Diagnostic performance of MRI for the assessment of extraocular muscle invasion in malignant sinonasal tumors.

PURPOSE: Preoperative assessment of extraocular muscle invasion is essential for therapeutic strategies and prognostic evaluation. The aim of this study was to assess the diagnostic accuracy of MRI for evaluation of extraocular muscle (EM) invasion by malignant sinonasal tumors. MATERIALS AND METHODS: Consecutively, 76 patients of sinonasal malignant tumors with orbital invasion were included in the present study. Preoperative MRI imaging features were analyzed by two radiologists independently. The diagnostic performances of MR imaging features for detecting EM involvement were evaluated by comparing imaging findings to histopathology data. RESULTS: A total of 31 extraocular muscles were involved by sinonasal malignant tumors in 22 patients, including 10 medial rectus muscles (32.2%), 10 inferior rectus muscles (32.2%), 9 superior oblique muscles (29.1%), and 2 external rectus muscles (6.5%). The EM involved by sinonasal malignant tumors usually showed relatively high signal intensity on T2-weighted images, indistinguishable from the tumor, nodular enlargement and abnormal enhancement (p = 0.001, < 0.001, < 0.001 and < 0.001, respectively). Using a combination of EM abnormal enhancement and indistinguishable from the tumor in multivariate logistic regression analysis, sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for detecting orbital EM invasion by sinonasal tumors were 93.5, 85.2, 76.3, 96.3 and 88%, respectively. CONCLUSION: MRI imaging features show high diagnostic performance for the diagnosis of extraocular muscle invasion by malignant sinonasal tumors.

Novel compound heterozygous CPLANE1 variants identified in a Chinese family with Joubert syndrome.

Joubert syndrome (JS) and JS-related disorders (JSRD) are a group of neurodevelopmental diseases that share the "molar tooth sign" on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype-phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six-person family, and the pathogenic variants were detected by whole-exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1: c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1, which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.

Disease Prediction via Graph Neural Networks.

With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, patient demographics, etc.) to the estimated diseases for each patient. However, existing machine learning-based solutions heavily rely on abundant manually labeled EMR training data to ensure satisfactory prediction results, impeding their performance in the existence of rare diseases that are subject to severe data scarcity. For each rare disease, the limited EMR data can hardly offer sufficient information for a model to correctly distinguish its identity from other diseases with similar clinical symptoms. Furthermore, most existing disease prediction approaches are based on the sequential EMRs collected for every patient and are unable to handle new patients without historical EMRs, reducing their real-life practicality. In this paper, we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs. By aggregating information from directly connected neighbor nodes, the proposed neural graph encoder can effectively generate embeddings that capture knowledge from both data sources, and is able to inductively infer the embeddings for a new patient based on the symptoms reported in her/his EMRs to allow for accurate prediction on both general diseases and rare diseases. Extensive experiments on a real-world EMR dataset have demonstrated the state-of-the-art performance of our proposed model.

Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation.

OBJECTIVES: To investigate the impact of deep learning (DL) on radiologists' detection accuracy and reading efficiency of rib fractures on CT. METHODS: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3. RESULTS: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1. CONCLUSIONS: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture. ADVANCES IN KNOWLEDGE: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.

Diagnostic utility of diffusion-weighted magnetic resonance imaging in two common renal tumors.

The aim of the present study was to evaluate the utility of diffusion-weighted magnetic resonance imaging (DWI) in the diagnosis of common renal tumors. Conventional magnetic resonance imaging and DWI were performed on 85 patients with renal lesions (54 renal carcinoma and 31 renal angiomyolipoma cases). The apparent diffusion coefficient (ADC) values in each case at b=800 sec/mm2 were measured in the ADC maps using a statistical software package. The 54 cases of renal cell carcinoma showed a high signal intensity in the parenchyma, and the 31 renal angiomyolipoma cases showed a well-defined mixed signal intensity on DWI. The soft-tissue component showed a high signal intensity and the fat tissue showed a low signal intensity on DWI. When the b-value was set to 800 sec/mm2, the mean ADC was significantly lower in the renal carcinoma cases than in the renal angiomyolipoma cases. In conclusion, the measurement of ADC on DWI can reveal the structure of renal tumors, which is beneficial in diagnosing and determining the prognosis of benign and malignant renal tumors.

