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Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside. Keywords: Artificial intelligence, clinical translation, digital innovation, guidelines

Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered 'high risk' through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication.This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC.Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI (Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%.Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.

Global increase in obesity has led to alarming rise in co-morbidities leading to deteriorated quality of life. Obesity phenotyping benefits profiling and management of the condition but warrants accurate quantification of fat compartments. Manual quantification MR scans are time consuming and laborious. Hence, many studies rely on semi/automatic methods for quantification of abdominal fat compartments. We propose a MultiRes-Attention U-Net with hybrid loss function for segmentation of different abdominal fata compartments namely (i) Superficial subcutaneous adipose tissue (SSAT), (ii) Deep subcutaneous adipose tissue (DSAT), and (iii) Visceral adipose tissue (VAT) using abdominal MR scans. MultiRes block, ResAtt-Path, and attention gates can handle shape, scale, and heterogeneity in the data. Dataset involved MR scans from 190 community-dwelling older adults (mainly Chinese, 69.5% females) with mean age—67.85 ± 7.90 years), BMI 23.75 ± 3.65 kg/m2. Twenty-six datasets were manually segmented to generate the ground truth. Data augmentations were performed using MR data acquisition variations. Training and validation were performed on 105 datasets, while testing was conducted on 25 datasets. Median Dice scores were 0.97 for SSAT & DSAT and 0.96 for VAT, and mean Hausdorff distance was

Pneumonia is one of the most common treatable causes of death, and early diagnosis allows for early intervention. Automated diagnosis of pneumonia can therefore improve outcomes. However, it is challenging to develop high-performance deep learning models due to the lack of well-annotated data for training. This paper proposes a novel method, called Deep Supervised Domain Adaptation (DSDA), to automatically diagnose pneumonia from chest X-ray images. Specifically, we propose to transfer the knowledge from a publicly available large-scale source dataset (ChestX-ray14) to a well-annotated but small-scale target dataset (the TTSH dataset). DSDA aligns the distributions of the source domain and the target domain according to the underlying semantics of the training samples. It includes two task-specific sub-networks for the source domain and the target domain, respectively. These two sub-networks share the feature extraction layers and are trained in an end-to-end manner. Unlike most existing domain adaptation approaches that perform the same tasks in the source domain and the target domain, we attempt to transfer the knowledge from a multi-label classification task in the source domain to a binary classification task in the target domain. To evaluate the effectiveness of our method, we compare it with several existing peer methods. The experimental results show that our method can achieve promising performance for automated pneumonia diagnosis.

Cholangiocarcinoma (CCA) is the second most common primary hepatobiliary malignancy and presents as three separate morphological subtypes; namely mass-forming, periductal-infiltrating, and intraductal-growing patterns. Each of these subtypes have distinct imaging characteristics, as well as a variety of benign and malignant mimics, making accurate diagnosis of CCA on imaging challenging. Whilst histopathological examination is required to arrive at a definitive diagnosis, it is still important for radiologists to be cognizant of these entities and provide reasonable differential diagnoses, as these potentially have a large impact on patient management. This pictorial essay illustrates the three morphological subtypes of CCA, as well as some important mimics for each subtype, that are encountered in clinical practice.

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.

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. 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. 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

Focal liver lesions are commonly encountered. Grey-scale and Doppler sonographic characteristics of focal liver lesions are often non-specific and insufficient to conclusively characterise lesions as benign or malignant. Contrast-enhanced ultrasound is useful for the characterisation of FLLs in patients who are unable to undergo contrast-enhanced computed tomography or magnetic resonance imaging. It is also easily available and relatively cheap. However, interpretation of contrast-enhanced ultrasound can be challenging without a systematic approach. In this pictorial essay, we highlight an algorithm-based approach to FLLs and discuss the characteristic contrast-enhanced ultrasound features of commonly encountered and clinically significant focal liver lesions.

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents’ diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

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.

Splenosis is defined as the autotransplantation of viable splenic tissue throughout various anatomic compartments. Intrahepatic splenosis (IHS) is rare and diagnosis is often challenging. This study aims to provide a comprehensive review on IHS. A literature review was performed on PubMed database. Fifty-six articles with 59 reported cases were included. The majority of the patients were male (n = 49, 83.1%). Median age was 51 years. Risk factors for hepatocellular carcinoma (HCC) included hepatitis B (n = 8, 13.6%) and cirrhosis (n = 12, 20.3%). The majority of the patients were asymptomatic (62.7%) and did not have risk factors for HCC (55.9%). We report a diagnostic triad for IHS: 1) previous history of abdominal trauma or splenectomy, 2) absence of risk factors for liver malignancy and 3) typical imaging features. Non-invasive diagnostic tests such as technetium-99m-tagged heat-damaged red blood cell scintigraphy are useful in diagnosis. Malignancy should be ruled out in the presence of risk factors for HCC.

