture the complex relationships among different lung regions, we propose a bi-directional multi-scale vision Transformer (biMSVT), with which the informative messages between different lung regions are propagated through two directions. In addition, we also propose a gated multi-view aggregation (GMVA) to adaptively select the feature information from glance and focus views for further performance enhancement of pneumonia diagnosis. Our proposed method achieves AUCs of 0.9645 and 0.9550 for pneumonia classification on two different chest X-ray image datasets. In addition, it achieves an AUC of 0.9761 for evaluating positive and negative polymerase chain reaction (PCR). Furthermore, our proposed method also attains an AUC of 0.9741 for classifying non-COVID-19 pneumonia, COVID-19 pneumonia, and normal cases. Experimental results demonstrate the effectiveness of our method over other methods used for comparison in pneumonia diagnosis from chest X-ray images.

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology.

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Singapore tuberculosis (TB) clinical management guidelines 2024: A modified Delphi adaptation of international guidelines for drug-susceptible TB infection and pulmonary disease.

Introduction: Tuberculosis (TB) remains endemic in Singapore. Singapore's clinical practice guidelines for the management of tuberculosis were first published in 2016. Since then, there have been major new advances in the clinical management of TB, ranging from diagnostics to new drugs and treatment regimens. The National TB Programme convened a multidisciplinary panel to update guidelines for the clinical management of drug-susceptible TB infection and disease in Singapore, contextualising current evidence for local practice. Method: Following the ADAPTE framework, the panel systematically reviewed, scored and synthesised English-language national and international TB clinical guidelines published from 2016, adapting recommendations for a prioritised list of clinical decisions. For questions related to more recent advances, an additional primary literature review was conducted via a targeted search approach. A 2-round modified Delphi process was implemented to achieve consensus for each recommendation, with a final round of edits after consultation with external stakeholders. Results: Recommendations for 25 clinical questions spanning screening, diagnosis, selection of drug regimen, monitoring and follow-up of TB infection and disease were formulated. The availability of results from recent clinical trials led to the inclusion of shorter treatment regimens for TB infection and disease, as well as consensus positions on the role of newer technologies, such as computer-aided detection-artificial intelligence products for radiological screening of TB disease, next-generation sequencing for drug-susceptibility testing, and video observation of treatment. Conclusion: The panel updated recommendations on the management of drug-susceptible TB infection and disease in Singapore.

Use of positive oral contrast agents in abdominopelvic computed tomography for blunt abdominal injury: meta-analysis and systematic review.

OBJECTIVES: To determine whether positive oral contrast agents improve accuracy of abdominopelvic CT compared with no, neutral or negative oral contrast agent. METHODS: Literature was searched for studies evaluating the diagnostic performance of abdominopelvic CT with positive oral contrast agents against imaging with no, neutral or negative oral contrast agent. Meta-analysis reviewed studies correlating CT findings of blunt abdominal injury with positive and without oral contrast agents against surgical, autopsy or clinical outcome allowing derivation of pooled sensitivity and specificity. Systematic review was performed on studies with common design and reference standard. RESULTS: Thirty-two studies were divided into two groups. Group 1 comprised 15 studies comparing CT with positive and without oral contrast agents. Meta-analysis of five studies from group 1 provided no difference in sensitivity or specificity between CT with positive or without oral contrast agents. Group 2 comprised 17 studies comparing CT with positive and neutral or negative oral contrast agents. Systematic review of 12 studies from group 2 indicated that neutral or negative oral contrasts were as effective as positive oral contrast agents for bowel visualisation. CONCLUSIONS: There is no difference in accuracy between CT performed with positive oral contrast agents or with no, neutral or negative oral contrast agent. KEY POINTS: • There is no difference in the accuracy of CT with or without oral contrast agent. • There is no difference in the accuracy of CT with Gastrografin or water. • Omission of oral contrast, utilising neutral or negative oral contrast agent saves time, costs and decreases risk of aspiration.

Machine learning in medicine: what clinicians should know.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.

Harnessing artificial intelligence in radiology to augment population health.

This review article serves to highlight radiological services as a major cost driver for the healthcare sector, and the potential improvements in productivity and cost savings that can be generated by incorporating artificial intelligence (AI) into the radiology workflow, referencing Singapore healthcare as an example. More specifically, we will discuss the opportunities for AI in lowering healthcare costs and supporting transformational shifts in our care model in the following domains: predictive analytics for optimising throughput and appropriate referrals, computer vision for image enhancement (to increase scanner efficiency and decrease radiation exposure) and pattern recognition (to aid human interpretation and worklist prioritisation), natural language processing and large language models for optimising reports and text data-mining. In the context of preventive health, we will discuss how AI can support population level screening for major disease burdens through opportunistic screening and democratise expertise to increase access to radiological services in primary and community care.