Purpose: To measure diagnostic accuracy of fracture detection, visual accommodation, reading time, and subjective ratings of fatigue and visual strain before and after a day of clinical reading. Methods:Forty attending radiologists and radiology residents viewed 60 de-identified HIPAA compliant bone examinations, half with fractures, once before any clinical reading (Early) and once after a day of clinical reading (Late). Reading time was recorded. Visual accommodation (ability to maintain focus) was measured before and after each reading session. Subjective ratings of symptoms of fatigue and oculomotor strain were collected. The study was approved by local IRBs.Results: Diagnostic accuracy was reduced significantly after a day of clinical reading, with average receiver operating characteristic (ROC) area under the curve (AUC) of 0.885 for Early reading and 0.852 for Late reading (p < 0.05). After a day of image interpretation, visual accommodation was no more variable, though error in visual accommodation was greater (p < 0.01) and subjective ratings of fatigue were higher. Conclusions:After a day of clinical reading, radiologists have reduced ability to focus, increased symptoms of fatigue and oculomotor strain, and reduced ability to detect fractures. Radiologists need to be aware of the effects of fatigue on diagnostic accuracy and take steps to mitigate these effects.

The incidence of spinal infections has increased in the past two decades, owing to the increasing number of elderly patients, immunocompromised conditions, spinal surgery and instrumentation, vascular access and intravenous drug use. Conventional MRI is the gold standard for diagnostic imaging; however, there are still a significant number of misdiagnosed cases. Diffusion-weighted imaging (DWI) with a b-value of 1000 and apparent diffusion coefficient (ADC) maps provide early and accurate detection of abscess and pus collection. Pyogenic infections are classified into four types of extension based on MRI and DWI findings: (1) epidural/paraspinal abscess with spondylodiscitis, (2) epidural/ paraspinal abscess with facet joint infection, (3) epidural/paraspinal abscess without concomitant spondylodiscitis or facet joint infection and (4) intradural abscess (subdural abscess, purulent meningitis and spinal cord abscess). DWI easily detects abscesses and demonstrates the extension, multiplicity and remote disseminated infection. DWI is often a key image in the differential diagnosis. Important differential diagnoses include epidural, subdural or subarachnoid haemorrhage, cerebrospinal fluid leak, disc herniation, synovial cyst, granulation tissue, intra-or extradural tumour and post-surgical fluid collections. DWI and the ADC values are affected by susceptibility artefacts, incomplete fat suppression and volume-averaging artefacts. Recognition of artefacts is essential when interpreting DWI of spinal and paraspinal infections. DWI is not only useful for the diagnosis but also for the treatment planning of pyogenic and nonpyogenic spinal infections.Spinal and paraspinal infections include vertebral osteomyelitis, spondylodiscitis, infectious facet arthropathy, epidural infections, meningitis, myelitis and infections of paraspinal soft tissue and musculature. Evidence of spinal infections has been discovered in the remains of prehistoric humans from 7000 BC. 1 The incidence has increased in the past two decades, owing to the rising number of elderly patients, immunocompromised conditions, spinal surgery and instrumentation, vascular access and intravenous drug use.1-4 Despite advances in medical knowledge, imaging modalities and surgical interventions, the diagnosis of this entity is still challenging since the clinical features can be subtle and misleading. MRI including post-contrast studies is the gold standard for diagnostic imaging. However, although MRI has relatively high diagnostic sensitivity, specificity and accuracy, there are still a significant number of challenging cases. In such cases, diagnostic delays and suboptimal management can result in irreversible paralysis, critical sepsis and even death.Diffusion-weighted imaging (DWI) has proven to be a useful tool for the diagnosis of a variety of intracranial infections especially in the detection of brain abscesses and pus collections, which encompass subdural and epidural empyema, purulent meningitis and ventriculitis. [5][6][7][8] Therefore, DWI ...

The S100A9/EGFR level is a novel prognostic marker to predict the chemoresponsiveness of patients with locally recurrent or metastatic MIBC.

PurposeAccurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation.MethodsCT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours.ResultsThe median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries.ConclusionSemi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.