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Introduction Olfactory neuroblastoma (ONB) is a rare malignant tumor of the upper nasal cavity. The Hyams classification is an important histological grading system for diagnosing recurrence and predicting survival in ONB. This study aimed to evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis in distinguishing between high-grade and low-grade ONB based on the Hyams classification system. Methods This retrospective study included 17 patients (11 males, six females; mean age 54 years, range 29-84) diagnosed with ONB who underwent pretreatment magnetic resonance imaging (MRI) including diffusion-weighted imaging between December 2017 and September 2022. Two board-certified radiologists outlined the regions of interest on ADC maps of the tumors. Mean, minimum, maximum ADC, standard deviation, skewness, kurtosis, and entropy were calculated from the ADC histograms. Patients were divided into low-grade (Hyams I-II) and high-grade (Hyams III-IV) groups based on histopathological evaluation by a board-certified pathologist. ADC histogram parameters were compared between the two groups using Mann-Whitney U tests. Two-sided p-values of < 0.05 were considered statistically significant. Results The study included 10 low-grade (two grade I, eight grade II) and seven high-grade (five grade III, one grade III/IV, one grade IV) ONB cases. Comparison between the low-grade and high-grade groups showed no statistically significant differences in any of the ADC histogram parameters analyzed: mean ADC (median 1.02 vs 0.95; p = 0.591), minimum ADC (0.84 vs 0.78; p = 0.494), maximum ADC (1.06 vs 1.19; p = 0.625), standard deviation (0.09 vs 0.14; p = 0.433), skewness (-0.48 vs -0.75; p = 0.133), kurtosis (2.79 vs 3.12; p = 0.161), and entropy (4.69 vs 5.06; p = 0.315). Conclusion This study demonstrated that ADC histogram analysis was unable to differentiate between high-grade and low-grade ONB based on the Hyams classification. The findings suggest that preoperative grading of ONB malignancy using ADC histogram parameters is challenging. Thus, grading based on preoperative imaging evaluation is difficult.
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BACKGROUND: To compare the potential of various bone evaluations by considering photon-counting CT (PCCT) and multiple energy-integrating-detector CT (EIDCT), including three dual-energy CT (DECT) scanners with standardized various parameters in both standard resolution (STD) and ultra-high-resolution (UHR) modes. METHODS: Four cadaveric forearms were scanned using PCCT and five EIDCTs, by applying STD and UHR modes. Visibility of bone architecture, image quality, and a non-displaced fracture were subjectively scored against a reference EIDCT image by using a five-point scale. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also compared. To assess metal artifacts, a forearm with radial plate fixation was scanned by with and without Tin filter (Sn+ and Sn-), and virtual monoenergetic image (VMI) at 120 keV was created. Regarding Sn+ and VMI, images were only obtained from the technically available scanners. Subjective scores and the areas of streak artifacts were compared. RESULTS: PCCT demonstrated significantly lower noise (p < 0.001) and higher bone SNR and CNR (p < 0.001) than all EIDCTs in both resolution modes. However, there was no significant difference between PCCT and EIDCTs in almost all subjective scores, regardless of scan modes, except for image quality where a significant difference was observed, compared to several EIDCTs. Metal artifact analysis revealed PCCT had larger artifact in Sn- and Sn+ (p < 0.001), but fewer in VMIs than three DECTs (p < 0.001 or 0.001). CONCLUSIONS: Under standardized conditions, while PCCT had almost no subjective superiority in visualizing bone structures and fracture line when compared to EIDCTs, it outperformed in quantitative analysis related to image quality, especially in lower noise and higher tissue contrast. When using PCCT to assess cases with metal implants, it may be recommended to use VMIs to minimize the possible tendency for artifact to be pronounced.
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BACKGROUND: A single reverse-transcription polymerase chain reaction (RT-PCR) test is not sufficient to exclude COVID-19 in hospital pre-admission screening. However, repeated RT-PCR tests are time-consuming. This study investigates the utility of chest computed tomography (CT) for COVID-19 screening in asymptomatic patients. METHODS: Between April 2020 and March 2021, RT-PCR testing and chest CT were performed to screen COVID-19 in 10 823 asymptomatic patients prior to admission. Chest CT findings were retrospectively evaluated using the reporting system of the Radiological Society of North America. Using RT-PCR results as a reference, we assessed the diagnostic efficacy of chest CT during both the low- and high-prevalence periods of the COVID-19 pandemic. RESULTS: Following a positive RT-PCR test, 20 asymptomatic patients (0.18%) were diagnosed with COVID-19; in the low-prevalence period, 5 of 6556 patients (0.076%) were positive; and in the high-prevalence period, 15 of 4267 patients (0.35%) were positive. Of the 20 asymptomatic COVID-19 positive patients, chest CT results were positive for COVID-19 pneumonia in 8 patients. Chest CT results were false-positive in 185 patients (1.7% false-positive rate, and 60% false-negative rate). Pneumonia that was classified as a "typical appearance" of COVID-19 reported as false-positives in 36 of 39 patients (92.3%). Across the study period, the diagnostic efficacy of "typical appearance" on chest CT were characterized by a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 15%, 99.7%, 99.7%, 7.7%, and 99.8%; 20%, 99.6%, 99.6%, 4%, and 99.9%; and 13.3%, 99.7%, 99.7%, 14.3%, and 99.7%, in the entire study, low-, and high-prevalence periods, respectively. CONCLUSIONS: Addition of chest CT to RT-PCR testing provides no benefit to the detection of COVID-19 in asymptomatic patients.