Background:Spread through air spaces (STAS) is recognized as a novel invasive mode of lung adenocarcinoma (LADC), linked with poorer prognosis and high risk of recurrence. The aim of this study was to develop and evaluate a radiomics nomogram using computed tomography (CT)-based tumoral and peritumoral radiomics features for preoperatively predicting STAS status in clinical stage I pure-solid LADC. Methods:This study retrospectively enrolled 308 individuals with stage I LADC appearing as pure-solid nodules on thin-section CT who underwent surgical resection from three institutions. We randomly split the patients at authors' hospital into a training set (n=174) and internal validation set (n=73) in a ratio of 7:3, while the external validation set consisted of 61 patients from the other two hospitals. The radiomics features extracted from the gross tumor volume (GTV), two types of peritumoral tumor volume (PTV) (5 and 10 mm around the tumor), and their corresponding two types of gross peritumoral tumor volume (GPTV) were utilized to construct five radiomics models, respectively. Univariate and multivariate analyses identified the independent predictors of STAS. The radscore of the radiomics model with optimal performance was integrated with clinical predictor to develop a comprehensive nomogram. Results:The STAS positive status was found in 118 (38.3%) of the 308 patients {female: 54.2%; median [interquartile range (IQR)] age: 65, [57-72] years}. The GPTV10 model achieved the highest area under the curve (AUC) values of 0.741, 0.737 and 0.741 in three cohorts. The multivariate logistic regression (LR) suggested that micropapillary component was the independent risk factor of pathological STAS. The comprehensive model constructed using the GPTV10 radscore and clinical predictor exhibited AUCs of 0.788, 0.748 and 0.783. The decision curve analysis (DCA) revealed that the nomogram had superior capacity for predicting STAS status in LADC. Furthermore, both pathological STAS status and STAS predicted by the combined model stratified patients for prognosis, with 5-year recurrence-free survival (RFS) showing obvious difference between STAS-positive and STAS-negative. Conclusions:Peritumoral features were significantly correlated with STAS status. The integration of radiomics characteristics and clinical factor provided better performance in the prediction of STAS status.

Purpose To develop and validate a delta computed tomography radiomics (delCT-RS) based nomogram for accurate preoperative prediction of tumor regression grade (TRG) in locally advanced gastric cancer (LAGC) patients following neoadjuvant chemotherapy (NAC). Methods This retrospective study enrolled 147 LAGC patients. Two delineation strategies were compared: (1) contouring both the primary tumor and the largest lymph node (P + L) as regions of interest (ROIs), and (2) contouring only the primary tumor (P). Subsequently, radiomic features were extracted to construct corresponding radiomic models. This study compared the predictive accuracy of delCT-RS signatures to conventional single-phase radiomic signatures for TRG assessment. Then, delCT-RS signatures and clinical variables were combined into a nomogram. Finally, the prediction performance of nomogram was comprehensively evaluated. Results In assessing tumor response, delCT-RS outperformed single-phase radiomic signatures. Notably, delta computed tomography delCT-RS P + L demonstrated superior accuracy to delCT-RS P (delCT-RS P + L vs. delCT-RS area under the curve (AUC): training cohort: 0.805 vs. 0.727; validation cohort: 0.795 vs. 0.655). The nomogram, combining delCT-RS P + L and clinical factors, achieved optimal performance among all models (training cohort AUC = 0.841; validation cohort AUC = 0.817). (p < 0.05) Conclusion In this study, we innovatively employed a method that simultaneously delineated the primary tumor and the largest lymph node. This model can accurately predict TRG, effectively identify LAGC patients who can benefit from NAC, and provide scientific support for individualized treatment.

