DFR-U-Net: Dual residual and feature fusion network for ulna and radius segmentation on dual-energy X-ray absorptiometry images.

BACKGROUND: Ulna and radius segmentation of dual-energy X-ray absorptiometry (DXA) images is essential for measuring bone mineral density (BMD). OBJECTIVE: To develop and test a novel deep learning network architecture for robust and efficient ulna and radius segmentation on DXA images. METHODS: This study used two datasets including 360 cases. The first dataset included 300 cases that were randomly divided into five groups for five-fold cross-validation. The second dataset including 60 cases was used for independent testing. A deep learning network architecture with dual residual dilated convolution module and feature fusion block based on residual U-Net (DFR-U-Net) to enhance segmentation accuracy of ulna and radius regions on DXA images was developed. The Dice similarity coefficient (DSC), Jaccard, and Hausdorff distance (HD) were used to evaluate the segmentation performance. A one-tailed paired t-test was used to assert the statistical significance of our method and the other deep learning-based methods (P < 0.05 indicates a statistical significance). RESULTS: The results demonstrated our method achieved the promising segmentation performance, with DSC of 98.56±0.40% and 98.86±0.25%, Jaccard of 97.14±0.75% and 97.73±0.48%, and HD of 6.41±11.67 pixels and 8.23±7.82 pixels for segmentation of ulna and radius, respectively. According to statistics data analysis results, our method yielded significantly higher performance than other deep learning-based methods. CONCLUSIONS: The proposed DFR-U-Net achieved higher segmentation performance for ulna and radius on DXA images than the previous work and other deep learning approaches. This methodology has potential to be applied to ulna and radius segmentation to help doctors measure BMD more accurately in the future.

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.

Objective: To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Method: Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. Results: The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. Conclusions: The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.

Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative.

BACKGROUND: Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge. OBJECTIVE: To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice. METHODS: A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated. RESULTS: A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60]. CONCLUSION: The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.

Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge.

PURPOSE: To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS: Fifty-nine patients (31 males and 28 females ranging from 25 to 70 years old) with confirmed COVID-19 infection performed follow-up thin-section thorax CT. After 31.5±7.9 days (range, 24 to 39 days) of hospital admission, the results of CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups namely, groups A and B (with and without CT evidence of fibrosis), respectively. Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS: Among the 59 patients, 89.8% (53/59) had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Also, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4±16.9 vs. 33.8±10.2 years) (P = 0.001), with longer LOS (19.1±5.2 vs. 15.0±2.5 days) (P = 0.001), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)) (P = 0.061), higher peak C-reactive protein level (30.7±26.4 vs. 18.1±17.9 mg/L) (P = 0.041), and higher maximal CT score (5.2±4.3 vs. 4.0±2.2) (P = 0.06) than those in group B. CONCLUSIONS: Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.

Clinical and computed tomographic (CT) images characteristics in the patients with COVID-19 infection: What should radiologists need to know?

OBJECTIVE: To evaluate the clinical and computed tomographic (CT) features in the patients with COVID-19 pneumonia confirmed by the real-time reverse transcriptase polymerase chain reaction (rRT-PCR) amplification of the viral DNA from a sputum sample. MATERIAL AND METHODS: Clinical information and CT findings of a total of 14 patients with COVID-19 infection (age range, 12-83 years; females 6) were analyzed retrospectively. The clinical information includes the history of exposure, laboratory results, and the symptoms (such as fever, cough, headache, etc.); CT findings of chest include the extension and distribution of lesion, the ground-glass opacity (GGO), consolidation, bronchovascular enlarged, irregular linear appearances, pleural effusion, and lymphadenopathy. RESULTS: Eight patients had the exposure history for recent travel to Wuhan of Hubei province (8/14, 57%), 6 had the exposure to patients with COVID-19 infection. Significant statistical differences were observed in lymphocyte percentage decreased and C-reactive protein elevated (p = 0.015). Seven patients had fever, 7 had cough, 2 had headache, 3 had fatigue, 1 had body soreness, 3 had diarrhea, and 2 had no obvious symptoms. In chest CT examination, 10 patients were positive (10/14, 71.43%). Among these patients, 9 had lesions involving both lungs (9/10, 90%), 8 had lesions involving 4 to 5 lobes (8/10, 80%). Most of lesions were distributed peripherally and the most significant lesions were observed in the right lower lobe in 9 patients (9/10, 90%). Nodules were observed in 5 patients (5/10, 50%); GGO, consolidation, and bronchovascular enlarged were shown in 9 patients (9/10, 90%); irregular linear appearances were revealed in 7 patients (7/10, 70%); and pleural effusions were exhibited in 2 patients (2/10, 20%). Last, no patients showed lymphadenopathy. CONCLUSION: There were some typical CT features for diagnosis of COVID-19 pneumonia. The radiologists should know these CT findings and clinical information, which could help for accurate analysis in the patients with 2019 novel coronavirus infection.

