Objectives Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[ 18 F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). Materials and methods A total of 466 stage I LAC patients who underwent 2-[ 18 F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort ( n = 232, 20.3% STAS-positive), a validation cohort ( n = 122, 27.0% STAS-positive), and a test cohort ( n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUV max were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC. Results The differences in age ( P = 0.009), lesion density subtype ( P < 0.001), spiculation sign ( P < 0.001), bronchus truncation sign ( P = 0.001), and SUV max ( P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [ OR (95% CI ):3.310(1.150–9.530), P = 0.027], lesion density subtype ( P = 0.004) and SUV max ≥ 2.5 g/ml [ OR (95% CI ):3.268(1.021–1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUV max ) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively. Conclusions STAS was more likely to occur in older patients, in solid lesions and higher SUV max in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.

ObjectivesTo develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).MethodsTwo medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).ResultsTumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.ConclusionsThe radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.

Objective:To investigate the prognostic value of primary and metastatic metabolic parameters of 18F-fluorodeoxyglucose(FDG) PET/CT imaging before chemoradiotherapy in patients with esophageal squamous cell carcinoma (ESCC). Methods:A retrospective analysis was performed on 106 patients [98 males, 8 females, aged (63.9±8.8) years] with metastatic ESCC who received radiochemotherapy and underwent 18F-FDG PET/CT from November 2013 to April 2021 in the Affiliated Hospital of Qingdao University. Clinical factors included age, sex, primary location, clinical stage, degree of differentiation, and treatment. Using 40% maximum standardized uptake value (SUV max) as the threshold, delineate the region of interest (ROI) of the primary and metastatic lesions of esophageal cancer before treatment. Metabolic parameters included SUV max of primary lesion, metabolic tumor volume (MTV) of primary lesion (MTV p), total lesion glycolysis (TLG) of primary lesion (TLG p), MTV of whole body (MTV wb), TLG of whole body (TLG wb), and SUV max, MTV, TLG ratio of metastatic lesion to primary lesion (R-SUV max, R-MTV, R-TLG). Kaplan-Meier method and Log-Rank test were used for univariate analysis and multivariate analysis was conducted by Cox proportional hazards model to predict the prognostic factors affecting progression-free survival (PFS) and overall survival (OS) of patients. Results:Univariate analysis showed that T stage, MTV p, TLG p, MTV wb, TLG wb and R-TLG were prognostic factors for PFS and OS in ESCC patients receiving chemoradiotherapy ( χ2=4.105-27.992, all P<0.05). Multivariate analysis showed that T stage and R-TLG were independent prognostic factors for PFS ( HR=2.210, 95% CI: 1.307-3.737, P=0.003; HR=3.118, 95% CI: 1.414-6.875, P=0.005) and OS ( HR=1.885, 95% CI: 1.072-3.317, P=0.028; HR= 2.584, 95% CI: 1.186-5.629, P=0.017) in ESCC patients. Combined with T stage and R-TLG, the patients were divided into low-risk, medium-risk and high-risk groups. The results showed that there were statistically significant differences in PFS and OS among the groups ( χ2=38.392, 19.857; both P<0.001). Conclusion:T stage and 18F-FDG PET/CT metabolic parameter R-TLG were independent prognostic factors for PFS and OS in ESCC patients before chemoradiotherapy.

Objective:To investigate the value of 18F-FDG PET/CT imaging signs and metabolic parameters in predicting tumor spread through air spaces (STAS) of stage Ⅰ lung adenocarcinoma. Methods:From January 2019 to December 2021, clinical, imaging and metabolic parameters of 381 patients (126 males, 255 females, age (61.2±9.2) years) with stage Ⅰ lung adenocarcinoma were retrospectively analyzed in the Affiliated Hospital of Qingdao University. According to the postoperative pathological results, patients were divided into STAS positive group and STAS negative group. According to the operation time, patients were divided into training set ( n=254) and verification set ( n=127). χ2 test or Mann-Whitney U test was used to compare the differences of different parameters between patients with STAS positive and negative, and binary logistic regression analysis was used to select the predictors of STAS status. The prediction model was established, and ROC curve was used to evaluate the predictive efficacy. Results:There were 49(19.3%, 49/254) patients with STAS positive and 205(80.7%, 205/254) patients with STAS negative in the training set, while those were 35(27.6%, 35/127) and 92(72.4%, 92/127) in the verification set. In the training set, the differences of age ( z=-2.30, P=0.021), type of lesions ( χ2=6.81, P=0.009), spiculation ( χ2=12.64, P<0.001), bronchus truncation ( χ2=6.98, P=0.008), ground glass ribbon sign ( χ2=26.93, P<0.001) and SUV max ( z=-4.62, P<0.001) between the two groups were statistically significant. Multivariate logistic regression analysis showed that age (odds ratio ( OR)=1.048, 95% CI: 1.004-1.094, P=0.032), ground glass ribbon sign ( OR=3.857, 95% CI: 1.693-8.788, P=0.001) and SUV max ( OR=1.133, 95% CI: 1.001-1.282, P=0.049) were independent predictors of STAS status in stage Ⅰ lung adenocarcinoma patients. The logistic regression model was P=1/(1+ e - x), x=-5.292+ 0.480×age (year)+ 1.493×ground glass ribbon sign+ 0.170×SUV max. The AUCs of the model in the training set and verification set were 0.770 and 0.801, with the sensitivity of 81.6%(40/49) and 82.9%(29/35), and the specificity of 69.8%(143/205) and 65.2%(60/92), respectively. Conclusion:Age, ground glass ribbon sign and SUV max have good predictive effects on the occurrence of STAS in stage Ⅰ lung adenocarcinoma.

Abstract Purpose To investigate the prognostic value of baseline 18F-FDG PET/CT in patients with esophageal squamous cell carcinoma (ESCC) treated with definitive (chemo)radiotherapy. Methods A total of 98 ESCC patients with cTNM stage T1-4, N1-3, M0 who received definitive (chemo)radiotherapy after 18F-FDG PET/CT examination from December 2013 to December 2020 were retrospectively analyzed. Clinical factors included age, sex, histologic differentiation grade, tumor location, clinical stage, and treatment strategies. Parameters obtained by 18F-FDG PET/CT included SUVmax of primary tumor (SUVTumor), metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax of lymph node (SUVLN), PET positive lymph nodes (PLNS) number, the shortest distance between the farthest PET positive lymph node and the primary tumor in three-dimensional space after the standardization of the patient BSA (SDmax(LN-T)). Univariate and multivariate analysis was conducted by Cox proportional hazard model to explore the significant factors affecting overall survival (OS) and progression-free survival (PFS) in ESCC patients. Results Univariate analysis showed that tumor location, SUVTumor, MTV, TLG, PLNS number, SDmax (LN-T) were significant predictors of OS and tumor location, and clinical T stage, SUVTumor, MTV, TLG, SDmax (LN-T) were significant predictors of PFS (all p < 0.1). Multivariate analysis showed that MTV and SDmax (LN-T) were independent prognostic factors for OS (HR = 1.018, 95% CI 1.006–1.031; p = 0.005; HR = 6.988, 95% CI 2.119–23.042; p = 0.001) and PFS (HR = 1.019, 95% CI 1.005–1.034; p = 0.009; HR = 5.819, 95% CI 1.921–17.628; p = 0.002). Combined with independent prognostic factors MTV and SDmax (LN-T), we can further stratify patient risk. Conclusions Before treatment, 18F-FDG PET/CT has important prognostic value for patients with ESCC treated with definitive (chemo)radiotherapy. The lower the value of MTV and SDmax (LN-T), the better the prognosis of patients.