Radiology - Research Publications
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ItemNo Preview AvailableExamining the structural correlates of amyloid‐beta in people with DLB(Wiley, 2021-12)Background Dementia with Lewy bodies (DLB) is a neurodegenerative disorder characterized pathologically by the deposition of alpha synuclein. Many patients with DLB also have brain compatible with Alzheimer’s disease (namely Amyloid‐β and tau), which can lead to challenges with clinical diagnosis and management. In this study we aim to understand the influence of Aβ on brain atrophy in DLB patients. Method 19 participants with probable DLB underwent 3T MRI T1‐weighted (voxel size=0.8x0.8x0.8mm3, TR=2400ms, TE=2.31ms) and β‐amyloid (Aβ) PET (radiotracer 18F‐NAV4694) imaging. Participants were grouped into Aβ negative (n=10; age=71.6±5.8 years) and Aβ positive (n=9; age=75.1±4.3 years) with a threshold of 50 centiloid units to identify neuropathological change (Amadoru et al. 2020). Brain volume measures (regional subcortical grey matter and global white and grey matter) were segmented from T1‐weighted images with FreeSurfer (Fischl et al. 2002, Fischl 2012). Given previous literature suggesting prominence of thalamic structural changes in DLB, we also specifically analysed changes in the thalamus by segmenting the thalamus into 25 nuclei, which were then grouped into six regions (anterior, lateral, ventral, intralaminar, medial and posterior) (Watson et al. 2017, Iglesias et al. 2018). All brain volumes were expressed as fractions of intracranial volume to account for differences in head size. Group comparison analyses were not controlled for age and sex as both these covariates did not statistically differ between groups. Result Brain volume differed significantly between Aβ‐ and Aβ+ DLB patients in the left thalamus (Aβ‐:4.39±0.37x103, Aβ+:4.07±0.19x103, p=0.03) and right thalamus (Aβ‐:4.17±0.34x103, Aβ+:3.84±0.22 x103, p=0.03). Specifically, the ventral (LEFT; Aβ‐:1.78±0.15, Aβ+:1.63±0.14, p=0.03. RIGHT; Aβ‐:1.83±0.15, Aβ+:1.65±0.12, p=0.01) and posterior (LEFT; Aβ‐:1.30±0.12, Aβ+:1.17±0.10, p=0.04. RIGHT; Aβ‐:1.42±0.14, Aβ+:1.21±0.12, p=0.003) regions were significantly reduced in Aβ+ compared to Aβ‐ DLB patients. Conclusion We demonstrated significant thalamic atrophy in Aβ+ patients compared to Aβ‐ DLB patients. We did not observe significant differences in grey matter and hippocampal volume between patient groups. This study showed that AD‐related processes in DLB patients are associated with thalamic atrophy, specifically in the ventral and posterior regions. Future studies would benefit a larger DLB cohort to further understand the association between AD‐related pathology and the regional thalamic correlates of clinical function.
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ItemMacroscopic fat containing renal cell carcinoma(WILEY, 2021-12)Renal masses containing macroscopic fat traditionally are pathognomonic for angiomyolipoma, a benign tumour. We describe two cases contrary to this axiom, the first being initially referred for angioembolisation, but subsequently biopsied when it was angiographically occult, whilst the second case showed a small macroscopic fat component and arterial enhancement prompting biopsy. Neither of these two cases demonstrated calcification which would usually suggest a more sinister lesion requiring further workup. The results demonstrated renal cell carcinoma for both lesions. Our multidisciplinary meeting approach to renal masses with a small amount of macroscopic fat and no calcifications has now changed.
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ItemNo Preview AvailableState-Wide Utilization and Performance of Traditional and Cell-Free DNA-Based Prenatal Testing Pathways: The Victorian Perinatal Record Linkage (PeRL) Study(LIPPINCOTT WILLIAMS & WILKINS, 2021-01)(Abstracted from Ultrasound Obstet Gynecol 2020;56:215–224) In recent years, the use of combined first-trimester screening (CFTS) and cell-free DNA (cfDNA) screening has increased. With the rise of CFTS and cfDNA prenatal testing, there has been a dramatic decrease in the number of invasive diagnostic tests performed during pregnancy.