Diagnostic performance on multiple parameters of real-time ultrasound shear wave elastography for evaluating nonalcoholic fatty liver disease: A rabbit model.

OBJECTIVE: To study the diagnostic value of real-time ultrasound shear wave elastography (US-SWE) in evaluating the histological stages of nonalcoholic fatty liver disease (NAFLD) in a rabbit model. MATERIALS AND METHODS: Twenty-one 8-week-old rabbits were fed a high-fat, high-cholesterol diet (experimental groups), and seven rabbits were fed a standard diet (control group). All rabbits underwent real-time US-SWE at various time points to document the histological stages of NAFLD. We categorized the histological stages as normal, NAFL, borderline nonalcoholic steatohepatitis (NASH), and NASH. We measured the elastic modulus of the liver parenchyma and analyzed the diagnostic efficacy of real-time US-SWE using the area under receiver operating characteristic curve (AUC) for the four histological stages. RESULTS: The mean, minimum, and maximum elastic modulus increase for NAFL, borderline NASH, and NASH. For the mean, minimum, and maximum elastic modulus, AUCs are 0.891 (95% confidence interval [CI]: 0.716-0.977), 0.867 (95% CI: 0.686-0.965), and 0.789 (95% CI:0.594-0.919) for differentiating normal liver from liver with NAFLD, respectively; AUCs are 0.846 (95% CI: 0.660-0.954), 0.818 (95% CI: 0.627-0.937), and 0.797 (95% CI:0.627-0.913) for differentiating normal liver or liver with NAFL from liver with borderline NASH or NASH, respectively; AUCs are 0.889 (95% CI: 0.713-0.976), 0.787 (95% CI: 0.591-0.918), and 0.895 (95% CI:0.720-0.978) for differentiating liver with NASH from liver with lower severity NAFLD or normal liver, respectively. CONCLUSIONS: Real-time US-SWE is an accurate, noninvasive technique for evaluating the histological stages of NAFLD by measuring liver stiffness. We recommend using the mean elastic modulus to differentiate the histological stages, with the minimum and maximum elastic modulus as valuable complements.

Quantification of hepatic steatosis in histologic images by deep learning method.

OBJECTIVE: To develop and test a novel method for automatic quantification of hepatic steatosis in histologic images based on the deep learning scheme designed to predict the fat ratio directly, which aims to improve accuracy in diagnosis of non-alcoholic fatty liver disease (NAFLD) with objective assessment of the severity of hepatic steatosis instead of subjective visual estimation. MATERIALS AND METHODS: Thirty-six 8-week old New Zealand white rabbits of both sexes were fed with high-cholesterol, high-fat diet and sacrificed under deep anesthesia at various time points to obtain the pathological specimen. All rabbits were performed by multislice computed tomography for surveillance to measure density changes of liver parenchyma. A deep learning scheme using a convolutional neural network was developed to directly predict the liver fat ratio based on the pathological images. The average error value, standard deviation, and accuracy (error <5%) were evaluated and compared between the deep learning scheme and manual segmentation results. The Pearson's correlation coefficient was also calculated in this study. RESULTS: The deep learning scheme performs successfully on rabbit liver histologic data, showing a high degree of accuracy and stability. The average error value, standard deviation, and accuracy (error <5%) were 3.21%, 4.02%, and 79.10% for the cropped images, 2.22%, 1.92%, and 88.34% for the original images, respectively. The strong positive correlation was also observed for cropped images (R = 0.9227) and original images (R = 0.9255) in comparison to labeled fat ratio. CONCLUSIONS: This new deep learning scheme may aid in the quantification of steatosis in the liver and facilitate its treatment by providing an earlier clinical diagnosis.

