CT Features Research Articles

Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures

BackgroundTo accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT.MethodsThe study included a cohort of 1,325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort (n = 930), a testing cohort (n = 238), an external validation 1 cohort (n = 93), and 2 cohort (n = 64). We collected clinical characteristics and semantic features, and extracted radiomics features. We utilized the LightGBM algorithm to construct prediction models using the selected features. Quantifying the contribution of radiomics features of CT to prediction model using Shapley additive explanations (SHAP) method. The models' performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, specificity, calibration curve, and decision curve analysis (DCA).ResultsIn the training cohort, the clinical model achieved an AUC value of 0.775, the radiomics model achieved an AUC value of 0.836, and the combined model achieved an AUC value of 0.837. In the testing cohort, the AUC values of the models were 0.743, 0.755, and 0.768. In the external validation 1 cohort, the AUC values of the models were 0.717, 0.758, and 0.765, while in the external validation 2 cohort, 0.725, 0.726 and 0.746. The DeLong test results indicated that the combined model outperformed the clinical model (p < 0.05). DCA indicated that the models provided a net benefit in predicting STAS. The SHAP algorithm explains the contribution of each feature in the model, visually demonstrating the impact of each feature on the model's decisions.ConclusionThe combined model has the potential to serve as a biomarker for predicting STAS using preoperative CT scans, determining the appropriate surgical strategy, and guiding the extent of resection.

Computed tomographic findings in dogs with ovarian tumors: A tortuous ovarian artery consistently identifies ovarian origin in complex abdominal masses.

The aim of this retrospective multicentric case series is to describe the CT findings of ovarian neoplasia in dogs. Twenty dogs with pre- and postcontrast CT exams and cytological/histological diagnosis of ovarian neoplasia were included. Five dogs presented with bilateral tumors, for a total of 25 neoplasms: 15 carcinomas (4 bilateral), 4 granulosa cell tumors, 2 poorly differentiated malignant neoplasia (bilateral), 2 luteomas, 1 teratoma, 1 dysgerminoma. In two dogs, the tumor developed from an ovarian remnant. Ovarian tumors showed variable size, lobulated shape, and precontrast heterogenous appearance. Mineral foci and/or fat components were rare, observed in teratoma, granulosa cell tumors (2), and ovarian carcinoma. Tumor type was not found to be associated with any CT features. Larger masses were more likely located in the central abdomen ventral to the ipsilateral kidney, demonstrated signs of tumor rupture, and were associated with abdominal or sternal lymphadenopathy and peritoneal effusion. A tortuous ovarian artery was constantly detectable, associated with an enlarged gonadal vein (12 cases). Related cavitary changes were peritoneal effusion (14 dogs) and sternal lymphadenopathy (7 dogs). Presumed or confirmed metastasis was reported in 9 of 20 cases, with CT evidence of transcoelomic (serosal thickening, peritoneal nodules, omental cake, implant lesions to the liver, spleen, and diaphragm), lymphatic and hematogenous spread (lungs, liver, bone, muscles, and spleen). In conclusion, the present study reports the CT features of different canine ovarian neoplasia. A tortuous ovarian artery may be useful to consistently recognize the ovarian origin of a large abdominal mass.

Comparison of computed tomography findings between perianal tumors originating from the anal sacs and perianal glands in dogs.

Tumors originating from the anal sacs and perianal glands are common tumors of the perianal region. However, differentiation of perianal tumors based on CT findings remains challenging because perianal tumors can show overlapping CT characteristics. Therefore, this retrospective, multi-institutional study aimed to identify the CT findings that can differentiate between tumors originating from the anal sacs and perianal glands in canines. Twenty dogs were included, with nine apocrine gland anal sac adenocarcinomas (AGASACAs), six perianal gland adenomas, three perianal gland carcinomas, and two perianal gland epitheliomas. The tumor CT features included the tumor growth pattern, tumor shape, cystic changes within the tumor, contrast enhancement pattern, displacement of the anus, and identification of the anal sacs. The prevalences of suspected metastatic lymphadenopathy of the iliosacral lymph centers and lung metastases were evaluated. The tumor volumes and values normalized to body weight were also assessed. The tumor growth patterns differed significantly between AGASACAs and perianal gland tumors. AGASACAs mostly showed unilateral growth, whereas perianal gland tumors mostly exhibited bilateral growth. CT features, except for the tumor growth pattern, failed to show significant differences between tumors arising from the anal sacs and those arising from the perianal glands. Although histopathological diagnosis remains the gold standard, this study's findings suggest that CT analysis of tumor growth patterns may help differentiate AGASACAs from perianal gland tumors.