Targeted biopsy using multiparametric magnetic resonance imaging increases the detection rate of clinically significant prostate cancer (csCaP). In this meta-analysis, we compare the diagnostic accuracy of transrectal (TR) vs transperineal (TP) approaches for MRI-guided software fusion biopsy (FB) in the detection of csCaP. A literature search was performed in PubMed, Cochrane and Embase electronic databases up until July 2019 following the preferred reporting items for systematic review and meta-analysis system. The pooled sensitivity and specificity of either approach was evaluated using radical prostatectomy or systematic biopsies with ≥24 biopsy cores to be the reference standard. Fourteen papers with a total of 2002 patients were selected. Seven hundred and sixty-five patients underwent TR FB, while 1,387 underwent TP FB. One hundred and fifty of the patients underwent both TR and TP approaches. Both approaches were similar in terms of sensitivity (TR vs. TP: 0.81 vs 0.80) and specificity (TR vs. TP: 0.99 vs 0.95). In terms of likelihood ratios and diagnostic odds ratio, TR performed better than TP approach. The area under the receiving operator curve for both approaches was similar (0.91 vs 0.88 respectively). However, there was substantial heterogeneity across the studies for both approaches. TP and TR approaches to software-based FB yield similar diagnostic performance for the detection of csCaP. When deciding on the approach, physicians should consider other inherent features of either technique that suit their practice.

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 and was declared a global pandemic by the World Health Organization on 11 March 2020. A definitive diagnosis of COVID-19 is made after a positive result is obtained on reverse transcription-polymerase chain reaction assay. In Singapore, rigorous contact tracing was practised to contain the spread of the virus. Nasal swabs and chest radiographs (CXR) were also taken from individuals who were suspected to be infected by COVID-19 upon their arrival at a centralised screening centre. From our experience, about 40% of patients who tested positive for COVID-19 had initial CXR that appeared “normal”. In this case series, we described the temporal evolution of COVID-19 in patients with an initial “normal” CXR. Since CXR has limited sensitivity and specificity in COVID-19, it is not suitable as a first-line diagnostic tool. However, when CXR changes become unequivocally abnormal, close monitoring is recommended to manage potentially severe COVID-19 pneumonia. Key words: Diagnostic Radiology, Infectious Diseases, Pulmonary

Objectives: To perform a systematic review and meta-analysis comparing diagnostic performance and inter reader agreement between PI-RADS v. 2.1 and PI-RADS v. 2 in the detection of clinically significant prostate cancer (csPCa). Methods: A systematic review was performed, searching the major biomedical databases (Medline, Embase, Scopus), using the keywords “PIRADS 2.1” or “PI RADS 2.1” or “PI-RADS 2.1”. Studies reporting on head-to-head diagnostic comparison between PI-RADS v. 2.1 and v. 2 were included. Pooled sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were compared between PI-RADS v. 2.1 and v. 2. Summary receiver operator characteristic graphs were plotted. Analysis was performed for whole gland, and pre-planned subgroup analysis was performed by tumour location (whole gland vs transition zone (TZ)), high b-value DWI (b-value ≥1400 s/mm2), and reader experience (Results: Eight studies (1836 patients, 1921 lesions) were included. Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for whole gland (0.62 vs 0.66, p = 0.02). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.17, 0.31, 0.41). Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for TZ only (0.67 vs 0.72, p = 0.01). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.06, 0.36, 0.17). Amongst studies utilising diffusion-weighted imaging with highest b-value of ≥1400 s/mm2, pooled sensitivities, specificities, PPVs and NPVs were not significantly different (p = 0.52, 0.4, 0.5, 0.47). There were no significant differences in pooled sensitivities, specificities, PPVs and NPVs between PI-RADS v. 2.1 and PI-RADS v. 2 for less-experienced readers (p = 0.65, 0.37, 0.65, 0.81) and for more experienced readers (p = 0.57, 0.90, 0.91, 0.65). For PI-RADS v. 2.1 alone, there were no significant differences in pooled sensitivity, specificity, PPV and NPV between less and more experienced readers (p = 0.38, 0.70, 1, 0.48). Inter-reader agreement was moderate to substantial for both PI-RADS v. 2.1 and v. 2. There were no significant differences between pooled csPCa rates between PI-RADS v. 2.1 and v. 2 for PI-RADS 1–2 lesions (6.6% vs 7.3%, p = 0.53), or PI-RADS 3 lesions (24.1% vs 26.8%, p = 0.28). Conclusions: Diagnostic performance and inter-reader agreement for PI-RADS v. 2.1 is comparable to PI-RADS v. 2, however the significantly lower specificity of PI-RADS v. 2.1 may result in increased number of unnecessary biopsies. Advances in knowledge: 1. Compared to PI-RADS v. 2, PI-RADS v. 2.1 has a non-significantly higher sensitivity but a significantly lower specificity for detection of clinically significant prostate cancer. 2. PI-RADS v. 2.1 could potentially result in considerable increase in number of negative targeted biopsy rates for PI-RADS 3 lesions, which could have been potentially avoided.