RATIONALE AND OBJECTIVES:To develop and validate a fluorine-18-fludeoxyglucose (18F-FDG) PET/CT-based radiomics nomogram for preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grading and recurrence-free survival (RFS) in patients with clinical stage I pure-solid invasive lung adenocarcinoma (LADC). MATERIALS AND METHODS: 418 patients with clinical stage I pure-solid invasive LADC who underwent preoperative 18F-FDG PET/CT examination were retrospectively enrolled. All patients were separated into the low-grade group (grade I and II; n=315) and the high-grade group (grade III; n=103) according to the IASLC grading system, and the cohort was randomly divided into a training set (n=292) and a testing set (n=126) at a ratio of 7:3. Radiomics features were extracted from CT and PET images in regions of the entire tumor. Multivariate analysis identified the independent predictors for IASLC grading and RFS. The Radscore, along with clinical and radiological features were combined to establish a predictive nomogram. RESULTS:The ultimate Radiomics model, achieving AUCs of 0.838 and 0.768 in the training and testing sets. The multivariate logistic regression showed that higher maximum standard uptake value (SUVmax), cavity presence are the independent risk factors for IASLC grading. The integrated nomogram showed superior prediction performance than CT model (p=0.001) and PET model (p=0.028) in the training set. Furthermore, both pathological grade and preoperatively predictive IASLC grade derived by nomogram significantly stratified patients for RFS, with 5-year survival rates showing marked differences between low-grade and high-grade LADC (p<0.001). CONCLUSION:The preoperative PET/CT-based radiomics nomogram represents a potential biomarker for predicting IASLC grade and RFS in patients with clinical stage I pure-solid invasive LADC.

BACKGROUND:Radiomics is increasingly applied in carotid plaques analysis to evaluate plaque characteristics and predict cardiovascular risk. However, the influence of different image reconstruction algorithms, particularly deep learning reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V), on the reproducibility of radiomic features remains poorly understood. PURPOSE:To evaluate the impact of DLIR and ASIR-V on CT radiomic features of carotid plaques. METHODS:76 patients with 104 carotid plaques who underwent head & neck CT angiography were retrospectively enrolled. Images were reconstructed by filtered back projection (FBP), ASIR-V (30%, 50%, and 80%) and DLIR (DL, DM, and DH). A total of 214 CT-based radiomic features were organized by statistic family (18 first-order; 75 texture: 24 GLCM, 14 GLDM, 16 GLRLM, 16 GLSZM, and 5 NGTDM) and transform domain (original and wavelet sub-bands); 121 features were extracted from wavelet sub-bands. Features were extracted from both 2D and 3D plaque images. The reliability of feature extraction was evaluated by the intraclass correlation coefficient (ICC). RESULTS:Different reconstruction algorithms influenced the most radiomic features. The percentages of first-order, texture, and features in the wavelet domain without statistical difference among 2D and 3D lesions for all seven groups were 0% (0/18), 12.0% (9/75), and 14.9% (18/121), respectively. Compared with FBP, the unaffected features for AV30%, 50%, and 80% decreased from 99.8% and 95.1% to 81.3%, and for DL, DM, and DH from 75.5% and 52.3% to 40.7%. Across statistic families, texture features were the most stable in pairwise comparisons in both the original and wavelet domains. Unaffected features in 2D lesion were larger than 3D lesion. The consistency of first-order feature in 3D lesion was excellent in both intra- and inter-observer, with ICC values ranging from 0.865 to 1 and 0.790 to 0.999, respectively. CONCLUSION:Both ASIR-V and DLIR algorithms profoundly impact carotid plaque radiomics, with higher strengths exacerbating feature instability. Texture features exhibited superior robustness across all reconstruction protocols. Our findings advocate for a stability-driven approach to model development: prioritizing robust texture features and employing lower-strength DLIR are crucial steps to ensure the generalizability of radiomic biomarkers.

