Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Background: Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods: A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results: LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions: Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Tislelizumab combined with GT chemotherapy for intimal sarcoma of inferior vena cava: A case report.

RATIONALE: Intimal sarcoma of inferior vena cava (IVC) is a rare soft tissue sarcoma with no typical symptoms and specific imaging features in the early stage, and there is a lack of standardized treatment and methods. PATIENT CONCERNS: A 54-year-old female patient presented to Fenghua District People's Hospital with a post-active cough and hemoptysis and was subsequently referred to our hospital. DIAGNOSES: The patient was pathologically diagnosed as intimal sarcoma of IVC complicating multiple intrapulmonary metastases. Chest CT revealed left lung malignant tumor with multiple intrapulmonary metastases; while enhanced upper abdominal CT showed cancer embolus of IVC with extension to right atrium and bilateral renal veins. Besides, hematoxylin and eosin staining suggested intimal sarcoma of veins. Immunohistochemical staining showed positivity for PD-L1, Ki-67, CD31, Desmin and ERG. INTERVENTIONS: The patient initially received GT chemotherapy (gemcitabine injection + docetaxel). Then, immunotherapy (tislelizumab) was added based on the results of genetic testing (TP53 gene mutation). OUTCOMES: The disease was stabilized after receiving the treatment. LESSONS: Given the lack of characteristic clinical manifestations in patients with intimal sarcoma of IVC, imaging examination combined with immunohistochemical index were helpful for diagnosis of intimal sarcoma of IVC. Furthermore, the combination of tislelizumab and GT chemotherapy was feasible in such patients with positive PD-L1 expression and TP53 mutation.

A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors.

BACKGROUND: The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diagnostic features, indicating precise tumor segmentation is essential. Therefore, we build a CT image segmentation method based on an encoder-decoder structure combined with a Transformer for volumetric segmentation of adrenal tumors. METHODS: This study included a total of 182 patients with adrenal metastases, and an adrenal tumor volumetric segmentation method combining encoder-decoder structure and Transformer was constructed. The Dice Score coefficient (DSC), Hausdorff distance, Intersection over union (IOU), Average surface distance (ASD) and Mean average error (MAE) were calculated to evaluate the performance of the segmentation method. RESULTS: Analyses were made among our proposed method and other CNN-based and transformer-based methods. The results showed excellent segmentation performance, with a mean DSC of 0.858, a mean Hausdorff distance of 10.996, a mean IOU of 0.814, a mean MAE of 0.0005, and a mean ASD of 0.509. The boxplot of all test samples' segmentation performance implies that the proposed method has the lowest skewness and the highest average prediction performance. CONCLUSIONS: Our proposed method can directly generate 3D lesion maps and showed excellent segmentation performance. The comparison of segmentation metrics and visualization results showed that our proposed method performed very well in the segmentation.

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method.

Objectives: The purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features. Methods: In this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features. Results: Cystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927-0.973) in the training set and 0.91 (95% CI: 0.860-0.929) in the external validation set. Conclusion: A reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method.

Risk stratification of 2- to 5-cm gastric stromal tumors based on clinical and computed tomography manifestations.

OBJECTIVE: To evaluate the risk stratification of 2- to 5-cm gastric stromal tumors (GSTs) by analyzing their clinical and computed tomography (CT) manifestations with the goal of providing imaging evidence for rational selection of surgical methods. METHODS: This study involved 223 patients with pathologically diagnosed GSTs of 2 to 5 cm in diameter. According to the pathological results and malignant risk category, the patients were divided into a low-risk biological behavior group (very low and low risk) and high-risk biological behavior group (intermediate and high risk). The clinical and CT manifestations were compared between the groups. The chi-square test was used to analyze categorical variables, and the independent-samples t test was used to analyze continuous variables. Multivariate logistic regression and receiver operating characteristic curve analysis were performed for statistically significant variables. RESULTS: The tumor contour, necrosis, surface ulceration, and long diameter were significantly different between the low-risk group and the high-risk group (P < 0.05). Multivariate logistic regression analysis showed that the tumor contour and long diameter were independent risk factors. The area under the curve was 0.82, and the accuracy, sensitivity, and specificity were 0.78, 77.4 %, and 79.7 %, respectively. CONCLUSIONS: The risk associated with 2- to 5-cm GSTs can be preoperatively predicted in an indirect manner through analysis of clinical and CT manifestations, and this model has high diagnostic value.

Case report: Epidural blood patches are effective in treating intracranial hypotension due to a subarachnoid-pleural fistula.