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ItemNo Preview AvailableHealthy Life-Year Costs of Treatment Speed From Arrival to Endovascular Thrombectomy in Patients With Ischemic Stroke A Meta-analysis of Individual Patient Data From 7 Randomized Clinical Trials(AMER MEDICAL ASSOC, 2021-06)IMPORTANCE: The benefits of endovascular thrombectomy (EVT) are time dependent. Prior studies may have underestimated the time-benefit association because time of onset is imprecisely known. OBJECTIVE: To assess the lifetime outcomes associated with speed of endovascular thrombectomy in patients with acute ischemic stroke due to large-vessel occlusion (LVO). DATA SOURCES: PubMed was searched for randomized clinical trials of stent retriever thrombectomy devices vs medical therapy in patients with anterior circulation LVO within 12 hours of last known well time, and for which a peer-reviewed, complete primary results article was published by August 1, 2020. STUDY SELECTION: All randomized clinical trials of stent retriever thrombectomy devices vs medical therapy in patients with anterior circulation LVO within 12 hours of last known well time were included. DATA EXTRACTION/SYNTHESIS: Patient-level data regarding presenting clinical and imaging features and functional outcomes were pooled from the 7 retrieved randomized clinical trials of stent retriever thrombectomy devices (entirely or predominantly) vs medical therapy. All 7 identified trials published in a peer-reviewed journal (by August 1, 2020) contributed data. Detailed time metrics were collected including last known well-to-door (LKWTD) time; last known well/onset-to-puncture (LKWTP) time; last known well-to-reperfusion (LKWR) time; door-to-puncture (DTP) time; and door-to-reperfusion (DTR) time. MAIN OUTCOMES AND MEASURES: Change in healthy life-years measured as disability-adjusted life-years (DALYs). DALYs were calculated as the sum of years of life lost (YLL) owing to premature mortality and years of healthy life lost because of disability (YLD). Disability weights were assigned using the utility-weighted modified Rankin Scale. Age-specific life expectancies without stroke were calculated from 2017 US National Vital Statistics. RESULTS: Among the 781 EVT-treated patients, 406 (52.0%) were early-treated (LKWTP ≤4 hours) and 375 (48.0%) were late-treated (LKWTP >4-12 hours). In early-treated patients, LKWTD was 188 minutes (interquartile range, 151.3-214.8 minutes) and DTP 105 minutes (interquartile range, 76-135 minutes). Among the 298 of 380 (78.4%) patients with substantial reperfusion, median DTR time was 145.0 minutes (interquartile range, 111.5-185.5 minutes). Care process delays were associated with worse clinical outcomes in LKW-to-intervention intervals in early-treated patients and in door-to-intervention intervals in early-treated and late-treated patients, and not associated with LKWTD intervals, eg, in early-treated patients, for each 10-minute delay, healthy life-years lost were DTP 1.8 months vs LKWTD 0.0 months; P < .001. Considering granular time increments, the amount of healthy life-time lost associated with each 1 second of delay was DTP 2.2 hours and DTR 2.4 hours. CONCLUSIONS AND RELEVANCE: In this study, care delays were associated with loss of healthy life-years in patients with acute ischemic stroke treated with EVT, particularly in the postarrival time period. The finding that every 1 second of delay was associated with loss of 2.2 hours of healthy life may encourage continuous quality improvement in door-to-treatment times.
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ItemEffect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study(ELSEVIER, 2021-08)BACKGROUND: Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model. METHODS: In this retrospective study, a deep-learning model was trained on 821 681 images (284 649 patients) from five data sets from Australia, Europe, and the USA. 2568 enriched chest x-ray cases from adult patients (≥16 years) who had at least one frontal chest x-ray were included in the test dataset; cases were representative of inpatient, outpatient, and emergency settings. 20 radiologists reviewed cases with and without the assistance of the deep-learning model with a 3-month washout period. We assessed the change in accuracy of chest x-ray interpretation across 127 clinical findings when the deep-learning model was used as a decision support by calculating area under the receiver operating characteristic curve (AUC) for each radiologist with and without the deep-learning model. We also compared AUCs for the model alone with those of unassisted radiologists. If the lower bound of the adjusted 95% CI of the difference in AUC between the model and the unassisted radiologists was more than -0·05, the model was considered to be non-inferior for that finding. If the lower bound exceeded 0, the model was considered to be superior. FINDINGS: Unassisted radiologists had a macroaveraged AUC of 0·713 (95% CI 0·645-0·785) across the 127 clinical findings, compared with 0·808 (0·763-0·839) when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deep-learning model. Unassisted radiologists had a macroaveraged mean AUC of 0·713 (0·645-0·785) across all findings, compared with 0·957 (0·954-0·959) for the model alone. Model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. INTERPRETATION: This study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice. FUNDING: Annalise.ai.