BACKGROUND:The current prediction of postoperative growth in synchronous nodules remaining after surgical resection of dominant lung tumors in patients with multiple subsolid lung nodules is limited. This study aims to assess the efficacy of preoperative CT-based radiomics in predicting the 5-year growth of these residual nodules (RNs), versus models constructed using commonly utilized CT morphological and quantitative features. METHODS:Data from 1392 patients who underwent resection for lung subsolid nodules confirmed as adenocarcinoma or precursor glandular lesions between 2014 and 2018 were retrospectively reviewed. Among the participants, 208 surgical patients with 603 RNs were included, with a follow-up period exceeding five years. Each RN was classified as either grown or stable based on CT imaging. All enrolled RNs were randomly allocated to training and testing sets at an approximately 4:1 ratio. Four models (radiomics, morphological, quantitative, and combined) were built separately by using Random Forest. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses, and compared using DeLong test and net reclassification improvement (NRI). RESULTS:Patients harbored 1-26 RNs. 17.9% RNs grew in 5 years. Growth proportions varied by size: 4% for < 5 mm, 8.4% for 5-8 mm, and 48.5% for > 8 mm. Eighteen radiomics features, 5 morphological features, and 2 quantitative features were selected to build the respective models. The radiomics model showed a good ability to predict growth with an accuracy of 97.2% and 86.7% in the training and testing sets, respectively. The radiomics model showed a significantly higher area under the curve (AUC: 0.892) than the morphological model (AUC: 0.834, P < 0.05), an advantage over the quantitative model (AUC: 0.862, P = 0.251), and similarity to the combined model (AUC: 0.887) in the testing set. The radiomics model showed better reclassification than morphological (NRI = 7.4%; P = 0.017) and quantitative (NRI = 14%; P = 0.005) models in risk stratification. The calibration curves and decision curve analyses further confirmed the clinical value of radiomics. CONCLUSIONS:CT-based radiomics demonstrated superior predictive performance for the 5-year growth of RNs, and can be used independently as a promising tool for future clinical guidance.

RATIONALE AND OBJECTIVES:Early prediction of response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) helps guide treatment decisions and optimize treatment. This study aimed to establish a radiomics nomogram for predicting NAC response in LAGC patients using dual-phase contrast-enhanced CT (CECT) images. MATERIALS AND METHODS:This retrospective study recruited 143 patients with LAGC from January 2018 to March 2024. Radiomics features were extracted from arterial phase (AP) and venous phase (VP) CT images, and were used to develop three radiomics models: AP, VP, and a combined AP_VP model. Clinicopathological characteristics were selected via univariate and multivariate logistic regression. A nomogram was then constructed by integrating the AP_VP radiomics signature with clinicopathological characteristics. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The clinical utility and benefits were further quantified by comparing the nomogram to the clinical model using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Stratified analyses were conducted to explore the model's performance across different patient subgroups. RESULTS:The AP_VP model performed well in the radiomics models, with AUCs of 0.810 (95% CI, 0.721-0.899) and 0.745 (95% CI, 0.588-0.903) in the training and validation cohorts, respectively. The clinical model was constructed by cT stage and differentiation, with AUCs of 0.723 (95% CI, 0.619-0.827) and 0.754 (95% CI, 0.591-0.916). The nomogram combining radiomics and clinicopathological characteristics achieved AUCs of 0.845 (95% CI, 0.767-0.924) and 0.829 (95% CI, 0.697-0.96), significantly outperforming the clinical model (DeLong test p < 0.05). CONCLUSION:The nomogram, incorporating dual-phase enhanced CT and clinicopathological characteristics, demonstrated satisfactory performance in predicting NAC response in LAGC patients, assisting with individualized treatment.

