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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.

Objective: This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images. Materials and Methods: The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results: Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion: We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging.

BACKGROUND: Segmentation of the ulna and radius is a crucial step for the measurement of bone mineral density (BMD) in dual-energy X-ray imaging in patients suspected of having osteoporosis. PURPOSE: This work aimed to propose a deep learning approach for the accurate automatic segmentation of the ulna and radius in dual-energy X-ray imaging. METHODS AND MATERIALS: We developed a deep learning model with residual block (Resblock) for the segmentation of the ulna and radius. Three hundred and sixty subjects were included in the study, and five-fold cross-validation was used to evaluate the performance of the proposed network. The Dice coefficient and Jaccard index were calculated to evaluate the results of segmentation in this study. RESULTS: The proposed network model had a better segmentation performance than the previous deep learning-based methods with respect to the automatic segmentation of the ulna and radius. The evaluation results suggested that the average Dice coefficients of the ulna and radius were 0.9835 and 0.9874, with average Jaccard indexes of 0.9680 and 0.9751, respectively. CONCLUSION: The deep learning-based method developed in this study improved the segmentation performance of the ulna and radius in dual-energy X-ray imaging.

Further evidence for Zenker's diverticulum in a slim woman with body figure predisposition: a case report and literature review.

Zenker's diverticulum (ZD) is a bag-like pharyngeal pouch that protrudes to the outside of the pharynx. It is thought to be an acquired disease that occurs following the dysfunction of laryngopharynx muscle, and certain body shapes may be predisposed to this condition. We report a 56-year-old female of slim build with ZD. Computed tomography scanning revealed a hypodense lesion on the left posterior side of her upper esophagus that was filled with air and had no obvious wall. To verify this finding, a barium esophagogram was carried out which showed a round pouch at the level of the 6th cervical vertebral body that communicated with the esophagus through a narrow neck. ZD was subsequently confirmed by endoscopy. These findings provide further evidence in support of a body shape predisposition for ZD.

Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013-2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient's enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733-0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696-0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: - 3.133, P = 0.008), maximum tumor diameter (Z value: - 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659-0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography.

This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Retroperitoneal dendritic cell sarcoma: A case report.

RATIOANLE: Interdigitating dendritic cell sarcoma (IDCS) is a rare sarcoma that originates from interdigitating dendritic cells in lymphoid tissue, the imaging characteristics of which are poorly defined. Pathological examination can identify the tumor, but reports on the imaging characteristics of IDCS are limited. PATIENT CONCERNS: Here, we report a case of IDCS in a 48-year-old female involving the retroperitoneal area. The patient had a lumbar mass on her right lower back for 4 years, and which started increasing in size 1 year before. DIAGNOSES: An irregular soft tissue mass (10.1cm × 8.5 cm in size) in the right lower back of retroperitoneum was detected by CT examination with unclear borders, uneven density, and necrosis. The solid components of the mass were significantly enhanced on postcontrast imaging. The soft tissue was irregular and uneven. Cystic solid masses were observed on MRI examination in the right retroperitoneum, lateral abdominal wall, waist, and back. Necrosis, hemorrhage, and cystic transformation were observed inside the lesion. The cyst wall, separation, and wall nodules were significantly enhanced on the postcontrast image. No distant metastasis was observed. Postoperative pathology confirmed the diagnosis of IDCS. INTERVENTIONS: The patient underwent surgical resection. The resected margin was positive, and the patient received adjuvant radiotherapy 2 months after the surgery. OUTCOMES: Twelve months after radiotherapy, the patient's chest CT showed multiple metastases in both lungs. The patient was started on combination chemotherapy of doxorubicin and ifosfamide, and the follow-up is still ongoing. LESSONS: Imaging provides a unique advantage to determine the extent of the IDCS, the invasion of adjacent tissues, and the presence or absence of distant metastases.

Value of MRI Radiomics Based on Enhanced T1WI Images in Prediction of Meningiomas Grade.

OBJECTIVE: Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade. MATERIALS AND METHODS: A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model. RESULTS: A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948. CONCLUSION: The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.