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field. Published version

Pneumonia can be difficult to diagnose since its symptoms are too variable, and the radiographic signs are often very similar to those seen in other illnesses such as a cold or influenza. Deep neural networks have shown promising performance in automated pneumonia diagnosis using chest X-ray radiography, allowing mass screening and early intervention to reduce the severe cases and death toll. However, they usually require many well-labelled chest X-ray images for training to achieve high diagnostic accuracy. To reduce the need for training data and annotation resources, we propose a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM). It transfers the knowledge from different but relevant datasets to the unlabelled small-size target dataset and improves the semantic quality of the learnt representations. Specifically, we design a conditional domain adversarial network to exploit discriminative information conveyed in the predictions to mitigate the domain gap between the source and target datasets. Furthermore, due to the small scale of the target dataset, we construct a feature cloud for each target sample and leverage contrastive learning to extract more discriminative features. Lastly, we propose adaptive feature cloud expansion to push the decision boundary to a low-density area. Unlike most existing transfer learning methods that aim only to mitigate the domain gap, our method instead simultaneously considers the domain gap and the data deficiency problem of the target dataset. The conditional domain adaptation and the feature cloud generation of our method are learning jointly to extract discriminative features in an end-to-end manner. Besides, the adaptive feature cloud expansion improves the model's generalisation ability in the target domain. Extensive experiments on pneumonia and COVID-19 diagnosis tasks demonstrate that our method outperforms several state-of-the-art unsupervised domain adaptation approaches, which verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.

Magnetic resonance imaging (MRI)-targeted prostate biopsy is the recommended investigation in men with suspicious lesion(s) on MRI. The role of concurrent systematic in addition to targeted biopsies is currently unclear. Using our prospectively maintained database, we identified men with at least one Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesion who underwent targeted and/or systematic biopsies from May 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as any Gleason grade group ≥2 cancer. Of 545 patients who underwent MRI fusion-targeted biopsy, 222 (40.7%) were biopsy naïve, 247 (45.3%) had previous prostate biopsy(s), and 76 (13.9%) had known prostate cancer undergoing active surveillance. Prostate cancer was more commonly found in biopsy-naïve men (63.5%) and those on active surveillance (68.4%) compared to those who had previous biopsies (35.2%; both P0.001). Systematic biopsies provided an incremental 10.4% detection of csPCa among biopsy-naïve patients, versus an incremental 2.4% among those who had prior negative biopsies. Multivariable regression found age (odds ratio [OR] = 1.03, P = 0.03), prostate-specific antigen (PSA) density ≥0.15 ng ml

Introduction: Ultrasound (US) is current standard of care for imaging surveillance in patients at risk for hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and a dual-sequence non-contrast MRI (NCEMRI) for HCC surveillance, in the local setting. Methods: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed-up for 40 years to estimate their disease status, direct medical costs, and effectiveness. Quality-adjusted life years (QALYs) and incremental cost effectiveness ratio were calculated. Results: 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios – no surveillance, US surveillance and NCEMRI surveillance were S$1,193/7.460 QALYs; S$8,099/11.195 QALYs; S$9,720/11.366 QALYs, respectively. Conclusion: Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, CEMRI) based on patients’ risk profiles.