Background: Although F-18-prostate-specific membrane antigen-1007 (F-18-PSMA-1007) positron emission tomography/computed tomography (PET/CT) and multiparametric magnetic resonance imaging (mpMRI) are good predictors of prostate cancer (PCa) prognosis, their combined ability to predict prostate-specific antigen (PSA) persistence has not been thoroughly evaluated. In this study, we assessed whether clinical, mpMRI, and F-18-PSMA-1007 PET/CT characteristics could predict PSA persistence in patients with PCa treated with radical prostatectomy (RP). Methods: This retrospective study involved consecutive patients diagnosed with PCa who underwent both preoperative mpMRI and PSMA PET/CT scans between April 2019 and June 2022. Scatter plots and heat maps were employed to determine the correlation of mpMRI and PSMA PET/CT features with preoperative PSA. Univariate logistic regression analyses were used assess the correlation between age, maximum Prostate Imaging-Reporting and Data System (PI-RADS) score, prostate-specific antigen density (PSAD), extracapsular extension (EPE), seminal vesicle invasion (SVI), total lesion PSMA (PSMA-TL), and PSA persistence. Multivariate logistic regression analyses were used to develop a predictive model for PSA persistence, while decision tree analysis was used to classify patients into different risk groups for easy interpretation and visualization. We divided the patient cohort into training and validation sets in an 8:2 ratio. To ensure the reliability of the model, we performed five-fold cross-validation of the validation results. Results: Ultimately, this study included 190 patients with PCa. The median age of the patients was 69 years [interquartile range (IQR) 64-73 years]. Among the patients, 35 (18%) experienced PSA persistence following RP. Additionally, SVI was identified in 31 (16%) patients. The median values for SUVmax and PSMA-TL were 11.83 (IQR 7.44-20.89) and 41.92 (IQR 21.25-113.83), respectively. Spearman correlation analysis indicated that the preoperative PSA levels in patients with PCa were slightly correlated with the maximum standardized uptake value (SUVmax) (r=0.41; P<0.001), significantly correlated with PSMA-TL (r=0.58, P<0.001), and strongly correlated with PSAD (r=0.865, P<0.001). Multivariate logistic regression analysis showed that the independent predictors of PSA persistence were SVI on mpMRI [area under the curve (AUC)=0.63; 95% confidence interval (CI): 0.516-0.739] and PSMA-TL (AUC =0.80; 95% CI: 0.723-0.877) on PSMA PET/CT (all P values <0.05). Patients with SVI and PSMA-TL >63.38 cm(3) were more likely to have PSA persistence. Decision tree analysis stratified patients into low-risk (5%), intermediate-risk (36%), and high-risk (48%) categories for PSA persistence. The model exhibited good discriminatory capability in internal validation (AUC 0.93, 95% CI: 0.850-0.930). Conclusions: F-18-PSMA-1007 PET/CT and mpMRI parameters were proved effective in predicting PSA persistence in postoperative patients with PCa. The decision tree classification model could help clinicians to assess patients with individualized risk stratification. Patients with PSMA-TL levels below the threshold are highly likely not to have PSA persistence.

Rationale and objectivesAccurate identification of symptomatic carotid plaques remains a clinical challenge, as conventional imaging focuses mainly on luminal stenosis and lacks sensitivity to plaque vulnerability and perivascular inflammation. This study aimed to develop and validate an explainable machine learning model integrating CT-based radiomics features from carotid plaque and perivascular adipose tissue (PVAT) to identify symptomatic carotid plaques.Materials and methods324 patients with extracranial carotid atherosclerosis and stenosis who had undergone head and neck computed tomography angiography (CTA) were retrospectively included. Three-dimensional radiomics features were extracted from segmented carotid plaque, PVAT and combined carotid plaque and PVAT (CP-PVAT) regions. Independent clinical factors were identified using univariate and multivariate logistic regression analyses. A combined model integrating the radiomics signature with selected clinical factors was developed. Models were developed and underwent internally validated using five-fold cross-validation to enhance robustness and minimize overfitting. Model interpretability was assessed using Shapley Additive Explanations (SHAP).ResultsThe combined model, which integrated CP-PVAT features and clinical factors, achieved excellent discriminative performance, with mean AUCs of 0.903 and 0.904 in the training and testing sets, respectively. It significantly outperformed models based solely on carotid plaque, PVAT, CP-PVAT or clinical factors (p < 0.05, DeLong’s test). SHAP analysis demonstrated that radiomics features provided complementary information, enhancing model interpretability and clinical relevance.ConclusionThis explainable radiomics-based model, combining CP-PVAT features with clinical risk factors, may serve as a promising tool for identifying symptomatic carotid plaques and supporting individualized cerebrovascular risk assessment.