NAFLD evaluation: Which is more appropriate, multislice computed tomography or ultrasound real-time shear wave elastography?

OBJECTIVE: This study aims to assess the value of ultrasound real-time shear wave elastography (US-SWE) for evaluation of nonalcoholic fatty liver disease (NAFLD) in a rabbit model compared with multislice computed tomography (MSCT). MATERIAL AND METHODS: Twenty-six rabbits were fed with high-fat, high-cholesterol diet and six rabbits were fed with a standard diet. All rabbits were performed with MSCT and US-SWE at various time points to measure changes in liver parenchyma. The diagnostic efficiency of US-SWE was analyzed using receiver operating characteristics (ROC) curves compared with MSCT based on the liver pathology. RESULTS: The statistically significant differences in the areas under the ROC curves between using MSCT and US-SWE modalities were detected to discriminate between normal vs. NAFLD or higher severity pathology. Similarly, for normal or NAFLD vs. borderline or NASH livers, statistically significant differences between using US-SWE and MSCT modalities were also detected for nonalcoholic steatohepatitis (NASH) vs. lower severity pathology. CONCLUSIONS: MSCT, but not US-SWE, had a better ability to differentiate normal or NAFLD livers from higher severity NAFLD livers. However, the diagnostic efficiency of US-SWE was superior to that of MSCT for differentiating NASH from normal or lower severity NAFLD.

Synchronous Double Primary Malignant Tumors and their Possible Shared Genes: A Rare Clinical Entity.

Objective: This study sought to analyze the 18F-FDG PET/CT and contrast-enhanced computed tomography (CT) images of synchronous colorectal cancer (CRC) and renal clear cell carcinoma (ccRCC) and identify the shared genes between these two types of cancer through bioinformatic analysis. Methods: A retrospective analysis was conducted on a patient with synchronous CRC and ccRCC who underwent 18F-FDG PET/CT and contrast-enhanced CT before treatment. Databases were analyzed to identify differentially expressed genes between CRC and ccRCC, and co-expression genes were extracted for RCC and CRC. Results: 18F-FDG PET/CT revealed intense metabolic activity in the primary colorectal lesion (SUVmax 13.2), while a left renal mass (diameter = 35 mm) was observed with no significant uptake. Contrast-enhanced CT during the arterial phase showed heterogeneous intense enhancement of the renal lesion, and the lesion washed out earlier than in the renal cortex in the nephrographic and excretory phases, indicating ccRCC. The histopathological results confirmed synchronous double primary malignant tumors. Our bioinformatic analysis results showed that synchronous occurrence of CRC and ccRCC may correlate with simultaneous expression of Carbonic Anhydrase 9 (CA9), integrin-binding sialoprotein (IBSP), and Fibrinogen γ chain (FGG). Conclusion: 18F-FDG PET/CT combined with contrast-enhanced CT is an effective diagnostic tool in evaluating synchronous CRC and RCC. By analyzing this clinical case and conducting bioinformatic analysis, we improved our current understanding of the mechanisms underlying synchronous tumors.

Quantifying the natural growth rate of hepatocellular carcinoma: A real-world retrospective study in southwestern China.

BACKGROUND: In recent years, approximately half of the newly diagnosed cases and mortalities attributed to hepatocellular carcinoma (HCC) have been reported in China. Despite the high incidence of HCC, there remains a paucity of data regarding the natural growth pattern and the determination of optimal surveillance intervals specific to the Chinese population. AIM: To quantify the natural tumor growth pattern of HCC in regional China. METHODS: A retrospective analysis was performed on patients from a single institution in Southwest China who had undergone two or more serial dynamic computed tomography or magnetic resonance imaging scans between 2014 and 2020, without having received any anti-cancer therapy. Tumor growth was assessed using tumor volume doubling time (TVDT) and tumor growth rate (TGR), with volumes measured manually by experienced radiologists. Simple univariate linear regression and descriptive analysis were applied to explore associations between growth rates and clinical factors. RESULTS: This study identifies the median TVDT for HCC as 163.4 d, interquartile range (IQR) 72.1 to 302.3 d, with a daily TGR of 0.42% (IQR 0.206%-0.97%). HCC growth patterns reveal that about one-third of tumors grow indolently with TVDT exceeding 270 d, another one-third of tumors exhibit rapid growth with TVDT under 90 d, and the remaining tumors show intermediate growth rates, with TVDT ranging between 3 to 9 months. CONCLUSION: The identified TGRs support biannual surveillance and follow-up for HCC patients in certain regions of China. Given the observed heterogeneity in HCC growth, further investigation is warranted.