Background: Intracranial hypotension (IH) is usually associated with cerebrospinal fluid (CSF) leakage and/or CSF hypotension, and epidural blood patch (EBP) therapy has been proven to be effective for treating spontaneous IH and post-dural puncture headaches. Tarlov cysts (TCs) are common lesions of the sacral spine. They have rarely been reported in thoracic locations and are even less common in the posterior mediastinum, which can lead to their misdiagnosis as neurogenic tumors. Case presentation: Here, we report the case of a 60-year-old woman who developed an orthostatic headache after the thoracoscopic resection of a TC in the posterior mediastinum that was presumed to be a schwannoma preoperatively. The patient was finally diagnosed with IH caused by a subarachnoid-pleural fistula (SPF) and was cured by targeted EBP treatment. Conclusion: This is a case to show that a single targeted EBP treatment is effective for a patient with IH caused by an SPF after thoracoscopic resection of a TC. This case reminds us to be vigilant that a TC may be masquerading as a posterior mediastinal neurogenic tumor, and a detailed examination should be performed to identify it before deciding on a surgical procedure. In addition, postural headache after thoracoscopic spinal surgery should be alert to the possibility of IH induced by an SPF. Once it occurs, early treatment is necessary, and targeted EBP treatment can serve as a safe and effective alternative when conservative treatment fails.

Undifferentiated carcinoma of the pancreas with osteoclast like giant cells: Literature review with CT/MR imaging findings in 3 cases.

Undifferentiated carcinoma with osteoclast-like giant cells of pancreas (UCOGCP) is a relatively rare tumor worldwide. Its accurate preoperative diagnosis is extremely difficult. Because the mass is usually large and closely related to neighboring structures, it is difficult to locate the tumor and it is often misdiagnosed as pancreatic cancer, neuroendocrine tumor or gastrointestinal stromal tumor. Combining literature to analyze UCOGCP clinical features (including age of onset, prevalent location) and imaging features (including lesion size, mass nature), to explore the value of preoperative CT and MRI in the diagnosis and differential diagnosis of UCOGCP and hope to help clinical diagnosis and treatment.

Comparative study of the image quality of twin beam dual energy and single energy carotid CT angiography.

PURPOSE: The aim of this study was to compare the image quality of low virtual monochromatic image (VMI) from the twin-beam dual energy (TBDE) mode to that from the single energy (SE) mode using both standard and low tube voltage acquisition in carotid computed tomography angiography (CTA). METHODS: In this prospective study, 221 patients were imaged using TBDE mode with 120 kV or SE mode with 80/90/120 kV, patient numbers in all groups are equal(n = 55) except in the group using 80 kV SE mode(n = 56). VMIs ranging from 40 to 90 keV with a step of 1 keV were reconstructed from TBDE mode. In objective image quality assessment, regions of interest (ROIs) were placed in both carotid artery lumens and in both sternocleidomastoid muscles to calculate and compare the contrast-to-noise ratio (CNR) and the dose-normalized CNR (CNRD) among groups. In subjective assessment, a total of 13 arterial segments were assessed using a four-point Likert scale by two observers. Cohen's kappa test was used to quantify the level of agreement between the two observers. RESULTS: For ROI1 at the level of the carotid bifurcation, VMIs showed a higher CNR than the 120 kV SE group (p = 0.028)/ 90 kV SE group (p = 0.037) when their energy level were lower than 79 keV (56.92 ± 16.01)/56 keV (90.08 ± 22.14) respectively. The 90 kV SE (80.68 ± 24.47) showed the best CNR in all the SE groups. For CNRD, the 120/90/80 kV SE group was equivalent to 83/63/67 keV VMIs respectively. For ROI2 at the level of the origin of the common carotid artery, VMIs also showed a higher CNR than the 120 kV SE group (p = 0.015)/90 kV SE group (p = 0.034) when their energy level were lower than 83 keV (46.31 ± 14.47)/60 keV (72.23 ± 16.96) respectively. The 90 kV SE (64.98 ± 18.51) showed the best CNR in all the SE groups. For CNRD, the 120/90/80 kV SE group was equivalent to 88/72/75 keV VMIs respectively. The highest subjective rating score was 50 keV TBDE(3.70 ± 0.53). Cohen's Kappa values(0.79-0.85) suggest a substantial level of agreement between the two observers. CONCLUSIONS: The novel TBDE technique with low keV VMI reconstruction provides better image quality of carotid CTA than the low tube voltage scan in the SE mode. VMIs with a keV level below 56 keV have a higher CNR than those from SE scans with 80/90/120 kV. Subjectively, the optimal keV energy level in TBDE for carotid CT angiography is 50 keV.