Usefulness of computed tomography findings for predicting soft tissue sarcoma grades.

Soft tissue sarcomas (STSs) constitute a heterogeneous group of tumors that originate from mesenchymal cells. Imaging plays an important role in the assessment of the oncological stage of STSs. Computed tomography findings facilitate the identification of the tumor location and margins in relation to the surrounding anatomic boundaries and thereby enable preoperative planning of tumor management. The histological grade is among the most important parameters that affect tumor recurrence and metastasis rates. However, the prediction of STS histological grades using CT characteristics has not yet been elucidated. This study retrospectively investigated CT findings of patients with different STS grades for differentiation. Thirty dogs with STSs (12 Grade 1, 10 Grade 2, and 8 Grade 3) were included from three animal hospitals. The investigated CT features included tumor delineation, dysmorphic intratumoral or peritumoral vessels, contrast enhancement patterns, peripheral rim enhancement, intratumoral calcification, degree of intratumoral cystic lesions, peritumoral fat stranding, metastatic regional lymphadenopathy, and lung metastasis. Quantitative findings included tumor size and pre- and postcontrast attenuation. Tumor size, dysmorphic intratumoral or peritumoral vessels, peritumoral fat stranding, and metastatic regional lymphadenopathy significantly differed among tumor grades. On post hoc analysis, Grade 3 tumors were significantly larger, had a higher prevalence of dysmorphic vessels than Grade 1 tumors, and had a significantly higher rate of adjacent metastatic lymphadenopathy than Grade 2 tumors. Predicting STS grades before the histopathological examination could potentially aid in the planning of the STS resection margins.

Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis.

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes. For hepatic steatosis, noncontrast CT provides a close approximation to MRI-based proton-density fat fraction (PDFF) quantification, with liver attenuation values less than or equal to 40 HU signifying at least moderate steatosis. Liver fat quantification with postcontrast CT is less precise but can generally provide categorical assessment (eg, mild vs moderate steatosis). Noncontrast CT can also trigger appropriate assessment for iron overload when increased parenchymal attenuation values are observed (eg, >75 HU). A variety of morphologic and functional CT features indicate the presence of underlying hepatic fibrosis and cirrhosis. Beyond subjective assessment, quantitative CT methods for staging fibrosis can provide comparable performance to that of elastography. Furthermore, quantitative CT assessment can be performed retrospectively, since prospective techniques are not required. Many of these CT quantitative measures are now fully automated via artificial intelligence (AI) deep learning algorithms. These retrospective and automated advantages have important implications for longitudinal clinical care and research. Ultimately, regardless of the indication for CT, opportunistic detection of steatosis, iron overload, and fibrosis can result in appropriate clinical awareness and management. ©RSNA, 2024 See the invited commentary by Yeh in this issue.

Correlation of Aspergillus fumigatus Sensitization with Mucus Plugging in COPD.

Both Aspergillus fumigatus sensitization and mucus plugs are associated with poor clinical outcomes in COPD. However, little is known about the association between Aspergillus hypersensitivity and mucus plugging in patients with COPD. We retrospectively enrolled COPD patients who had visited Peking University Third Hospital and received measurement of the Aspergillus Fumigatus specific IgE (Af sIgE) from Oct 1, 2018 to Sep 30, 2023. The clinical, laboratory, and chest CT features were analyzed, with mucus plugging evaluation using the bronchopulmonary segment-based scoring system. Comparison was performed between COPD patients with and without Aspergillus hypersensitivity (AH). Among the 378 COPD patients with measurement of Af sIgE, 29 (7.7%) were classified as having AH (Af sIgE>0.35KU/L). By propensity score matching (1:2), 58 patients without AH were included for comparison. Patients with AH had lower FEV1%pred (P=0.008) and FEV1/FVC (%) (P=0.023), and were more likely to have a blood eosinophil count exceeding 300/µL and higher white blood cell and neutrophil counts. The prevalence of luminal plugging on chest CT in subjects with AH was 58.6%, compared to 31.0% in those without AH (P=0.013). Multivariate regression analyses showed that Af sIgE more than 0.70 KU/L and blood neutrophil count were associated with mucus plugging. In patients with COPD, Aspergillus sensitization was associated with lower lung function and mucus plugging on chest CT.

