Background: Non-invasive discrimination between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes of non-small-cell lung cancer (NSCLC) could be very beneficial to the patients unfit for the invasive diagnostic procedures. The aim of this study was to investigate the feasibility of utilizing the multimodal magnetic resonance imaging (MRI) radiomics and clinical features in classifying NSCLC. This retrospective study involved 148 eligible patients with postoperative pathologically confirmed NSCLC. The study was conducted in three steps: (1) feature extraction was performed using the online freely available package with the multimodal MRI data; (2) feature selection was performed using the Student's t test and support vector machine (SVM)-based recursive feature elimination method with the training cohort (n = 100), and the performance of these selected features was evaluated using both the training and the validation cohorts (n = 48) with a non-linear SVM classifier; (3) a Radscore model was then generated using logistic regression algorithm; (4) Integrating the Radscore with the semantic clinical features, a radiomics-clinical nomogram was developed, and its overall performance was evaluated with both cohorts. Results: Thirteen optimal features achieved favorable discrimination performance with both cohorts, with area under the curve (AUC) of 0.819 and 0.824, respectively. The radiomics-clinical nomogram integrating the Radscore with the independent clinical predictors exhibited more favorable discriminative power, with AUC improved to 0.901 and 0.872 in both cohorts, respectively. The Hosmer-Lemeshow test and decision curve analysis results furtherly showed good predictive precision and clinical usefulness of the nomogram. Conclusion: Non-invasive histological subtype stratification of NSCLC can be done favorably using multimodal MRI radiomics features. Integrating the radiomics features with the clinical features could further improve the performance of the histological subtype stratification in patients with NSCLC.

Background: The histological grading plays an essential role in the treatment decision of lung cancer. Detected tumors are usually biopsied to confirm histologic grade. How to use MRI extracted radiomics features for accurately grading lung cancer is still challenging. Purpose: To examine the diagnostic utility of multiparametric MRI radiomics and clinical factors for grading non-small-cell lung cancer (NSCLC). Study type: Retrospective. Population: A total of 148 patients (25.7% female) with postoperative pathologically confirmed NSCLC and divided into the training cohort (N = 110) and the validation cohort (N = 38). Field Strength/Sequence: A 1.5 T; single-shot turbo spin-echo (TSE), T2-weighted imaging (T2WI), and integrated shimming-echo planar imaging (ISHIM-EPI) diffusion-weighted imaging (DWI). Assessment: A total of 2775 radiomics features were extracted from carcinomatous regions of interest on T2WI, DWI, and the apparent diffusion coefficient (ADC) maps. The five optimal features were selected by using the Student' s t-test, the least absolute shrinkage and selection operator (LASSO) and stepwise regression. The Radscore combined with clinical factors, which selected by univariate and multivariate analyses, to develop a radiomicsclinical nomogram. Its performance was evaluated in the training cohort and the validation cohort. The potential clinical usefulness was analyzed by the receiver operating characteristic curve (ROC), area under the curve (AUC), and the Hosmer-Lemeshow test. Statistical Tests: Student's t-test, univariate analyses, multivariate analyses, LASSO, ROC, AUC, and the Hosmer-Lemeshow test. P < 0.05 was considered statistically significant. Results: Favorable discrimination performance was obtained for five optimal features (out of the 2775 features), using the training cohorts (AUC 0.761) and validation cohorts (AUC 0.753). In addition, the radiomics-clinical nomogram significantly improved the ability to identify histological grades in the training cohort (AUC 0.814) and the validation cohort (AUC 0.767). Data Conclusions: The radiomics-clinical nomogram based on multiparametric MRI might have the potential to distinguish the histological grade of NSCLC.

Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC. Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.

Purpose To determine the diagnostic value of fetal magnetic resonance imaging (MRI) for congenital spine/spinal cord malformations. Methods This single‐center retrospective study included 120 cases of fetal spine/spinal cord abnormalities detected using fetal ultrasonography (US) and further examined by fetal MRI between 2016 and 2020. Cases were divided into three groups (congenital spine, spinal cord, and spine + spinal cord malformations) based on US assessment. We analyzed the accuracy of fetal US and MRI relative to postnatal imaging. Results The diagnostic accuracy of fetal MRI for fetal spinal cord, spine, and spine + spinal cord malformations was 86.2% (25/29), 89.4% (42/47), and 86.3% (38/44), respectively, and the corresponding rates for fetal US were 51.7% (15/29), 87.2% (41/47), and 68.2% (30/44). The diagnostic accuracy did not differ between fetal MRI and US for congenital spine malformations (p > 0.05); for congenital spinal cord malformations and congenital spine + spinal cord malformations, the diagnostic accuracy was significantly higher for fetal MRI than for fetal US (p < 0.05). Conclusions Fetal MRI is effective in the assessment of congenital spine/spinal cord malformations. It can yield information that supplements US findings, especially for congenital spinal cord malformations, and can improve the accuracy of fetal diagnosis.