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ItemDefining primary anal cancer tumour volume on FDG–PET – an initial assessment of semi–automated methods(MedCrave Group, LLC, 2021-01-12)Purpose Clinician inexperience, intra–observer and inter–observer variations in tumour definition may affect staging, radiotherapy target definition, and treatment outcomes, particularly in rare cancers. The purpose of this study was to assess the correlation between semi–automated methods of primary anal cancer (AC) definition and our current clinical standard of manual clinician definition using 18F–FDG–PET imaging and to provide recommendations for clinical use. Methods All patients referred for chemoradiotherapy for AC between 2012 and 2016 were prospectively enrolled, with all 18F–FDG–PET imaging acquired within one year of chemoradiotherapy collected. Three methods of primary AC definition were performed on all PET datasets. Manual definition by an experienced radiologist was considered the clinical standard for comparison of volume and coincidence (Dice coefficient) in our study. Semi–automated techniques assessed included a gradient–based SUV (SUV–gradient) method and a SUV threshold method with a range of thresholds relative to SUVmax (40 (T40), 50 (T50) and 60% (T60)). Results Ten patients were enrolled with 33 PET study sets available for analysis. While all methods created contours on pre– and post–treatment scans, manual definition of PET–avid disease was only necessary on 11 of the 33 study sets. SUV–gradient and T40 defined contours were not statistically different in volume to the clinical standard (p = 0.83 & 0.72 respectively). The observed Dice coefficient relative to the manual clinician contours were 0.75 and 0.73 for the SUV–gradient and T40 methods respectively. Conclusions It is possible to define gross AC using SUV–based methods, with the SUV–gradient–based method followed by the T40 method most closely correlating with our current clinical standard. The SUV–gradient–based method studied is housed within a proprietary clinical system. A semi–automated approach that uses a vendor neutral T40 method and the clinician’s knowledge and skill appears optimal in defining AC. With this approach AC may be defined reliably to enhance efficiencies in radiotherapy and nuclear medicine processes, and to support clinicians in identifying and defining this rare disease. Trial registration ANZCTR, ACTRN12620000066987. Registered 28 January 2020–Retrospectively registered, https://www.anzctr.org.au/ACTRN12620000066987.aspx
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ItemDo comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography(BMJ PUBLISHING GROUP, 2021-12)OBJECTIVES: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset. DESIGN: A retrospective case-control study was undertaken. SETTING: Community radiology clinics and hospitals in Australia and the USA. PARTICIPANTS: A test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoracic radiologists for the presence of simple or tension pneumothorax as well as each subgroup other than positioning. Radiograph positioning was derived from radiographer annotations on the images. OUTCOME MEASURES: DCNN performance for detecting simple and tension pneumothorax was evaluated over the entire test set, as well as within each subgroup, using the area under the receiver operating characteristic curve (AUC). A difference in AUC of more than 0.05 was considered clinically significant. RESULTS: When compared with the overall test set, performance of the DCNN for detecting simple and tension pneumothorax was statistically non-inferior in all subgroups. The DCNN had an AUC of 0.981 (0.976-0.986) for detecting simple pneumothorax and 0.997 (0.995-0.999) for detecting tension pneumothorax. CONCLUSIONS: Hidden stratification has significant implications for potential failures of deep learning when applied in clinical practice. This study demonstrated that a comprehensively trained DCNN can be resilient to hidden stratification in several clinically meaningful subgroups in detecting pneumothorax.
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ItemSuperior mesenteric vein tumour thrombus in a patient with caecal adenocarcinoma: a rare and important finding.(Oxford University Press (OUP), 2021-04-01)Venous tumour thrombosis refers to the invasion of tumour into the venous system. Extramural venous invasion is routinely searched for and reported in rectal carcinoma due to its prognostic significance and influence on staging, prognosis and treatment approach. We describe a case of extramural venous invasion occurring as superior mesenteric vein tumour thrombus in the setting of a caecal carcinoma.
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ItemSomatic IDH1 variant ( p.R132C) in an adult male with Maffucci syndrome(COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT, 2021-12)Maffucci syndrome is a rare, highly variable somatic mosaic condition, and well-known cancer-related gain-of-function variants in either the IDH1 or IDH2 genes have been found in the affected tissues of most reported individuals. Features include benign enchondroma and spindle-cell hemangioma, with a recognized increased risk of various malignancies. Fewer than 200 affected individuals have been reported; therefore, accurate estimates of malignancy risk are difficult to quantify and recommended surveillance guidelines are not available. The same gain-of-function IDH1 and IDH2 variants are also implicated in a variety of other benign and malignant tumors. An adult male presented with several soft palpable lesions on the right upper limb. Imaging and histopathology raised the possibility of Maffucci syndrome. DNA was extracted from peripheral blood lymphocytes and tissue surgically resected from a spindle-cell hemangioma. Sanger sequencing and droplet digital polymerase chain reaction (PCR) analysis of the IDH1 gene were performed. We identified a somatic mosaic c.394C > T (p.R132C) variant in exon 5 of IDH1, in DNA derived from hemangioma tissue at ∼17% variant allele fraction. This variant was absent in DNA derived from blood. This variant has been identified in the affected tissue of most reported individuals with Maffucci syndrome. Although this individual has a potentially targetable variant, and there is a recognized risk of malignant transformation in this condition, a decision was made not to intervene with an IDH1 inhibitor. The reasons and prospects for therapy in this condition are discussed.
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ItemThe inferior vena cava: anatomical variants and acquired pathologies(SPRINGER, 2021-08-30)The inferior vena cava (IVC) is the largest vein in the body, draining blood from the abdomen, pelvis and lower extremities. This pictorial review summarises normal anatomy and embryological development of the IVC. In addition, we highlight a wide range of anatomical variants, acquired pathologies and a common pitfall in imaging of the IVC. This information is essential for clinical decision making and to reduce misdiagnosis.
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