The spleen is a complex organ involved in multiple physiological processes in the human body. Elective splenectomy is an uncommon operation, and the precise characterization of the lesion should be achieved to determine the risks and benefits of this operation accurately. Given the significant role of the spleen in homeostasis and the potential risks of the surgery itself and following sequelae such as infection susceptibility, accurate recognition, and classification of splenic lesions is required before surgery. This review provides an overview of malignant (e.g., lymphoma, angiosarcoma) and benign (e.g., cysts, hemangioma, hamartoma) splenic lesions that may warrant an elective splenectomy. Images from a cohort of adult patients undergoing isolated splenectomy for non-traumatic indications in a single center are provided. This review highlights the considerable overlap in imaging patterns between splenic lesions, splenic lesions masquerading as lesions in other organs, increased detection of asymptomatic splenic incidentalomas due to improvements in imaging modalities. This review also provides clinical correlations for each lesion, providing additional information to help clinicians differentiate between lesions and accurately identify diseases amenable to surgical management.

Gadoxetate magnetic resonance imaging (MRI) is widely used in clinical practice for liver imaging. For optimal use, we must understand both its advantages and limitations. This article is the outcome of an online advisory board meeting and subsequent discussions by a multidisciplinary group of experts on liver diseases across the Asia-Pacific region, first held on September 28, 2020. Here, we review the technical considerations for the use of gadoxetate, its current role in the management of patients with hepatocellular carcinoma (HCC), and its relevance in consensus guidelines for HCC imaging diagnosis. In the latter part of this review, we examine recent evidence evaluating the impact of gadoxetate on clinical outcomes on a continuum from diagnosis to treatment decision-making and follow-up. In conclusion, we outline the potential future roles of gadoxetate MRI based on an evolving understanding of the clinical utility of this contrast agent in the management of patients at risk of, or with, HCC.

Background The presence of concomitant sarcopenia and obesity in sarcopenic obesity (SO) confers worse functional, morbidity and mortality outcomes compared to either alone. Excess adiposity and central redistribution of fats are associated with systemic inflammation and ectopic tissue fat infiltration in forms of Intermuscular adipose tissue (IMAT). Our study examines the profile of IMAT across a spectrum of body compositions and associations with physical performance and inflammatory biomarkers including Monocyte Chemoattractant Protein-1 (MCP-1), a novel biomarker of adipose tissue inflammation. Methods 187 community dwelling elderly participants were recruited and classified into 4 subgroups: normal, obese, sarcopenia and SO, using validated criteria for sarcopenia and waist circumference to define central obesity. We performed magnetic resonance imaging of mid-thigh sections to segment IMAT and muscle. Participants were assessed for muscle strength, physical performance and blood inflammatory biomarkers of interleukin-6, C-Reactive Protein and MCP-1. We examined correlation of IMAT(ratio) with muscle function measures and blood biomarkers. Multiple regression analyses were used to examine the association of body composition types and IMAT(ratio) with muscle function. Results IMAT(ratio) was highest in SO and obese groups. Overall, higher IMAT(ratio) is significantly associated with raised MCP-1, lower gait speed and muscle strength. SO had lowest scores in Short Physical Performance Battery (SPPB), gait speed, hand-grip and knee extension strength. IMAT(ratio) is independently associated with SPPB and handgrip strength, whilst SO is independently associated with muscle strength. Conclusion Our results suggest the possible role of IMAT as a candidate imaging biomarker for adipose tissue inflammation and associated poorer functional outcomes in SO.

Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a “Centaur” model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications. Key words: Diagnostic radiology, Machine learning, Neural networks

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. Published version

PURPOSE: To assess the generalizability of a deep learning pneumothorax detection model on datasets from multiple external institutions and examine patient and acquisition factors that might influence performance. MATERIALS AND METHODS: In this retrospective study, a deep learning model was trained for pneumothorax detection by merging two large open-source chest radiograph datasets: ChestX-ray14 and CheXpert. It was then tested on six external datasets from multiple independent institutions (labeled A–F) in a retrospective case-control design (data acquired between 2016 and 2019 from institutions A–E; institution F consisted of data from the MIMIC–CXR dataset). Performance on each dataset was evaluated by using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, specificity, and positive and negative predictive values, with two radiologists in consensus being used as the reference standard. Patient and acquisition factors that influenced performance were analyzed. RESULTS: The AUCs for pneumothorax detection for external institutions A–F were 0.91 (95% CI: 0.88, 0.94), 0.97 (95% CI: 0.94, 0.99), 0.91 (95% CI: 0.85, 0.97), 0.98 (95% CI: 0.96, 1.0), 0.97 (95% CI: 0.95, 0.99), and 0.92 (95% CI: 0.90, 0.95), respectively, compared with the internal test AUC of 0.93 (95% CI: 0.92, 0.93). The model had lower performance for small compared with large pneumothoraces (AUC, 0.88 [95% CI: 0.85, 0.91] vs AUC, 0.96 [95% CI: 0.95, 0.97]; P = .005). Model performance was not different when a chest tube was present or absent on the radiographs (AUC, 0.95 [95% CI: 0.92, 0.97] vs AUC, 0.94 [95% CI: 0.92, 0.05]; P > .99). CONCLUSION: A deep learning model trained with a large volume of data on the task of pneumothorax detection was able to generalize well to multiple external datasets with patient demographics and technical parameters independent of the training data. Keywords: Thorax, Computer Applications-Detection/Diagnosis See also commentary by Jacobson and Krupinski in this issue. Supplemental material is available for this article. ©RSNA, 2021