Purpose:To develop and validate a predictor for early treatment response in hepatocellular carcinoma (HCC) patients accompanied by portal vein tumor thrombus (PVTT) undergoing transarterial chemoembolization (TACE), lenvatinib and a programmed cell death protein 1 (PD-1) inhibitor (TLP) therapy. Patients and Methods:In this retrospective study, patients with HCC and PVTT from two institutions receiving triple TLP therapy were enrolled. Radiomics features derived from pretreatment contrast-enhanced MRI were curated using intraclass correlation coefficient (ICC), Student's t-test, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) to ensure robust selection. Various machine learning (ML) algorithms were then used to construct the models. The meaningful clinical indicators were obtained via logistic regression analysis and ultimately integrated with radiomics features to develop a combined model. In addition, we used Shapley Additive exPlanation (SHAP) to clarify the model's operational dynamics. Results:Our study ultimately included 115 patients (7:3 randomization, 80 and 35 in the training and test cohorts, respectively) in total. No patients achieved complete remission, 47 achieved partial remission, 29 achieved stable disease, and 39 experienced disease progression. Among objective response rates (ORRs) and disease control rates (DCRs), 40.9% and 66.1% were reported. One of the four ML classifiers with optimal performance, namely random forest, was adopted as the radiomics model after testing. Regarding the performance assessment, the radiomics model's area under the curve (AUC) values reached 0.92 (95% CI: 0.86-0.97) and 0.79 (95% CI: 0.61-0.95), inferior to the combined model's AUCs of 0.95 (95% CI: 0.68-0.98) and 0.84 (95% CI: 0.91-0.99). Moreover, the SHAP plots illustrate the importance of global variables and the prediction process for individual samples. Conclusion:The model based on machine learning and radiomics showed favorable performance, and the operating mode was visualized through SHAP.

RATIONALE AND OBJECTIVES:This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patients with clinical stage IA pure-solid non-small cell lung cancer (NSCLC). MATERIALS AND METHODS:234 Patients who underwent lung resection for NSCLC from two hospitals were reviewed. Radiomic features were extracted from both intratumoral, peritumoral and habitat regions on PET. Univariate and multivariate logistic regression analyses were employed to determine significant clinical variables. Subsequently, a radiomics nomogram was developed by combining the radiomics signature with these identified clinical variables. Kaplan-Meier (KM) analysis was performed to investigate the prognostic value of the nomogram. Shapley Additive Explanations (SHAP) were used to interpret the ML models. RESULTS:The combination model which contained peritumoral 5 mm and habitat regions radiomics features, clinical variables obtained a strong well-performance, achieving area under the curve (AUC) of 0.905 (95% confidence interval (CI) 0.854-0.957) in the train set and 0.875 (95% CI 0.789-0.962) in the internal validation set. The radiomics signature was significantly associated with PFS, the model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use showed low-risk score given have far longer RFS than those with high-risk score (log-rank P＜0.001). CONCLUSION:The habitat and peritumoral radiomics signatures serve as an independent biomarker for predicting PFS in patients with early-stage NSCLC, effectively stratified survival risk among patients with clinical stage IA pure-solid non-small cell lung cancer.

Background:Both diabetes and osteoporosis have developed into major global public health problems due to the increasing aging population. It is crucial to screen populations at higher risk of developing osteoporosis for disease prevention and management in postmenopausal women with type 2 diabetes (T2D). This study aims to quantitatively investigate the association between risk factors and bone mineral density (BMD) and develop a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D. Methods:We retrospectively enrolled 1,309 postmenopausal women with T2D. Linear regression methods were used to assess the association between risk factors and BMD. Additionally, a multivariate logistic regression analysis was performed to identify independent risk factors associated with osteoporosis. Utilizing the logistic regression machine learning algorithm, we developed an osteoporosis screening tool that categorizes the population into three risk regions based on age and body mass index (BMI), indicating low, moderate, and high prevalence of osteoporosis in the age-BMI plane. Results:Older age and lower BMI were independently associated with decreased BMD. The BMD at the total hip, femur neck, and lumbar spine differed by 12.9, 10.9, and 15.5 mg/cm2 for each 1 unit increase in BMI, respectively. Both age and BMI were identified as independent predictors of osteoporosis. The osteoporosis screening tool was developed by using two straight lines with equations of BMI = 0.56 * age-4.12 and BMI = 0.56 * age-10.88; there were no significant differences in the prevalence of osteoporosis among the training, internal test, and external test datasets in the low-, moderate-, and high-risk regions. Conclusion:We have successfully developed and validated a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D for the first time. BMI was identified as a significant modifiable risk factor. Our study may improve awareness of osteoporosis and is valuable for disease prevention and management for postmenopausal women with T2D.