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors.

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

A Novel Nutrition-Based Nomogram to Predict Prognosis After Curative Resection of Gastric Cancer.

Objective: We sought to investigate the prognostic significance of body composition and weight change during the first 6 months of adjuvant chemotherapy after R0 resection and develop novel nomograms to accurately predict relapse-free survival (RFS) and overall survival (OS). Methods: This retrospective study included 190 patients who underwent curative radical gastrectomy for gastric cancer and received adjuvant chemotherapy. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) were analyzed for 6 months. LASSO Cox regression and multivariate Cox regression were conducted to evaluate other clinical characteristics, which were used to construct a nomogram for the prediction of 3- and 5-year RFS and OS. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms, non-nutritional nomograms, and pTNM stage. Results: The median follow-up duration was 42.0 (25.2-55.1) months. Factors included in the newly-built nomogram for RFS were pT stage, pN stage, tumor site, tumor size, nerve invasion or not, surgery type, and change of L3SMI, while factors included in the nomogram for OS were pT stage, pN stage, tumor size, nerve invasion or not, surgery type, and change of L3SMI. The C-index and t-ROC indicated that our newly-built nomograms had greater potential to accurately predict prognosis than the non-nutritional nomograms and pTNM stage system. Besides, oral nutritional supplements can reduce the degree of weight and L3SMI loss. Conclusion: Change in skeletal muscle mass during adjuvant chemotherapy can be incorporated into predictive prognostic nomograms for RFS and OS in GC patients after radical resection. Dynamic changes in body composition and weight during adjuvant chemotherapy contribute to the early detection of poor outcomes.

Evaluation of Modified Calcium Removal Algorithm in dual energy CT of Internal Carotid Artery.

PURPOSE: To evaluate the performance of a dual-energy (DE) calcium removal software based on a modified three-material decomposition algorithm in assessing the stenosis of the internal carotid artery (ICA) in comparison with mixed images using digital subtraction angiography (DSA) as the reference standard. METHODS: Forty-six patients (38 men; 67±8 years old), including 154 calcified ICA segments C1-C2 (59), C3-C5 (63), C6 (24), and C7 (8), were recruited in this retrospective study. Mixed images and virtual non-calcium (VNCa) images using the modified dual-energy computed tomography (DECT) algorithm were reconstructed. The differences between VNCa and DSA images vs. mixed and DSA images of degree of stenosis were compared. The intraclass correlation coefficient (ICC) was used for assessing the agreement between VNCa, mixed images, and DSA. RESULTS: The degree of stenosis differed significantly between mixed and DSA images in the C3-C5 (30%±17.9% vs. 23.0%±16.9%, p = 0.026) and C6 (38.3%±15.4% vs. 28.5%±13.3%, p = 0.023) segments. The stenosis of VNCa images showed no significant difference with DSA images in all segments (all p > 0.05). The ICCs between VNCa and DSA images (0.86-0.97) were higher than those between the mixed and DSA images (0.68-0.96) in all segments. CONCLUSION: The performance of a modified three-material decomposition DECT algorithm for calcium removal in ICA stenosis evaluation, particularly for the C3-C5 and C6 ICA segments, was promising.

Accurate Measurement of Agatston Score Using kVp-Independent Reconstruction Algorithm for Ultra-High-Pitch Sn150 kVp CT.

OBJECTIVE: To investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose. MATERIALS AND METHODS: This prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared. RESULTS: No significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0-232.28) and C (median, 60.25; IQR, 0-195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (-7.82) and with the limits of agreement from -65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0-120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode. CONCLUSION: The Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

Epithelioid angiomyolipoma of the pancreas: A case report and review of the literature.