We aim to describe a case series of critically and non-critically ill COVID-19 patients in Singapore. This was a multicentered prospective study with clinical and laboratory details. Details for fifty uncomplicated COVID-19 patients and ten who required mechanical ventilation were collected. We compared clinical features between the groups, assessed predictors of intubation, and described ventilatory management in ICU patients. Ventilated patients were significantly older, reported more dyspnea, had elevated C-reactive protein and lactate dehydrogenase. A multivariable logistic regression model identified respiratory rate (aOR 2.83, 95% CI 1.24–6.47) and neutrophil count (aOR 2.39, 95% CI 1.34–4.26) on admission as independent predictors of intubation with area under receiver operating characteristic curve of 0.928 (95% CI 0.828–0.979). Median APACHE II score was 19 (IQR 17–22) and PaO2/FiO2 ratio before intubation was 104 (IQR 89–129). Median peak FiO2 was 0.75 (IQR 0.6–1.0), positive end-expiratory pressure 12 (IQR 10–14) and plateau pressure 22 (IQR 18–26) in the first 24 h of ventilation. Median duration of ventilation was 6.5 days (IQR 5.5–13). There were no fatalities. Most COVID-19 patients in Singapore who required mechanical ventilation because of ARDS were extubated with no mortality.

Purpose This study investigates the differences in diagnostic performance between diffuse-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE), either alone or in combination with T2-weighted imaging (T2WI), for diagnosing deep myometrial invasion (dMI) of endometrial cancers (EC). Methods We performed a comprehensive search for published studies comparing DWI and DCE for preoperatively diagnosing dMI of EC. The overall diagnostic accuracy of each test was calculated using the areas under the summary receiver operating characteristic curves (AUCs). The sensitivities and specificities were compared using bivariate meta-regression. Results Pooled analysis of nineteen studies with 961 patients (main group) showed that DWI had a larger AUC (0.943, 95% confidence interval (CI) = 0.921–0.967) than DCE (0.922, 95% CI = 0.893–0.953). For the subgroup comprising 7 studies, DWI combined with T2WI and DCE combined with T2WI showed AUCs of 0.959 (95% CI, 0.932–0.986) and 0.929 (95% CI, 0.847–1.000), respectively. None of the differences in AUCs were statistically significant. All comparisons of the sensitivities and specificities of the main group and subgroup also showed no significant differences. Conclusion This meta-analysis found no significant difference in diagnostic performance between DWI and DCE for diagnosis of dMI in EC. DWI may be preferred for its ease of use in clinical practice.

Incidental pancreatic lesions are increasingly being detected due to the increasing use of cross-sectional imaging such as computed tomography (CT) and magnetic resonance (MR) imaging. These pancreatic lesions can be broadly categorised into hypervascular and hypovascular lesions by comparing the degree of lesion enhancement to the background pancreatic parenchymal enhancement. For practicality, we define pancreatic lesions as hypervascular when they show enhancement greater or equal to that of the pancreatic parenchyma on either CT or MR imaging vis-a-vis hypovascular lesions, which characteristically enhance less than the background pancreatic parenchyma. Hypovascular lesions have been widely described, including ductal adenocarcinoma and chronic focal pancreatitis.(1) A few prior published reviews related to hypervascular pancreatic masses have focused on the differential entities or their malignant potential.(2,3) To the best of our knowledge, there is a lack of an algorithm-based approach that enables general radiologists to prospectively differentiate the various hypervascular pancreatic lesions, as presented in this pictorial essay. We further discuss the pertinent radiologic pathologic features of each differential diagnosis and illustrate them using individual case examples. Published version