A First Report of Thyroid pneumatosis as a Complication of Ultrasound-Guided Thyroid Biopsy.

BACKGROUND: Ultrasound-guided needle biopsies, including fine-needle aspirations (FNA) and core needle biopsies (CNB), have become an effective technique in the evaluation of thyroid nodules. In this report, we discuss the first reported case, to our knowledge, of thyroid pneumatosis after ultrasound-guided FNA. CASE PRESENTATION: A 44-year-old woman underwent ultrasound-guided FNA in other hospitals after thyroid ultrasound revealed a solid lesion in the left lobe classified as TI-RADS 4. Two days later, this female presented to our hospital for an excision of a thyroid mass. Pre- and post-contrast CT scans of the thyroid showed extensive accumulation of gas in the thyroid gland and the retropharyngeal and retrotracheal space. A CT scan of the thyroid two days later revealed obvious absorption of thyroid gas and faint low-density nodules in the left lobe of the thyroid. The lesion was histopathologically confirmed as papillary carcinoma of the thyroid. CONCLUSION: We thought the aforementioned issues originating from the limited imaging capacity of ultrasound in the context of thyroid biopsy. To avoid these limitations, we highlight the need to thoroughly examine the location of a lesion prior to thyroid biopsy to understand in detail the relationship between the lesion and the adjacent tissues, especially the proximity of the lesion to the trachea, the occurrence of coughing during a biopsy (indicating puncture of the trachea) is what operators need to be aware of so that they can manage such cases. On the other hand, we recommend that pre-operative use of CT before thyroid biopsy and especially if CT is needed anyway later for nodules evaluation before surgery to ensure the CT image quality.

Application Potential of Radiomics based on the Unenhanced CT Image for the Identification of Benign or Malignant Pulmonary Nodules.

OBJECTIVE: With the rapid development in computed tomography (CT), the establishment of artificial intelligence (AI) technology and improved awareness of health in folks in the decades, it becomes easier to detect and predict pulmonary nodules with high accuracy. The accurate identification of benign and malignant pulmonary nodules has been challenging for radiologists and clinicians. Therefore, this study applied the unenhanced CT imagesbased radiomics to identify the benign or malignant pulmonary nodules. METHODS: One hundred and four cases of pulmonary nodules confirmed by clinicopathology were analyzed retrospectively, including 79 cases of malignant nodules and 25 cases of benign nodules. They were randomly divided into a training group (n = 74 cases) and test group (n = 30 cases) according to the ratio of 7:3. Using ITK-SNAP software to manually mark the region of interest (ROI), and using AK software (Analysis kit, Version 3.0.0.R, GE Healthcare, America) to extract image radiomics features, a total of 1316 radiomics features were extracted. Then, the minimum-redundancy-maximum-relevance (mRMR) algorithms were used to preliminarily reduce the dimension, and retain the 30 most meaningful features, and then the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal subset of features, so as to establish the final model. The performance of the model was evaluated by using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity and specificity. Calibration refers to the agreement between observed endpoints and predictions, and the clinical benefit of the model to patients was evaluated by decision curve analysis (DCA). RESULTS: The accuracy, sensitivity, and specificity of the training and testing groups were 81.0%, 77.7%, 82.1% and 76.6%, 85.7%, 73.9%, respectively, and the corresponding AUCs were of 0.83 in both groups. CONCLUSION: CT image-based radiomics could differentiate benign from malignant pulmonary nodules, which might provide a new method for clinicians to detect benign and malignant pulmonary nodules.