BACKGROUND: Angiomyolipomas (AMLs), belonging to the family of mesenchymal tumors, are considered benign lesions that occur mostly in the kidney or as a part of tuberous sclerosis. Epithelioid AML (EAML) is a rare type of AML that appears to have malignant potential. Extrarenal AMLs usually occur in the liver according to the retrieved literature reports. There have been only two previous reports of monofocal primary AML of the pancreas; however, no cases of primary monotypic EAML of the pancreas have been reported. CASE SUMMARY: An asymptomatic 59-year-old woman incidentally revealed a tumor during abdominal ultrasound examination. Routine blood tests and physical examination were within normal limits. Abdominal ultrasound revealed a 1.9-cm hypoechogenic mass in the tail of the pancreas, clearly visualized by endoscopic ultrasound. However, contrast-enhanced abdominal computed tomography scans did not demonstrate the lesion. A subsequent gadolinium-enhanced magnetic resonance imaging scan showed that the lesion had some characteristic manifestations. The lesion was initially thought to be a neuroendocrine tumor (asymptomatic PanNET). After surgical resection, histopathology and immunohistochemistry confirmed the diagnosis of EAML. At the 6-mo follow-up, no recurrence, spread, or metastasis was identified on computed tomography or magnetic resonance imaging. CONCLUSION: The preoperative diagnosis of pancreatic AML is extremely difficult. Imaging techniques are essential for providing valuable morphological features for differential diagnosis.

Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19.

PURPOSE: To describe the prognostic value of pulmonary artery (PA) trunk enlargement on the admission of in-hospital patients with severe COVID-19 infection by unenhanced CT image. MATERIALS AND METHODS: In-hospital patients confirmed COVID-19 from January 18, 2020, to March 7, 2020, were retrospectively enrolled. PA trunk diameters on admission and death events were collected to calculate the optimum cutoff using a receiver operating characteristic curve. According to the cutoff, the subjects on admission were divided into two groups. Then the in-hospital various parameters were compared between the two groups to assess the predictive value of PA trunk diameter. RESULTS: In the 180 enrolled in-hospital patients (46.99 ± 14.95 years; 93 (51.7%) female, 14 patients (7.8%) died during their hospitalization. The optimum cutoff PA trunk diameter to predict in-hospital mortality was > 29 mm with a sensitivity of 92.59% and a specificity of 91.11%. Kaplan-Meier survival curves for PA trunk diameter on admission showed that a PA trunk diameter > 29 mm was a significant predictor of subsequent death (log-rank < 0.001, median survival time of PA > 29 mm was 28 days). CONCLUSION: PA trunk enlargement can be a useful predictive factor for distinguishing between mild and severe COVID-19 disease progression.

Novel Nutrition-Based Nomograms to Assess the Outcomes of Lung Cancer Patients Treated With Anlotinib or Apatinib.

BACKGROUND: Previous studies have indicated that the changes in body composition during treatment are prognostic in lung cancer. The question which follows is it may be too late to identify vulnerable patients after treatment and to improve outcomes for these patients. In our study, we sought to explore the alterations of body composition and weight before the outset of the antiangiogenic treatment and its role in predicting clinical response and outcomes. METHODS: In this retrospective study, 122 patients with advanced lung cancer treated with anlotinib or apatinib were analyzed. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) for 3 months before the outset of antiangiogenic treatment and other clinical characteristics were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomograms was validated internally by using bootstrap method with 1,000 resamples models and was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA). RESULTS: The median progression-free survival (PFS) and overall survival (OS) were 128 (95% CI 103.2-152.8) days and 292 (95% CI 270.9-313.1) days. Eastern Cooperative Oncology Group performance status (ECOG PS), brain metastases, the Glasgow Prognostic Score (GPS), clinical response, therapeutic regimen, and ΔL1SMI per 90 days were significantly associated with PFS, while ECOG PS, GPS, clinical response, therapeutic regimen, ΔL1SMI per 90 days were identified for OS. The C-index for the nomograms of PFS and OS were 0.763 and 0.748, respectively. The calibration curves indicated excellent agreement between the predicted and actual survival outcomes of 3- and 4-month PFS and 7- and 8-month OS. DCA showed the considerable value of the model. CONCLUSION: Nomograms were developed from clinical features and nutritional indicators to predict the probability of achieving 3-month and 4-month PFS and 7-month and 8-month OS with antiangiogenic therapy for advanced lung cancer. Dynamic changes in body composition before the initiation of treatment contributed to early detection of poor outcome.

Exploring the Interobserver Agreement in Computer-Aided Radiologic Tumor Measurement and Evaluation of Tumor Response.