Introduction Chest radiographs (CXR) are widely used for the screening and management of the coronavirus disease 2019 (COVID-19). This paper determinates the radiographic features of COVID-19 based on an initial national cohort of patients. Methods This is a retrospective review of swab-positive COVID-19 patients admitted to four different hospitals in Singapore between 22 January and 9 March 2020. Initial and follow-up CXR were reviewed by three experienced radiologists to identify the predominant pattern and distribution of lung parenchymal abnormalities. Results In total, 347 CXR of 96 patients were reviewed. Initial CXR were abnormal in 41 out of 96 patients (42.7%). The mean time from onset of symptoms to CXR abnormality was 5.3 (range 1-21) days. The predominant pattern of lung abnormality was ground-glass opacity on initial CXR (51.2%) and consolidation on follow-up CXR (51.0%). Multifocal bilateral abnormalities in mixed central and peripheral distribution were seen in 63.4% and 59.2% of abnormal initial and follow-up CXR, respectively. The lower zones were involved in 90.2% of the initial CXR and 93.9% of the follow-up CXR. Conclusion In a cohort of swab-positive patients, including those identified from contact tracing, we found the incidence of CXR abnormality to be lower than previously reported. The most common pattern was ground-glass opacity or consolidation, but mixed central and peripheral involvement was more common than peripheral involvement alone.

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 and was declared a global pandemic by the World Health Organization on 11 March 2020. A definitive diagnosis of COVID-19 is made after a positive result is obtained on reverse transcription-polymerase chain reaction assay. In Singapore, rigorous contact tracing was practised to contain the spread of the virus. Nasal swabs and chest radiographs (CXR) were also taken from individuals who were suspected to be infected by COVID-19 upon their arrival at a centralised screening centre. From our experience, about 40% of patients who tested positive for COVID-19 had initial CXR that appeared "normal". In this case series, we described the temporal evolution of COVID-19 in patients with an initial "normal" CXR. Since CXR has limited sensitivity and specificity in COVID-19, it is not suitable as a first-line diagnostic tool. However, when CXR changes become unequivocally abnormal, close monitoring is recommended to manage potentially severe COVID-19 pneumonia.

Patient: Male, 77-year-old Final Diagnosis: COVID-19 pneumonia Symptoms: Cough • shortness of breath Medication:— Clinical Procedure: — Specialty: Radiology Objective: Challenging differential diagnosis Background: Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, SARS-CoV-2, and is associated with severe respiratory disease. There are extensive publications on the chest computed tomography (CT) findings of COVID-19 pneumonia, with ground-glass opacities (GGO) and mixed GGO and consolidation being the most common findings. Those with interstitial thickening manifesting as reticular opacities typically show superimposed ground-glass opacities, giving a crazy-paving pattern. Case Report: We report the case of a 77-year-old man with a background of asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS) who presented with progressive cough and shortness of breath for 2 days. He was in close contact with a confirmed COVID-19 case. Reverse-transcription polymerase chain reaction analysis of a nasopharyngeal swab was positive for SARS-CoV-2. The initial chest radiograph was negative for lung consolidation and ground-glass opacities. During admission, he had worsening shortness of breath with desaturation, prompting a chest CT examination, which was performed on day 14 of illness. The chest CT revealed an atypical finding of predominant focal subpleural interstitial thickening in the right lower lobe. He was provided supportive treatment along with steroid and antibiotics. He recovered well and subsequently tested negative for 2 consecutive swabs. He was discharged after 34 days. Conclusions: Interstitial thickening or reticular pattern on CT has been described in COVID-19 pneumonia, but largely in association with ground-glass opacity or consolidation. This case demonstrates an atypical predominance of interstitial thickening on chest CT in COVID-19 pneumonia on day 14 of illness, which is the expected time of greatest severity of the disease.

Aim: To compare the latest 2019 version of Bosniak classification (BCnew) against Bosniak classification prior to 2019 (BCold) using contrast-enhanced ultrasound (CEUS) and to compare CEUS against contrast-enhanced CT (CECT) based on BCnew.Material and methods: Patients who had both CEUS and CECT of the kidneys performed within three months of each other were included. CECT and CEUS images of renal cysts were retrospectively analysed by two independent readers using BCnew, extrapolating the BCnew criteria to CEUS. Where histopathology was not available, 3-year imaging follow-up was used as a reference standard.Results: Forty-nine patients with a total of 54 cysts were included. Using BCnew, Bosniak category between CEUS and CECT and both readers was concordant in 18 cysts (33.3%). Bosniak category between CEUS and CT was concordant in 27 cysts (50%) in reader 1 and in 33 cysts (61%) for reader 2. Based on Cohen’s weighted kappa statistic (k), inter-observer agreement was moderate for CEUS (k=0.49) and fair for CECT (k=0.36). Agreement between CEUS and CECT for both readers was fair (reader 1, k=0.24; reader 2, k=0.37). Compared to using BCold, almost half of the benign cysts were assigned to a lower Bosniak category with CEUS using BCnew (reader 1, 42.6%; reader 2, 50%).Conclusions: CEUS assessment based on BCnew more appropriately assigns benign renal cysts to a lower category than CEUS based on BCold. Readers tend to grade renal cysts to a higher Bosniak category with BCnew but with greater inter-reader agreement on CEUS than on CECT

