No abstract

ObjectiveWe aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.Methods170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics.ResultsUsing quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance.ConclusionThe use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.

Background The role of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT), measured by coronary CT angiography (CCTA), as cardiometabolic risk factors in heart failure patients with preserved ejection fraction (HFpEF) remains unclear. In this study, we aimed to evaluate the CCTA-derived EAT and PCAT in HFpEF patients and examine their association with cardiac function and prognostic value.Methods Between January 2019 and July 2022, 100 HFpEF patients and 100 healthy controls underwent CCTA to investigate the association between EAT and PCAT and composite endpoints for HFpEF. The composite endpoint was defined as a combination of all-cause mortality and rehospitalization for HF. EAT volume and PCAT attenuation were measured using automatic threshold segmentation in CCTA images, with thresholds set between − 30 and − 190 HU. Univariate and multivariate Cox regression models were used, including EAT, PCAT, and a cardiac metabolic risk score (incorporating age, sex, smoking, metabolic syndrome, and family history). The optimal cut-off point was determined using the Youden index. Survival estimates were calculated using Kaplan-Meier curves with the log-rank test.Results A total of 200 patients, with a mean age of 68.3 ± 10.3 years and 58.0% male, were retrospectively analyzed. Among them, 100 HFpEF patients (mean age: 71.7 ± 9.9 years; 59% male) were followed up for a median of 15 ± 0.6 months (range 2–29 months). Compared to healthy controls, HFpEF patients had higher EAT volume (56.1 cm3 ± 11.9) and lower attenuations in the right coronary artery (RCA) (-74.7 HU ± 3.82), left anterior descending artery (LAD) (-72.9 HU ± 3.98), and left circumflex artery (LCX) (-71.5 HU ± 3.06). EAT and PCAT-RCA attenuation were predictive of outcome with an optimal threshold of 56.29 cm3 (AUC: 0.77; sensitivity: 72.0%; specificity: 74%) and − 69.31 HU (AUC: 0.793; sensitivity: 76.9%; specificity: 74.1%), respectively.Conclusions We conclude that, in heart failure patients, EAT and PCAT-RCA add independent and incremental prognostic value of predicting HFpEF progression, superior to clinical risk factors.