BACKGROUND:18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography/computed tomography (PET/CT) as an imaging modality for the whole body has shown its value in detecting incidental colorectal adenoma. In clinical practice, adenomatous polyps can be divided into three groups: low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN) and cancer, which can lead to different clinical management. However, the relationship between the 18F-FDG PET/CT SUVmax and the histological grade of adenomatous polyps is still not established, which is a challenging but valuable task. METHODS:This retrospective study included 255 patients with colorectal adenoma (CRA) or colorectal adenocarcinomas (AC) who had corresponding 18F-FDG uptake incidentally found on PET/CT. The correlations of SUVmax with pathological characteristics and tumor size were assessed. Neoplasms were divided into LGIN, HGIN, and AC according to histological grade. Receiver operating characteristic (ROC) analysis was applied to evaluate the predictive value of the SUVmax-only model and comprehensive models which were established with imaging and clinical predictors identified by univariate and multivariate analysis. RESULTS:The SUVmax was positively correlated with histological grades (r=0.529, P<0.001). Univariate and multivariate analysis showed that SUVmax was an independent risk factor among all groups except between HGIN and AC. The area under the curves (AUCs) of the comprehensive model for distinguishing between AC and adenoma, LGIN and HIGN, LGIN and AC, and HGIN and AC were 0.886, 0.780, 0.945, 0.733, respectively, which is statistically higher than the AUCs of the SUVmax-only model with 0.812, 0.733, 0.863, and 0.688, respectively. CONCLUSIONS:As an independent risk factor, SUVmax based on 18F-FDG PET/CT is highly associated with the histological grade of CRA. Thus, 18F-FDG PET/CT can serve as a noninvasive tool for precise diagnosis and assist in the preoperative formulation of treatment strategies for patients with incidental CRA.

Objective:To evaluate the diagnostic performance of (18F)-PSMA-1007 PET/CT in prostate cancer patients with biochemical recurrence (BCR) after radical prostatectomy and the effect of (18F)-PSMA-1007 PET/CT on treatment strategy. Methods:A total of 114 patients with BCR after radical prostatectomy who performed (18F)-PSMA-1007 PET/CT were retrospectively analyzed. The Gleason scores (GS), maximum standardized uptake values (SUVmax) and the diagnostic performance were compared according to different prostate-specific antigen (PSA) groups. To evaluate the impact of (18F)-PSMA-1007 PET/CT on treatment management, we also collected subjects' therapy before and after PET/CT. The PSA value was monitored to evaluate the biochemical response. Results:(18F)-PSMA-1007PET/CT was positive in 92/114 patients (80.7%). The detection rates were 20/34 (58.8%), 13/17 (76.5%), 15/17 (88.2%) and 44/46 (95.7%) for PSA levels of 0.2-<0.5, 0.5-<1, 1-<2, ≥2 ng/ml. The positive lesions on PET/CT revealed local recurrence in 24/114 (21.1%) patients, lymph nodes metastases in 54/114 (47.4%) and metastatic sites in bone, lung, and others in 75/114 (65.8%). A significant positive correlation was observed between the GS/ SUVmax and PSA level (r1 = 0.375, r2 = 0.336, P<0.001). As a result of the (18F)-PSMA-1007 PET/CT, therapeutic decision-making changed in 60/114 (52.6%) patients. With a follow-up of 11.0 ± 6.4 months, 81/114 PSA were collected after treatment guided by (18F)-PSMA-1007 PET/CT, and in 42/81 (51.9%) of patients, serum PSA levels decreased of more than 60%. Conclusion:(18F)-PSMA-1007 PET/CT has a high lesion detection rate for recurrent prostate cancer (PCa) and could have significant implications in decision-making treatment plan for the majority of PCa patients.