The accurate, objective, and reproducible evaluation of tumor response to therapy is indispensable in clinical trials. This study aimed at investigating the reliability and reproducibility of a computer-aided contouring (CAC) tool in tumor measurements and its impact on evaluation of tumor response in terms of RECIST 1.1 criteria. A total of 200 cancer patients were retrospectively collected in this study, which were randomly divided into two sets of 100 patients for experiential learning and testing. A total of 744 target lesions were identified by a senior radiologist in distinctive body parts, of which 278 lesions were in data set 1 (learning set) and 466 lesions were in data set 2 (testing set). Five image analysts were respectively instructed to measure lesion diameter using manual and CAC tools in data set 1 and subsequently tested in data set 2. The interobserver variability of tumor measurements was validated by using the coefficient of variance (CV), the Pearson correlation coefficient (PCC), and the interobserver correlation coefficient (ICC). We verified that the mean CV of manual measurement remained constant between the learning and testing data sets (0.33 vs. 0.32, p = 0.490), whereas it decreased for the CAC measurements after learning (0.24 vs. 0.19, p < 0.001). The interobserver measurements with good agreement (CV < 0.20) were 29.9% (manual) vs. 49.0% (CAC) in the learning set (p < 0.001) and 30.9% (manual) vs. 64.4% (CAC) in the testing set (p < 0.001). The mean PCCs were 0.56 ± 0.11 mm (manual) vs. 0.69 ± 0.10 mm (CAC) in the learning set (p = 0.013) and 0.73 ± 0.07 mm (manual) vs. 0.84 ± 0.03 mm (CAC) in the testing set (p < 0.001). ICCs were 0.633 (manual) vs. 0.698 (CAC) in the learning set (p < 0.001) and 0.716 (manual) vs. 0.824 (CAC) in the testing set (p < 0.001). The Fleiss' kappa analysis revealed that the overall agreement was 58.7% (manual) vs. 58.9% (CAC) in the learning set and 62.9% (manual) vs. 74.5% (CAC) in the testing set. The 80% agreement of tumor response evaluation was 55.0% (manual) vs. 66.0% in the learning set and 60.6% (manual) vs. 79.7% (CAC) in the testing set. In conclusion, CAC can reduce the interobserver variability of radiological tumor measurements and thus improve the agreement of imaging evaluation of tumor response.

Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning.

OBJECTIVE: To identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). METHODS: This study enrolled 49 patients with schwannomas and 139 with GISTs proven by pathology. CT features with P < 0.1 derived from univariate analysis were inputted to four models. Five machine learning (ML) versions, multivariate analysis, and radiologists' subjective diagnostic performance were compared to evaluate diagnosis performance of all the traditional and advanced methods. RESULTS: The CT features with P < 0.1 were as follows: (1) CT attenuation value of unenhancement phase (CTU), (2) portal venous enhancement (CTV), (3) degree of enhancement in the portal venous phase (DEPP), (4) CT attenuation value of portal venous phase minus arterial phase (CTV-CTA), (5) enhanced potentiality (EP), (6) location, (7) contour, (8) growth pattern, (9) necrosis, (10) surface ulceration, (11) enlarged lymph node (LN). LR (M1), RF, DT, and GBDT models contained all of the above 11 variables, while LR (M2) was developed using six most predictive variables derived from (M1). LR (M2) model with AUC of 0.967 in test dataset was thought to be optimal model in differentiating the two tumors. Location in gastric body, exophytic and mixed growth pattern, lack of necrosis and surface ulceration, enlarged lymph nodes, and larger EP were the most important CT features suggestive of schwannomas. CONCLUSION: LR (M2) provided the optimal diagnostic potency among other ML versions, multivariate analysis, and radiologists' performance on differentiation of schwannomas from GISTs.

Computed tomography characteristics of adrenal ganglioneuroma: a retrospective analysis of 30 pathologically-confirmed cases.

OBJECTIVE: To investigate the computed tomography (CT) characteristics of adrenal ganglioneuromas (AGNs) and to determine the ability of CT scanning to distinguish between large (>3 cm) and small (≤3 cm) AGNs. METHODS: This retrospective study searched the electronic medical record system of a hospital between January 2008 and July 2019 in order to identify patients with pathologically-confirmed AGNs that underwent three phases of CT scanning. The CT features were compared between large and small AGNs. RESULTS: A total of 30 patients with pathologically-confirmed AGNs were included in the study. The majority of patients (76.7%; 23 of 30) were asymptomatic and there were nonspecific symptoms in seven patients. The 'pointed peach' sign appeared in more than half of the patients (53.3%; 16 of 30). The CT value of the arterial phase, progressive enhancement, morphology and calcification in the CT images were found to be significantly different between large and small AGNs. Progressive enhancement was more likely to occur in small AGNs. Most large AGNs had irregular shapes, while small AGNs were likely to be round or oval with a smooth border. Calcifications were noted in large AGNs (42.9%; six of 14). CONCLUSION: CT scanning can show many of the key imaging characteristics of AGNs used to distinguish between large and small AGNs.