Computed tomographic findings in 25 cats with ear canal neoplasia.

Computed tomography is commonly used to evaluate feline otic disease; however, published studies characterizing the CT appearance of ear canal neoplasia are limited. The purpose of this multicenter, retrospective, secondary analysis, cross-sectional study was to describe the CT features of histopathologically confirmed feline ear canal neoplasia. The CT studies of 25 cats with ear canal neoplasia were prospectively scored by consensus of two veterinary radiologists. Recorded parameters were the presence of focal or multifocal tissue enlargement (mass/masses), lesion shape, location of the center of mass, attenuation characteristics, features of contrast enhancement, involvement of otic structures, calvarial and brain changes, changes of nearby structures, and lymphadenopathy. There was a significant overlap of CT findings between cats with malignant ceruminous gland neoplasia, ceruminous gland adenoma, and squamous cell carcinoma (SCC). Ceruminous gland adenoma was typically homogeneous in attenuation with homogeneous contrast enhancement and no intralesional fluid accumulations (IFAs) or involvement of adjacent structures. In contrast, SCC consistently had heterogeneous attenuation, heterogeneous contrast enhancement, IFAs, and involvement/invasion of adjacent structures. Malignant ceruminous gland neoplasia had variable attenuation and pattern of contrast enhancement with occasional IFAs and occasional involvement/invasion of adjacent structures. Knowledge of these imaging features will inform the creation of prioritized differential diagnosis lists. However, a biopsy is required to confirm the diagnosis.

Computed tomographic features of Morel-Lavallée lesions in 3 cats.

To describe the CT features of Morel-Lavallée lesions (MLLs) and their association with trauma in cats. This study aimed to investigate whether MLL fluid in cats accumulates between the dermis and deep fascia near superficial trauma, exhibits a vertical attenuation gradient, and shows no or only mild contrast enhancement on CT images. 3 young cats: a 1-year-old neutered male domestic shorthair cat, a 2-year-old neutered male domestic shorthair cat, and a 3-year-old neutered female British Shorthair cat. The clinical presentation of the 3 cats in the study involved recurrent swelling and fluid accumulation affecting at least 1 pelvic limb following trauma. In all cases, there was a fluid-filled space deep to the skin and superficial subcutaneous layer but superficial to the deep fascial plane overlying the muscular component of the pelvic limbs. The MLL fluid had a vertical attenuation gradient and no or only mild contrast enhancement. Morel-Lavallée lesions are often initially overlooked, likely due to distraction by concomitant significant bone injuries, and can be mistaken for hematomas, seromas, subcutaneous edema, contusions, or cellulitis, leading to less aggressive treatment. If left untreated, MLLs present a significant risk of extensive skin necrosis compared to other types of subcutaneous swelling.

Preoperative clinical and contrasted-enhanced CT features to predict perineural invasion in gallbladder carcinoma: focus on clinical T3-4 stage.

To investigate the utility of combining clinical and contrasted-enhanced tomography (CECT) parameters for the preoperative evaluation of perineural invasion (PNI) in gallbladder carcinoma (GBC). A total of 134 patients with GBC (male/female, 52/82; age, 64.4 ± 9.7 years) were divided into PNI-positive (n = 63) and PNI-negative groups (n = 71). Clinical characteristics (demographic information, liver function indicators and tumor markers) and CECT parameters (tumor type, tumor size, gallbladder stone, invasion of gallbladder neck/cystic duct, clinical T stage and N stage) were collected and compared between two groups. Binary logistic regression analysis, receiver operating characteristic curves analyses and Delong test were used in further statistical analyses in clinical T3-4 stage (cT3-4) GBC patients. Overall survival (OS) rates after surgery were compared between PNI-negative group and PNI-positive group of cT3-4 GBC patients. The majority of GBC patients with PNI were classified as cT3-4 (61/63, 96.8%), while only 3.2% (2/63) of PNI-positive cases were identified at cT1-2. Among cT3-4 GBC, OS was significantly lower in the PNI-positive group than the PNI-negative group after surgery (HR,1.661; 95% CI, 1.044-2.643; P = 0.032). Gender and gallbladder neck/cystic duct invasion were independent predictive factors for cT3-4 GBC patients with PNI. A combination of gender and gallbladder neck/cystic duct invasion showed the best diagnostic performance than that of individual parameters (all P < 0.05). Preoperative T staging using CECT enables the initial assessment of PNI status in GBC patients. A combination of gender and gallbladder neck/cystic duct invasion may effectively predict PNI in GBC, particularly in cT3-4 GBC.