Stage I lung adenocarcinoma is a heterogeneous group. Previous studies have shown the prognostic evaluation value of PET/CT in this cohort; however, few studies focused on stage I invasive adenocarcinoma manifesting as solid nodules. This study aimed to evaluate the recurrence risk for patients with stage I invasive lung adenocarcinoma manifesting as solid nodules based on 18F-FDG PET/CT, CT imaging signs, and clinicopathological parameters. We retrospectively enrolled 230 patients who underwent 18F-FDG PET/CT examination between January 2013 and July 2019. Metabolic parameters: maximum standard uptake value (SUVmax), mean standard uptake value, tumor metabolic volume (MTV), and total tumor glucose digestion were collected. Kaplan–Meier method was used to evaluate recurrence-free survival (RFS), and the multivariate Cox proportional hazards model was used to determine the independent risk factors associated with RFS. The time-dependent receiver operating characteristic curve (ROC) method was used to calculate the optimal cutoff value of metabolic parameters. The 5-year RFS rate for all patients was 71.7

BackgroundMuscle depletion that impairs normal physiological function in elderly patients leads to poor prognosis. This study aimed to evaluate the association between total abdominal muscle area (TAMA), total psoas area (TPA), psoas muscle density (PMD), and short-term postoperative complications in elderly patients with rectal cancer.MethodsAll elderly patients underwent rectal cancer resection with perioperative abdominal computed tomography (CT). Complications were assessed according to the Clavien-Dindo classification. Severe complications were defined as grade III-V following the Clavien-Dindo classification. Univariate and multivariate analyses were performed to evaluate risk factors of short-term severe postoperative complications.ResultsThe cohort consisted of 191 patients with a mean age of 73.60 & PLUSMN; 8.81 years. Among them, 138 (72.25%) patients had Clavien-Dindo 0- II, 53 (27.75%) patients had severe postoperative complications (Clavien-Dindo III-V), and 1(0.52%) patient died within 30 days of surgery. PMD was significantly higher in the Clavien-Dindo 0-II cohort compared to the Clavien-Dindo III-V cohort (p=0.004). Nevertheless, TAMA and TPA failed to exhibit significant differences. Moreover, the multivariate regression analysis implied that advanced age [OR 1.07 95%CI (1.02-1.13) p=0.013], male [OR 5.03 95%CI (1.76-14.41) p=0.003], high charlson comorbidity index (CCI) score [OR 3.60 95%CI (1.44-9.00) p=0.006], and low PMD [OR 0.94 95%CI (0.88-0.99) p=0.04] were independent risk factors of Clavien-Dindo III-V.ConclusionPreoperative assessment of the PMD on CT can be a simple and practical method for identifying elderly patients with rectal cancer at risk for severe postoperative complications.

Purpose Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an F-18-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.Method In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative F-18-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.Result Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients(P < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.Conclusion The nomogram based on F-18-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

目的:基于临床及影像组学采用支持向量机(SVM)构建中轴性脊柱关节病(axSpA)的预测模型.方法:回顾性收集2012年10月至2019年2月在温州医科大学附属第一医院就诊的568例腰背痛患者,最终诊断axSpA 319例,非axSpA 249例.按7:3将患者随机分为训练组与验证组.于骶髂关节CT上手动勾画三维感兴趣区(V0I)并提取影像组学特征,应用最小冗余最大相关性(mRMR)及最小绝对收缩和选择算子(LASSO)算法进行降维及选择最优影像组学特征;采用单因素和多因素Logistic回归分析寻找诊断axSpA的临床危险因素.最后使用SVM分别构建临床、影像组学及临床-影像组学联合模型,利用受试者工作特征(ROC)曲线及Delong检验评估模型的诊断效能.结果:临床-影像组学联合模型在验证组中具有最佳诊断效能,诊断准确性为0.83,灵敏度和特异度分别为85.2％、79.7％,其ROC曲线下面积(AUC=0.91)高于临床模型(AUC=0.81)及影像组学模型(AUC=0.83),差异均有统计学意义(P＜0.05).结论:基于临床和影像组学构建SVM模型对诊断axSpA具有较高价值.

