Objectives: To develop and validate a machine learning model using ¹⁸F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. Methods: Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without. Results: PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit. Conclusion: ¹⁸F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. Critical relevance statement: ¹⁸F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. Graphical

Background: The purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast. Methods: The preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI_2min and DCE-T1WI_7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency. Results: For ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADC_Minimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI_7min also showed significant differences between these two groups. Except for ADC_Minimum, angular second moment of FS-T2WI (FS-T2WI_ASM), and the maximum gray values of DCE-T1WI_7min (DCE-T1WI_7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADC_Contrast) and entropy of FS-T2WI (FS-T2WI_Entropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%. Conclusion: Texture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Objectives: To analyze the relationship between the dietary preparation status prior to contrast-enhanced CT (CECT) and adverse drug reactions (ADR) and emetic complications. Methods: Non-emergency adult patients who underwent routine CECT in our hospital from January 2019 to December 2020 were retrospectively analyzed. Stratified dietary preparation regimens were implemented for different clinical scenarios. The relationship between actual dietary preparation status and ADR and emetic complications was analyzed. Results: A total of 127,200 cases were enrolled, including 49,676 cases in the fasting group (57 years ± 13, 56.79% men) and 77,524 cases in the non-fasting group (60 years ± 13, 54.55% men). No statistical difference was found in the overall incidence of ADR (0.211% vs. 0.254%, p = 0.126) or emetic complications (0.030% vs. 0.046%, p = 0.158) between the two groups, and no aspiration pneumonia or death occurred. For patients with an ICM-ADR history, the ADR incidence in non-fasting group was significantly lower than fasting group (2.424% vs. 12.371%, p = 0.002). For patients with hypertension, injection dose ≥ 100 mL, injection rate ≥ 5 mL/s, and Iopromide 370 usage, non-fasting was associated with higher ADR incidence (p < 0.05). 36.67% of the patients experienced unnecessary excessive fasting in practice. Excessive fasting (≥ 10 h) and more water ingestion (≥ 500 mL) within 1 h prior to CECT were associated with higher ADR incidence (p < 0.05). Conclusion: Unrestricted food ingestion would not increase the overall risk of ADR and emetic complications. For some special patient subgroups, non-fasting, excessive fasting, and more water ingestion were associated with higher ADR incidence.

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a core needle biopsy, which influences the efficacy of neoadjuvant chemotherapy (NAC). However, the highly heterogeneous nature of breast cancer and the limitations of needle aspiration biopsy increase the instability of pathological evaluation. The aim of this study was to predict HER2 status in preoperative breast cancer using deep learning (DL) models based on ultrasound (US) and magnetic resonance imaging (MRI). The study included women with invasive breast cancer who underwent US and MRI at our institution between January 2021 and July 2024. US images and dynamic contrast-enhanced T1-weighted MRI images were used to construct DL models (DL-US: the DL model based on US; DL-MRI: the model based on MRI; and DL-MRI&US: the combined model based on both MRI and US). All classifications were based on postoperative pathological evaluation. Receiver operating characteristic analysis and the DeLong test were used to compare the diagnostic performance of the DL models. In the test cohort, DL-US differentiated the HER2 status of breast cancer with an AUC of 0.842 (95% CI: 0.708–0.931), and sensitivity and specificity of 89.5% and 79.3%, respectively. DL-MRI achieved an AUC of 0.800 (95% CI: 0.660–0.902), with sensitivity and specificity of 78.9% and 79.3%, respectively. DL-MRI&US yielded an AUC of 0.898 (95% CI: 0.777–0.967), with sensitivity and specificity of 63.2% and 100.0%, respectively.