Solid-type adenocarcinoma on thin-section CT: quantitative parameters from dual-energy CT associated with spread through air spaces.

Spread through air spaces (STAS) is a well-established factor associated with poor oncological outcomes in patients undergoing surgery for solid lung adenocarcinoma. There could potentially be a disparity in iodine uptake between patients with positive and negative airway spread of solid lung adenocarcinoma. To explore the associations and find correlations of iodine uptake with STAS status in patients who underwent surgery for solid lung adenocarcinoma. Patients who underwent solid lung adenocarcinoma resection between January and June 2022 were included in this retrospective study. Iodine concentration and CT features were assessed using contrast-enhanced dual-energy computed tomography (DECT) scans, and these were compared with the status of STAS. Of 52 patients included, 25 (48%) were STAS-positive and 27 (52%) were STAS-negative. There were no statistically significant differences in CT features between the two groups (P > 0.05). STAS-positive was significantly associated with low arterial phase iodine concentration (ICA), normalized arterial phase iodine concentration (NICA), and venous phase iodine concentration (ICV), with a cutoff established at 1.15 mg/mL, 0.11, and 1.35 mg/mL, respectively (P < 0.05). The AUCs for ICA, NICA, and ICV in predicting STAS in solid lung adenocarcinoma were 0.82, 0.83, and 0.73, respectively. ICA and NICA were identified as independent risk factors for STAS in solid lung adenocarcinoma, with a combined AUC of 0.89. This study suggests that solid lung adenocarcinoma patients with low ICA, NICA, and ICVA were associated with STAS-positive, as well as a worse survival outcomes.

Identification of osteoid and chondroid matrix mineralization in primary bone tumors using a deep learning fusion model based on CT and clinical features: a multi-center retrospective study

We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements. Multivariate logistic regression analysis was used to screen the clinical features such as sex, age, mineralization location, and pathological fractures, which were then connected with the imaging features to construct the deep learning fusion model (SC-Net). The diagnostic performance of the SC-Net model and machine learning models were compared with radiologists' diagnoses, and their classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. In the external test set, the fusion model (SC-Net) achieved the best performance with an AUC of 0.901 (95% CI: 0.803-1.00), an accuracy of 83.7% (95% CI: 69.3%-93.2%) and an F1 score of 0.857, and outperformed the S-Net model with an AUC of 0.818 (95% CI: 0.694-0.942), an accuracy of 76.7% (95% CI: 61.4%-88.2%), and an F1 score of 0.828. The overall classification performance of the fusion model (SC-Net) exceeded that of radiologists' diagnoses. The deep learning fusion model based on multi-center CT images and clinical features is capable of accurate classification of osseous and chondroid matrix mineralization and may potentially improve the accuracy of clinical diagnoses of osteogenic versus chondrogenic primary bone tumors.

Radiomics based on dual-layer spectral detector CT for predicting EGFR mutation status in non-small cell lung cancer.

To explore the value of dual-layer spectral computed tomography (DLCT)-based radiomics for predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). DLCT images and clinical information from 115 patients with NSCLC were collected retrospectively and randomly assigned to a training group (n=81) and a validation group (n=34). A radiomics model was constructed based on the DLCT radiomic features by least absolute shrinkage and selection operator (LASSO) dimensionality reduction. A clinical model based on clinical and CT features was established. A nomogram was built combining the radiomic scores (Radscores) and clinical factors. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used for the efficacy and clinical value of the models assessment. A total of six radiomic features and two clinical features were screened for modeling. The AUCs of the radiomic model, clinical model, and nomogram were 0.909, 0.797, and 0.922, respectively, in the training group and 0.874, 0.691, and 0.881, respectively, in the validation group. The AUCs of the nomogram and the radiomics model were significantly higher than that of the clinical model, but no significant difference was found between them. DCA revealed that nomogram had the greatest clinical benefit at most threshold intervals. Nomogram integrating clinical factors and pretreatment DLCT radiomic features can help evaluate the EGFR mutation status of patients with NSCLC in a noninvasive way.