Formation of core-shell structured composite microparticles via cyclic gas-solid reactions.

This work reports a novel low-cost and environmental-friendly preparation strategy for core-shell structured composite microparticles and discusses its formation mechanism. Different from most conventional strategies, which involve coating or coating-like processes, this reported strategy uses irreversible solid-phase ionic diffusion in a gas-solid reaction cycle (e.g., reduction and oxidation of Fe) to gradually move the shell material from a core-and-shell material mixture microparticle to the surface. Without the need for solvent as do many conventional processes, this novel process only involves gas-solid reactions, which reduces environmental impact. To substantiate this conceived strategy, a micrometer-sized microparticle made up of a mixture of Fe2O3 and Al2O3 powders is first reduced by H2 and then oxidized by O2 over 50 cycles at 900 °C. These reactions are known to proceed mainly through the diffusion of solid-phase Fe cations. SEM and EDX analyses verify the formation of an Al2O3 core-Fe2O3 shell structure at the end of the 50 reaction cycles. If the cyclic reactions of a microparticle proceed mainly through the diffusion of gaseous-reactant-derived O anions such as the mixture of Fe2O3 and TiO2 instead of solid-phase Fe cation diffusion, no formation of the core-shell structure is observed in the resulting microparticle. These two opposing results underscore the dominating role of solid-phase ionic diffusion in the formation of the core-shell structure. A 2-D continuum diffusion model is applied to account for the inter-Fe-particle bridging and directional product layer growth phenomena during an oxidation reaction. The simulation further verifies the conceived core-shell formation strategy.

Reactive solid surface morphology variation via ionic diffusion.

In gas-solid reactions, one of the most important factors that determine the overall reaction rate is the solid morphology, which can be characterized by a combination of smooth, convex and concave structures. Generally, the solid surface structure varies in the course of reactions, which is classically noted as being attributed to one or more of the following three mechanisms: mechanical interaction, molar volume change, and sintering. Here we show that if a gas-solid reaction involves the outward ionic diffusion of a solid-phase reactant then this outward ionic diffusion could eventually smooth the surface with an initial concave and/or convex structure. Specifically, the concave surface is filled via a larger outward diffusing surface pointing to the concave valley, whereas the height of the convex surface decreases via a lower outward diffusion flux in the vertical direction. A quantitative 2-D continuum diffusion model is established to analyze these two morphological variation processes, which shows consistent results with the experiments. This surface morphology variation by solid-phase ionic diffusion serves to provide a fourth mechanism that supplements the traditionally acknowledged solid morphology variation or, in general, porosity variation mechanisms in gas-solid reactions.

Theoretical study on the motion of a La atom inside a C82 cage.

The motion of a single lanthanum atom inside a C82 (C2v) fullerene cage has been investigated by means of the hybrid density functional method (B3LYP). The obtained potential energy surface (PES) suggests that the encapsulated La atom can oscillate only around the minimum energy potential well, which is apparently different from the scenario of a giant bowl-shaped movement at room temperature described by Nishibori et al. (Nishibori, E.; Takata, M.; Sakata, M.; Tanaka, H.; Hasegawa, M.; Shinohara, H. Chem. Phys. Lett. 2000, 330, 497-502.) Interestingly, our calculations show that the La atom may probably undergo a boat-shaped movement when the temperature is high enough. In addition, the computed 13C NMR spectrum of the C2v [La@C82]- is in an excellent agreement with the experimental nuclear magnetic resonance (NMR) spectrum (Tsuchiya, T.; Wakahara, T.; Maeda, Y.; Akasaka, T.; Waelchli, M.; Kato, T.; Okubo, H.; Mizorogi, N.; Kobayashi, K.; Nagase, S. Anew. Chem. 2005, 117, 3346-3349), which confirms that the isomer of La@C82 with the C2v symmetry is the most stable.