Diagnostic efficiency on ultrasound shear wave elastography in evaluation of steatosis severity for non-alcoholic fatty liver disease: a rat model.

BACKGROUND: The pathological feature of steatosis affects the elasticity values measured by shear wave elastography (SWE) is still controversial in non-alcoholic fatty liver disease (NAFLD). The aim of this study is to demonstrate the influence of steatosis on liver stiffness measured by SWE on a rat model with NAFLD and analyze feasibility of SWE for grading steatosis in absence of fibrosis. METHODS: Sixty-six rats were fed with methionine choline deficient diet or standard diet to produce various stages of steatosis; 48 rats were available for final analysis. Rats underwent abdominal ultrasound SWE examination and pathological assessment. Liver histopathology was analyzed to assess the degree of steatosis, inflammation, ballooning, and fibrosis according to the non-alcoholic fatty liver disease activity score. The diagnostic performance of SWE for differentiating steatosis stages was estimated according to the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was conducted to determine clinical usefulness and the areas under DCA (AUDCAs) calculated. RESULTS: In multivariate analysis, steatosis was an independent factor affecting the mean elastic modules (B = 1.558, P < 0.001), but not inflammation (B = - 0.031, P = 0.920) and ballooning (B = 0.216, P = 0.458). After adjusting for inflammation and ballooning, the AUROC of the mean elasticity for identifying S ≥ S1 was 0.956 (95%CI: 0.872-0.998) and the AUDCA, 0.621. The AUROC for distinguishing S ≥ S2 and S = S3 was 0.987 (95%CI: 0.951-1.000) and 0.920 (95%CI: 0.816-0.986) and the AUDCA was 0.506 and 0.256, respectively. CONCLUSIONS: Steatosis is associated with liver stiffness and SWE may have the feasibility to be introduced as an assistive technology in grading steatosis for patients with NAFLD in absence of fibrosis.

MDM-U-Net: A novel network for renal cancer structure segmentation.

Accurate segmentation of the renal cancer structure, including the kidney, renal tumors, veins, and arteries, has great clinical significance, which can assist clinicians in diagnosing and treating renal cancer. For accurate segmentation of the renal cancer structure in contrast-enhanced computed tomography (CT) images, we proposed a novel encoder-decoder structure segmentation network named MDM-U-Net comprising a multi-scale anisotropic convolution block, dual activation attention block, and multi-scale deep supervision mechanism. The multi-scale anisotropic convolution block was used to improve the feature extraction ability of the network, the dual activation attention block as a channel-wise mechanism was used to guide the network to exploit important information, and the multi-scale deep supervision mechanism was used to supervise the layers of the decoder part for improving segmentation performance. In this study, we developed a feasible and generalizable MDM-U-Net model for renal cancer structure segmentation, trained the model from the public KiPA22 dataset, and tested it on the KiPA22 dataset and an in-house dataset. For the KiPA22 dataset, our method ranked first in renal cancer structure segmentation, achieving state-of-the-art (SOTA) performance in terms of 6 of 12 evaluation metrics (3 metrics per structure). For the in-house dataset, our method achieves SOTA performance in terms of 9 of 12 evaluation metrics (3 metrics per structure), demonstrating its superiority and generalization ability over the compared networks in renal structure segmentation from contrast-enhanced CT scans.

CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.

Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer.

Tyrosine kinase inhibitors (TKIs) are a significant treatment strategy for the management of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation status. Currently, EGFR mutation status is established based on tumor tissue acquired by biopsy or resection, so there is a compelling need to develop non-invasive, rapid, and accurate gene mutation detection methods. Non-invasive molecular imaging, such as positron emission tomography/computed tomography (PET/CT), has been widely applied to obtain the tumor molecular and genomic features for NSCLC treatment. Recent studies have shown that PET/CT can precisely quantify EGFR mutation status in NSCLC patients for precision therapy. This review article discusses PET/CT advances in predicting EGFR mutation status in NSCLC and their clinical usefulness.

Application of 18F-FDG PET/CT in Langerhans Cell Histiocytosis.