Preoperative prediction of microvascular/nerve invasion in locally advanced gastric cancer by differentiation and enhanced CT features.

The purpose of the article is to determine whether differentiation and enhanced CT features can preoperatively predict microvascular/nerve invasion in locally advanced gastric cancer. Retrospective analysis of the CT and pathological data of 325 patients with locally advanced gastric cancer confirmed by pathology in our hospital from July 2011 to August 2023. The patient's age, gender, tumor location, T stage, N stage, TNM stage, differentiation, Lauren classification, as well as tumor thickness, tumor longest diameter, plain CT value, arterial CT value, venous CT value, arterial phase enhancement rate, and venous phase enhancement rate were assessed. This study included a total of 325 patients with locally advanced gastric cancer and 189 patients (58.15%) with microvascular/nerve invasion. The results of the univariate analysis showed that gender, location, T stage, N stage, TNM stage, differentiation, Lauren classification, tumor thickness, and longest diameter of the tumor were associated with microvascular/nerve invasion (P < .05). Multivariate analysis suggested that TNM stage and differentiation were independent risk factors for microvascular/nerve invasion. The receiver operating characteristic analysis showed that the diagnostic efficacy of the combined parameter of TNM stage and differentiation was better than that of the single parameter, in which area under the curve, sensitivity, and specificity were 0.819 (95%CI: 0.770-0.867), 66.7%, and 83.8%, respectively. Differentiation and enhanced CT are helpful in predicting whether microvascular/nerve invasion occurs in locally advanced gastric cancer before operation, especially the combined parameters of TNM stage and differentiation.

Characterization of exclusive rib lesions detected by [ 68 Ga]Ga-PSMA-11 PET/CT.

The objective of this study was to characterize exclusive costal lesions detected by 68 Gallium-labelled prostate-specific membrane antigen ([ 68 Ga]Ga-PSMA-11) PET/computed tomography (CT) at initial staging or biochemical recurrence (BCR) in prostate cancer (PCa) patients, and to identify clinical and/or PET/CT criteria associated with benign and malignant lesions. We retrospectively identified 54 patients with PCa who underwent [ 68 Ga]Ga-PSMA-11 PET/CT for initial staging ( N = 39) or BCR ( N = 15) and whose reports described rib lesions, at the exclusion of any other lesions, whether doubtful, suspicious, or established. Posttherapy prostate-specific antigen (PSA) levels were used to determine whether those lesions were benign or malignant. Each patient's prostate-specific membrane antigen PET/CT report was classified as true positive, true negative, false positive, or false negative based on the posttherapy PSA level. We then assessed whether any clinical and/or PET/CT criteria could help differentiate benign from malignant lesions, and if any criteria were misleading. Among the 54 patients, 46 (85.2%) had 64 benign costal lesions, and eight (14.8%) had 10 malignant lesions. PET/CT reports indicated rib lesions as benign/equivocal in 38/54 (55.6%) patients and malignant in 16/54 (29.6%). Benign features on CT were the only parameter significantly associated with the final diagnosis. Factors such as patient age, maximum standardized uptake value of lesions, lesion dispersion, and malignant features described on CT were found to be misleading when deciding the malignant or benign status. Most exclusive costal lesions detected by [ 68 Ga]Ga-PSMA-11 PET/CT are benign. Apart from specific benign CT features, no clinical or PET/CT criteria reliably differentiate benign from malignant costal lesions.

Computed tomographic features of pulmonary and extrapulmonary lesions can be useful in prioritizing the diagnosis of hemangiosarcoma metastases in dogs.