INTRODUCTION This study aimed to evaluate the potential of non-contrast-enhanced magnetic resonance (MR) imaging as an imaging surveillance tool for detection of hepatocellular carcinoma (HCC) in at-risk patients and to compare the performance of non-contrast MR imaging with ultrasonography (US) as a screening modality for the same. METHODS In this retrospective study, patients diagnosed with HCC between 1 January 2010 and 31 December 2015 were selected from our institution’s cancer registry. Patients who underwent MR imaging and had US performed within three months of the MR imaging were included. For each MR imaging, two non-contrast MR imaging sequences – T2-weighted fat-saturated (T2-W FS) sequence and diffusion-weighted imaging (DWI) – were reviewed for the presence of suspicious lesions. A non-contrast MR image was considered positive if the lesion was seen on both sequences. The performance of non-contrast MR imaging was compared to that of hepatobiliary US for the detection of HCC. RESULTS A total of 73 patients with 108 HCCs were evaluated. Sensitivity of non-contrast MR imaging for the detection of HCC using T2-W FS and DWI was 93.2%, which was significantly higher than that of US, which was 79.5% (p = 0.02). In a subgroup of 55 patients with imaging features of liver cirrhosis, the sensitivity of non-contrast MR imaging was 90.9%, which was also significantly higher than that of US, which was 74.5% (p = 0.02). CONCLUSION Our pilot study showed that non-contrast MR imaging, using a combination of T2-W FS and DWI, is a potential alternative to US as a screening tool for surveillance of patients at risk for HCC.

BACKGROUND: Chest radiography (CXR) is performed more widely and readily than CT for the management of coronavirus disease (COVID-19), but there remains little data on its clinical utility. This study aims to assess the diagnostic performance of CXR, with emphasis on its predictive value, for severe COVID-19 disease. METHODS: A retrospective cohort study was conducted, 358 chest radiographs were performed on 109 COVID-19 patients (median age 44.4 years, 58 males and 30 with comorbidities) admitted between 22 January 2020 and 15 March 2020. Each CXR was reviewed and scored by three radiologists in consensus using a 72-point COVID-19 Radiographic Score (CRS). Disease severity was determined by the need for supplemental oxygen and mechanical ventilation. RESULTS: Patients who needed supplemental oxygen (n=19, 17.4%) were significantly older (P

Highlights • Platypnea-orthodeoxia syndrome (POS) is observed in COVID-19 acute respiratory distress syndrome (ARDS) survivors. • POS is associated with older age, lower body mass index and varying degrees of dyspnea. • Arterial to end-tidal carbon dioxide and alveolar to arterial oxygen partial pressure differences were persistently elevated. • POS is likely a gravitational exacerbation of intrapulmonary shunt in ARDS due to COVID-19 specific changes. • POS may cause alarm and requires adjustment in the rehabilitation approach during the recovery period., Platypnea-orthodeoxia syndrome (POS) is a rare clinical syndrome characterized by orthostatic oxygen desaturation and positional dyspnea from supine to an upright position. We observed POS in 5 of 20 cases of severe 2019 novel coronavirus (COVID-19) pneumonia, which demonstrated persistently elevated shunt fraction even after liberation from mechanical ventilation. POS was first observed during physiotherapy sessions; median oxygen desaturation was 8 % (range: 8–12 %). Affected individuals were older (median 64 vs 53 years old, p = 0.05) and had lower body mass index (median 24.7 vs 27.6 kg/m2, p = 0.03) compared to those without POS. While POS caused alarm and reduced tolerance to therapy, this phenomenon resolved over a median of 17 days with improvement of parenchymal disease. The mechanisms of POS are likely due to gravitational redistribution of pulmonary blood flow resulting in increased basal physiological shunting and upper zone dead space ventilation due to the predominantly basal distribution of consolidative change and reported vasculoplegia and microthrombi in severe COVID-19 disease.

