Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search

Radiology reports contain information that can be mined using a search engine for teaching, research, and quality assurance purposes. Current search engines look for exact matches to the search term, but they do not differentiate between reports in which the search term appears in a positive context (i.e., being present) from those in which the search term appears in the context of negation and uncertainty. We describe RadReportMiner, a context-aware search engine, and compare its retrieval performance with a generic search engine, Google Desktop. We created a corpus of 464 radiology reports which described at least one of five findings (appendicitis, hydronephrosis, fracture, optic neuritis, and pneumonia). Each report was classified by a radiologist as positive (finding described to be present) or negative (finding described to be absent or uncertain). The same reports were then classified by RadReportMiner and Google Desktop. RadReportMiner achieved a higher precision (81%), compared with Google Desktop (27%; p < 0.0001). RadReportMiner had a lower recall (72%) compared with Google Desktop (87%; p = 0.006). We conclude that adding negation and uncertainty identification to a word-based radiology report search engine improves the precision of search results over a search engine that does not take this information into account. Our approach may be useful to adopt into current report retrieval systems to help radiologists to more accurately search for radiology reports

Epithelioid hemangioma of the spine: Two cases

We report two cases of epithelioid hemangioma (EH) manifested in the thoracic spine with associated clinical, radiographic, and pathological findings. Epithelioid hemangioma is a benign vascular tumor that can involve any bone (including the spine in a subset of patients). Although recognized as a benign tumor by the WHO, it can display locally aggressive features. Within the spine, these features may lead to pain, instability, and/or neurologic dysfunction. The radiographic appearance is most typically that of a lytic, well-defined lesion on plain film or CT. The MRI appearance is typically hypointense on T1WI, hyperintense on T2WI, and avidly enhancing, often with an extraosseous soft-tissue component

Role of Damage-Associated Molecular Pattern/Cell Death Pathways in Vaccine-Induced Immunity

Immune responses induced by natural infection and vaccination are known to be initiated by the recognition of microbial patterns by cognate receptors, since microbes and most vaccine components contain pathogen-associated molecular patterns. Recent discoveries on the roles of damage-associated molecular patterns (DAMPs) and cell death in immunogenicity have improved our understanding of the mechanism underlying vaccine-induced immunity. DAMPs are usually immunologically inert, but can transform into alarming signals to activate the resting immune system in response to pathogenic infection, cellular stress and death, or tissue damage. The activation of DAMPs and cell death pathways can trigger local inflammation, occasionally mediating adaptive immunity, including antibody- and cell-mediated immune responses. Emerging evidence indicates that the components of vaccines and adjuvants induce immunogenicity via the stimulation of DAMP/cell death pathways. Furthermore, strategies for targeting this pathway to enhance immunogenicity are being investigated actively. In this review, we describe various DAMPs and focus on the roles of DAMP/cell death pathways in the context of vaccines for infectious diseases and cancer

Targeting a Lipid Desaturation Enzyme, SCD1, Selectively Eliminates Colon Cancer Stem Cells through the Suppression of Wnt and NOTCH Signaling

The elimination of the cancer stem cell (CSC) population may be required to achieve better outcomes of cancer therapy. We evaluated stearoyl-CoA desaturase 1 (SCD1) as a novel target for CSC-selective elimination in colon cancer. CSCs expressed more SCD1 than bulk cultured cells (BCCs), and blocking SCD1 expression or function revealed an essential role for SCD1 in the survival of CSCs, but not BCCs. The CSC potential selectively decreased after treatment with the SCD1 inhibitor in vitro and in vivo. The CSC-selective suppression was mediated through the induction of apoptosis. The mechanism leading to selective CSC death was investigated by performing a quantitative RT-PCR analysis of 14 CSC-specific signaling and marker genes after 24 and 48 h of treatment with two concentrations of an inhibitor. The decrease in the expression of Notch1 and AXIN2 preceded changes in the expression of all other genes, at 24 h of treatment in a dose-dependent manner, followed by the downregulation of most Wnt- and NOTCH-signaling genes. Collectively, we showed that not only Wnt but also NOTCH signaling is a primary target of suppression by SCD1 inhibition in CSCs, suggesting the possibility of targeting SCD1 against colon cancer in clinical settings

Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform’s low inter-user variability and the high accuracy of its T1c and FLAIR AI models