The aim of this study was to evaluate the CT features of pulmonary metastases in dogs with hemangiosarcoma (HSA) at various sites. Additionally, the CT characteristics of extrapulmonary metastases in the same population were assessed. Retrospective, observational, and descriptive study conducted from April 2013 to January 2024. Dogs with histologically confirmed HSA and suspected or cytologically/histologically confirmed lung metastases were included. Dogs were excluded if they had a second primary tumor or only 1 unsampled pulmonary nodule. 33 dogs were included, with 26/33 [78.8%] having more than 10 metastatic pulmonary nodules. Most nodules were generalized (24/33 [72.7%]), miliary (29/33 [87.9%]) to subcentimetric (32/33 [97%]) in size, well-defined margins (29/33 [87.9%]), or a perilesional halo sign (24/33 [72.7%]). When more than 10 nodules were present, a generalized distribution was prevalent, while a peripheral location was more common when 2 to 10 nodules were present (P < .0001). In 32/33 (97%) cases, a pulmonary vessel was directly connected to the nodule (feeding vessel). After contrast administration, most lung metastases appeared homogenous (26/33 [78.8%]), although some showed areas of intense enhancement (5/33 [15.1%]) a feature also observed in extrapulmonary metastases with varying frequency (0% to 85.7%). Pulmonary HSA metastases were characterized by generalized, small (miliary/subcentimetric), well-defined nodules, commonly associated with a halo sign and feeding vessel. Intralesional areas of spotty postcontrast linear or amorphous strong hyperdensity were frequently observed especially in extrapulmonary metastases. These features may help radiologists and clinicians orient their diagnosis toward metastatic HSA.

Clinical and chest CT manifestations of Talaromycosis marneffei: a retrospective study comparing anti-IFN-γ autoantibodies-positive and HIV-positive cases

BackgroundTo explore the clinical and chest CT features of Talaromycosis marneffei (TSM), and to compare the differences between anti‑IFN‑γ autoantibodies‑positive and HIV-positive cases.MethodsClinical data and chest CT images of 54 HIV-negative patients with Talaromyces marneffei (TM) infection and positive anti-interferon-γ (anti-IFN-γ) autoantibody were retrospectively analyzed. Ninety-three HIV-positive patients with TM infection during the same period were included as controls. Differences between groups were compared by the two-sample t-test and the Mann-Whitney U test, respectively.ResultsThe time from symptom onset to diagnosis of TSM with positive anti-IFN-γ autoantibodies was significantly longer than that of controls (P < 0.001). BALF/sputum and lymph nodes were the better specimens for detecting TM in the anti-IFN-γ autoantibody-positive group, while TM were more often isolated or detected in blood samples in the other group. Patients with TM infection tended to present with cough, expectoration, and fever. Compared with controls, anti-IFN-γ antibody-positive patients with TM infection had a higher incidence of shortness of breath, skin lesions, joint pain, and high CD4/CD8 ratio (P < 0.05). There were only 1 and 6 cases with negative lung involvements in CT scans in anti-IFN-γ autoantibody-positive patients and HIV-positive patients with TM infection, respectively. In the remaining patients, chest CT manifestations were diverse, mainly presenting as fibrous cord‑like lesions, lymph node enlargement, and sporadic nodules. The incidences of patchy consolidation, air bronchogram, pleural effusion and pleural thickening were higher in anti-IFN-γ autoantibody-positive patients with TM infection than those of controls (P < 0.05).ConclusionsAnti-IFN-γ antibody positive patients with TM infection showed a longer diagnostic interval. clinical specimens from which TM was isolated or detected were different between the two groups. The clinical and imaging manifestations of patients with TM infection are characteristic.

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy. The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability. A total of 176 patients were included, divided into benign/preinvasive (n = 76) and invasive cancer groups (n = 100). Multivariate analysis identified eight independent predictors of malignancy: lobulation sign, bronchial inflation sign, AI-predicted malignancy probability, nodule nature, diameter, solidity proportion, vascular endothelial growth factor, and lung cancer autoantibodies. The combined predictive model demonstrated high accuracy (area under the curve [AUC] = 0.946). DCA showed that the combined model significantly outperformed the traditional model, and also proved superior to models using AI-predicted malignancy probability or the seven lung cancer autoantibodies plus traditional model. External validation confirmed its robustness (AUC = 0.856), achieving a sensitivity of 0.80 and specificity of 0.86, effectively distinguishing between invasive and noninvasive nodules. This combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.

Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease

Objective: To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. Methods: In this single-center retrospective cohort study, consecutive patients with obstructive coronary heart disease (with at least one stenosis≥50%) on their first coronary computed tomography angiography (CCTA) in Beijing Anzhen Hospital from February 2017 to July 2018 were included. Baseline clinical and CT characteristics were collected. Deep-learning-based CT-FFR and Leiden CCTA risk score were calculated. All patients enrolled were followed up for at least 5 years. The study endpoint was major adverse cardiovascular events (MACE), defined as the composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and unplanned revascularization. Receiver operating characteristic (ROC) curves were drawn to define the optimal cut-off point of the Leiden score in predicting the 5-year MACE, and survival analysis and Cox regression were performed to explore the related factors of MACE. Results: A total of 622 patients, aged 61 (54, 66) years, with 407 (65.4%) males were included. Diagnostic coronary angiography was performed in 78 patients after their baseline CCTA, with 34 (43.6%) patients had CT-FFR>0.80. During a follow-up time of 2 181 (2 093, 2 355) days, 155 patients (24.9%) suffered from MACE. ROC derived optimal cut-off point of Leiden score for predicting MACE was 15.48. Survival analysis found that male patients, Leiden risk score>15 and CT-FFR≤0.80 had worse prognosis. Multivariate Cox regression analysis identified CT-FFR≤0.80 as an robust and independent predictor of MACE (HR=4.98, 95%CI 3.15-7.86, P<0.001). Conclusion: Deep-learning-based CT-FFR aids in clinical decision-making and the evaluation of long-term prognosis in patients with obstructive coronary heart disease.

Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging. A data set of Centers 1 (training set: 2633; internal testing set: 1129); Center 2 and Center 3 (external testing set: 218) of patients with pulmonary nodule cases was retrospectively collected. Handcrafted features were extracted from noncontrast chest CT scans by three senior radiologists. A total of 22 clinically handcrafted parameters (age, gender, L-RADS, and PNI-GARS etal.) were used to construct machine learning models (random forest, gradient boosting, and explainable boosting) for the classification of preoperative pulmonary nodules, and the parameters of the model were adjusted to achieve optimal performance. To evaluate the prediction capacity of each model. Both 5-fold cross-validation and 10-fold cross-validation were used to test the robustness of the models. The explainable boosting model had the best performance on our constructed data. The model achieves an accuracy of 89.9%, a precision of 97.48%, a specificity of 89.5%, a sensitivity of 91.1%, and an AUC of 90.3%. In human-machine comparison, the AUC of machine learning models (90.4%, 95% CI: 85.5%-94.8%) was significantly improved compared to radiologists (60%, 95% CI: 50%-71.4%). The explainable boosting model exhibited superior performance on our dataset, achieving high accuracy and precision in the diagnosis of pulmonary nodules compared to experienced radiologists.

Assessment of high-risk gastroesophageal varices in cirrhotic patients using quantitative parameters from dual-source dual-energy CT.

To investigate the clinical value of dual-source dual-energy CT (dsDECT) quantitative parameters in evaluating hemodynamics and predicting high-risk gastroesophageal varices in cirrhotic patients. 98 consecutive patients were collected in this prospectively study and all patients underwent an abdominal triple-phase contrasted-enhanced examination with dsDECT. Iodine concentration (IC) and normalized iodine concentration (NIC) of the liver parenchyma, spleen parenchyma and aorta at different phases were recorded, and arterial iodine fraction (AIF), iodine washout rate (IWR), and extracellular volume (ECV) were calculated. Using upper gastrointestinal endoscopy as the reference standard, patients who met the inclusion and exclusion criteria were divided into groups with varices need treatment (VNT) and non-VNT. The clinical characteristics, traditional CT features and quantitative dsDECT parameters were compared between the VNT group and the non-VNT group using univariate analysis. The binary logistics analysis was used to build a model for diagnosing VNT. The receiver operating characteristic (ROC) curve was used for analysis and the DeLong test was used to compare different ROC curves. Finally, 57 patients were included in this study. Univariate analysis showed statistically significant differences in NIC of the liver at the portal venous phase (NIC-LPVP), IWR of the liver (IWR-L) and spleen volume between the VNT group and the non-VNT group (p < 0.05). The mixed-CT model was built by binary logistics analysis. The ROC curves of NIC-LPVP, IWR-L, spleen volume and the mixed-CT model were statistically significant (p < 0.05) for predicting VNT in cirrhotic patients, among which the area under the ROC curve of the mixed-CT model was the highest. Dual-source dual-energy CT has added clinical value in evaluating hepatic hemodynamics and diagnosing VNT in patients with liver cirrhosis.