Background To determine the utility of chest radiography (CXR) for assessing and prognosticating COVID-19 disease with an objective radiographic scoring system.Methods A multicenter, prospective study was conducted, forty patients were included. Seventy-eight CXR’s were performed on the first derivation cohort of twenty patients with COVID-19 (median age 47.5 years, 10 females and four with comorbidities) admitted between 22 January 2020 and 1 February 2020. Each CXR was scored by three radiologists in consensus and graded on a 72-point COVID-19 Radiographic Score (CRS). This was correlated with supplemental oxygen requirement, C-reactive protein (CRP), lactate dehydrogenase (LDH) and lymphocyte count. To validate our findings, the parameters of another validation cohort of twenty patients with 65 CXRs were analysed.Results In the derivation cohort, seven patients needed supplemental oxygen and one was intubated for mechanical ventilation with no death. The maximum CRS was significantly different between patients on and not on supplemental oxygen (p=Conclusion Using an objective scoring system (CRS), the degree of abnormalities on CXR correlates closely with known markers of disease severity. CRS may further be applied to predict patients who require oxygen supplementation during the course of their disease.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver malignancies. HCC and ICC have characteristic imaging findings, but a number of benign entities can appear similar and can cause diagnostic dilemma. Ideally, accurate and timely diagnosis of these conditions can help the patient to avoid a needle biopsy or even unnecessary treatment. In this article, we present various benign liver lesions that display imaging characteristics that are similar to HCC and ICC on magnetic resonance imaging (MRI) and discuss salient features that may assist in accurate diagnosis.

The coronavirus disease 2019 (COVID-19) is typically diagnosed by specific assays that detect viral nucleic acid from the upper respiratory tract; however, this may miss infections involving only the lower airways. Computed tomography (CT) has been described as a diagnostic modality in the COVID-19 diagnosis and treatment plan. We present a case series with virologically confirmed COVID-19 pneumonia. Variable CT features were observed: consolidation with ground-glass opacities, ground-glass opacities with subpleural reticular bands, and an anterior-posterior gradient of lung abnormalities resembling that of acute respiratory distress syndrome. Evolution of CT findings was observed in one patient, where there was interval resolution of bilateral lung consolidation with development of bronchiolectasis and subpleural fibrotic bands. While sensitive for detecting lung parenchymal abnormalities in COVID-19 pneumonia, the use of CT for initial diagnosis is discouraged and should be reserved for specific clinical indications. Interpretation of chest CT findings should be correlated with duration of symptoms to better determine the disease stage and aid in patient management. Published version

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.

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the "live" dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift. Agency for Science, Technology and Research (A*STAR) Published version The project is partially supported by A*Star GAP funds ACCL/19-GAP012-R20H and ACCL/19-GAP004-R20H.

Hepatocellular carcinoma (HCC) is a unique tumor because it is one of the few cancers which can be treated based on imaging alone. Magnetic resonance imaging (MRI) carries higher sensitivity and specificity for the diagnosis of HCC than either computed tomography (CT) or ultrasound. MRI is imaging modality of choice for the evaluation of complex liver lesions and HCC because of its inherent ability to depict cellularity, fat, and hepatocyte composition with high soft tissue contrast. The imaging features of progressed HCC are well described. However, many HCC tumors do not demonstrate classical imaging features, posing a diagnostic dilemma to radiologists. Some of these can be attributed to variations in tumor biology and histology, which result in radiological features that differ from the typical progressed HCC. This pictorial review seeks to demonstrate the appearance of different variants of HCC on MRI imaging, in relation to their histopathologic features.

Introduction The reverse shoulder arthroplasty is a common orthopaedic procedure, where placement of the initial guiding wire is paramount to the implant instrumentation and position. To improve the position of the guiding wire, navigation and patient specific instrumentation have been used. These are however expensive and lengthy with many logistical issues. Material and methods We utilised in house 3D printing to create a surgical guide to help with positioning of the central guiding wire. Pre and post op CT scans were utilised to determine positioning of the central screw. Results Position of the screw tip was a mean of 3.3 mm away from the central point of the thickest portion of bone in the scapula with good bony purchase. There were no complications reported. Discussion We report our experience in creation of the 3D printed surgical jig and the pearls of its creation, detailing from CT scan image acquisition to creation of surgical guide to intraoperative usage. 3D printing is a cost effective and accurate solution for the positioning of orthopaedic instrumentation. This can be easily applied to other operations in our institution, even with a low start up cost.