Purpose: This study aims to explore the application value of the 18F-FDG PET/CT imaging in diagnosing, staging, and typing Langerhans cell histiocytosis (LCH) via the morphological and metabolic analyses of the 18F-FDG PET/CT images. Methods: We retrospectively analyzed the 18F-FDG PET/CT images and clinical data of nineteen patients with LCH. The shape, size, density, distribution, and 18F-FDG uptake of all lesions were documented. In addition, the SUVmax of the lesions, liver, and blood pool was measured prior to calculating the lesion-to-liver and lesion-to-blood pool ratios. Results: Among the 19 analyzed patients, the positive rate of the PET/CT image was 94.7% (18/19), with 1 false negative (5.3%, 1/19) case occurring in the cutaneous LCH. Among the 76 lesions, 69 were FDG-avid lesions (69/76, 90.8%). Additionally, we observed no FDG uptake in 7 lesions (7/76, 9.2%). In contrast, 59 lesions (59/76, 77.6%) were abnormal on diagnostic CT scan, but 17 lesions (17/76, 22.4%) were undetected. The 18F-FDG PET/CT image revealed additional 6 lesions in the bone, 4 in the lymph node, 3 in the spleen, and 3 occult lesions, which CT scan did not detect. Additionally, there were 6 cases with single-system LCH. The remaining 13 cases were multisystem LCH. Our 18F-FDG PET/CT image analyses altered the typing of 4 LCH patients. In the case of all lesions, the mean SUVmax of the 18F-FDG-avid lesions was 5.4 ± 5.1 (range, 0.8∼26.2), and the mean lesion-to-liver SUVmax ratio was 3.1 ± 2.52 (range, 0.7∼11.9), and the mean lesion-to-blood pool SUVmax ratio was 4.6 ± 3.4 (range 0.7∼17.5). Conclusion: The 18F-FDG PET/CT image plays an essential role in LCH diagnosis, primary staging, and typing. It can accurately evaluate the distribution, range, and metabolic information of LCH, providing a vital imaging basis for the clinical evaluation of disease conditions, selection of treatment schemes, and determining patient prognosis.

Case report: Hepatic tuberculosis mimicking hepatocellular carcinoma in a patient with cirrhosis induced by hepatitis B virus.

Hepatic tuberculosis (TB), which is secondary to post-hepatitis B cirrhosis, is extremely rare. We report the case of a 69-year-old man with post-hepatitis B cirrhosis complicated by primary isolated hepatic TB who was initially misdiagnosed as having hepatocellular carcinoma (HCC). The patient was hospitalized with complaints of 2 weeks of fever. He had a 20-year history of post-hepatitis B cirrhosis. The laboratory tests suggested that his serum alpha-fetoprotein (AFP) level was markedly elevated to 1210 ng/ml. From the abdominal ultrasound (US) and magnetic resonance imaging (MRI) images, we confirmed the presence of cirrhosis and discovered a space-occupying lesion of the hepatic left lobe as well as portal vein-filling defects. These results led us to consider primary liver cancer and portal vein tumor thrombus combined with decompensated cirrhosis. Biopsy and histology may be considered the ultimate diagnostic tests, but we excluded needle biopsy because of his high risk of bleeding, in addition, the patient declined the procedure. To cope with his fever, the patient was given broad-spectrum antibiotic treatment initially, followed by intravenous vancomycin. After antibiotic treatment had failed, the patient was treated with anti-TB for 10 days; after that, the patient maintained a normal temperature. The patient continued to receive tuberculostatic therapy for 6 months following his discharge. AFP completely returned to the normal level, and the aforementioned mass disappeared. Finally, hepatic TB secondary to post-hepatitis B cirrhosis with portal vein thrombosis (PVT) was considered to be the final diagnosis. More than two imaging techniques discover a space-occupying liver lesion and that the serum alpha-fetoprotein (AFP) level is extremely elevated, which means that hepatocellular carcinoma (HCC) could be diagnosed. However, some exceedingly rare diseases should not be excluded. This case illustrated that the non-invasive diagnostic criteria for liver cancer should be considered carefully when discovering a space-occupying liver lesion in a patient with cirrhosis and an elevated AFP level. In addition, primary hepatic TB should be considered and included in the differential diagnosis, and a biopsy should be performed promptly.