Background: The predictive values of the platelet to lymphocyte ratio (PLR) and red cell distribution width (RDW) have been demonstrated in different types of abdominal surgery. The aim of this study was to investigate the interest of the preoperative PLR and RDW as predictors of 30-day postoperative complications in patients with acute mesenteric ischemia (AMI). Methods: Clinical data of 105 AMI patients were retrospectively reviewed. Postoperative complications were evaluated by the Clavien-Dindo classification. The cutoff values for neutrophil to lymphocyte ratio (NLR), PLR, and RDW were determined by receiver operating characteristic curves. Univariate and multivariate analyses evaluating the risk factors for postoperative complications were performed. Results: In the univariate analyses, advanced age, female, anemia, high white blood cell (WBC), high PLR, high NLR, high RDW, Charlson comorbidity index (CCI) score >= 2, and bowel resection were associated with the postoperative complications. A multivariable analysis revealed that advanced age, high PLR, high RDW, and bowel resection were independent predictors of postoperative complications. Conclusions: The PLR and RDW might play important roles in evaluation of the risk of postoperative complications in AMI patients. The preoperative PLR and RDW are simple and useful predictors of postoperative complications in AMI patients.

孤立性纤维瘤(solitary fibrous tumor,SFT)是间质组织来源的梭形细胞肿瘤,既往认为其起源于胸膜[1]. 胸膜孤立性纤维瘤(solitary fibrous tumor of the pleura,SFTP)约占所有胸膜肿瘤的 5％,局部有复发倾向,生物学行为介于良恶性肿瘤之间;胸膜多发SFT罕见,笔者回顾性分析2例发生于胸膜的多发SFT,探讨其临床病理及CT特点,以提高对该病的认识.

Background:The purpose of this study was to evaluate the value of quantitative assessment of intratumoral 2-deoxy-2-[18F]fluoro-D-glucose (2-[18F]FDG) metabolic spatial distribution (Q-FMSD) in differentiating pulmonary lesions with high 2-[18F]FDG uptake.Methods:In this retrospective study, a total of 564 patients with pulmonary lesions who underwent 2-[18F]FDG positron emission tomography/computed tomography (PET/CT) examination were analyzed. The maximum standard uptake value (SUVmax) of the proximal (pSUVmax) and distal (dSUVmax) regions of the lesions were measured, respectively. Then, Q-FMSD was obtained by the ratio of pSUVmax to dSUVmax. The diagnostic performance and area under receiver operating characteristic curve (AUC) were compared between Q-FMSD and conventional PET/CT methods for the diagnosis of pulmonary lesions with high 2-[18F]FDG uptake.Results:The malignant tumors presented significantly higher Q-FMSD values than the benign lesions (1.11 vs. 0.94, P<0.001), which indicated that the 2-[18F]FDG uptake in the proximal region was significantly higher than that of distal region in malignant lesions when compared with benign ones. For distinguishing hypermetabolic pulmonary malignant and benign lesions, the sensitivity, specificity and accuracy of Q-FMSD were 96.9%, 83.2% and 92.7%, respectively. Compared with other traditional methods, Q-FMSD presented significantly higher specificity than visual PET/CT (61.8%, P<0.001), retention index (RI) (33.8%, P<0.001) and SUVmax (11.0%, P<0.001). The AUC of Q-FMSD was 0.920, which was obviously larger than that of the SUVmax (0.587, P<0.001), RI (0.701, P<0.001), and visual PET/CT (0.781, P<0.001).Conclusions:Q-FMSD provides a simply and quantitative indicator for differentiating hypermetabolic pulmonary lesions with higher diagnostic performance than conventional PET/CT methods. Therefore, Q-FMSD should be recommended as a new promising marker to improve the diagnostic performance of hypermetabolic pulmonary lesions in clinical practice.