A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study.

OBJECTIVE: To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS: The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION: The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus.

Objective: To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus. Materials and methods: In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model. Results: Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models. Conclusion: Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.

Super Resolution of Pulmonary Nodules Target Reconstruction Using a Two-Channel GAN Models.

RATIONALE AND OBJECTIVES: To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets. MATERIALS AND METHODS: This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed. RESULTS: DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation. CONCLUSION: Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets.

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables.

Purpose: The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes. Methods: A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC. Results: A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86. Conclusion: Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids.

Background: The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it is difficult for an accurate prediction of the efficacy of high intensity focused ultrasound (HIFU) ablation in fibroids before the treatment. The purpose of this study was to set up a radiomics model based on MRI T2-weighted imaging (T2WI) for predicting the efficacy of HIFU ablation in uterine fibroids, and it would be used in preoperative screening of the fibroids for achieving high non-perfused volume ratio (NPVR). Methods: A total of 178 patients with uterine fibroids were consecutively enrolled and treated with ultrasound-guided HIFU under conscious sedation between February 2017 and December 2021. Among them, 96 patients with 108 uterine fibroids with high ablation efficacy (NPVR ≥80%, h_NPVR) and 82 patients with 92 fibroids with lower ablation efficacy (NPVR <80%, l_NPVR) were retrospectively analyzed. The transverse T2WI images of fibroids were selected, and the fibroids were delineated slice by slice using ITK-SNAP software. The radiomics analysis was performed to find the imaging biomarker for the construction of a predicting model for the evaluation of the ablation efficacy, including the feature extraction, feature selection and model construction. The prediction model was built by logistic regression and assessed by receiver operating characteristic (ROC) curve, and the prediction efficiency of the two models was compared by Delong test. The ratio of the training set to the testing set was 8:2. Results: The logistic regression model showed that the mean area under the curve (AUC) of the training set was 0.817 [95% confidence interval (CI): 0.755-0.882], and the testing set was 0.805 (95% CI: 0.670-0.941), respectively, which indicated a strong classification ability. The Delong test showed that there was no significant difference in the area under the ROC curve between the training set and testing set (P>0.05). Conclusions: The radiomics model based on T2WI is feasible and effective for predicting the efficacy of HIFU ablation in treatment of uterine fibroids.

Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma.

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis. The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation, with a Dice score of 0.87. The nuclear-grade prediction model performed best in the logistic regression classifier, with area under curve values from 0.782 to 0.746. Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk, with a hazard ratio (HR) of 2.49 [95% confidence interval (CI): 1.13-5.45, P = 0.023] in the General cohort. Excellent performance had also been verified in the Cancer Genome Atlas cohort, the Clinical Proteomic Tumor Analysis Consortium cohort, and the Kidney Tumor Segmentation Challenge cohort, with HRs of 2.77 (95%CI: 1.58-4.84, P = 0.0019), 3.83 (95%CI: 1.22-11.96, P = 0.029), and 2.80 (95%CI: 1.05-7.47, P = 0.025), respectively. In conclusion, we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRCC. The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments, which could provide practical advice for deciding treatment options.

Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI.

RATIONALE AND OBJECTIVES: To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram. METHODS: A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts. RESULTS: MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram. CONCLUSION: The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.

Evaluation of an automated intracranial aneurysm detection and rupture analysis approach using cascade detection and classification networks.

Intracranial aneurysm is commonly found in human brains especially for the elderly, and its rupture accounts for a high rate of subarachnoid hemorrhages. However, it is time-consuming and requires special expertise to pinpoint small aneurysms from computed tomography angiography (CTA) images. Deep learning-based detection has helped improve much efficiency but false-positives still render difficulty to be ruled out. To study the feasibility of deep learning algorithms for aneurysm analysis in clinical applications, this paper proposes a pipeline for aneurysm detection, segmentation, and rupture classification and validates its performance using CTA images of 1508 subjects. A cascade aneurysm detection model is employed by first using a fine-tuned feature pyramid network (FPN) for candidate detection and then applying a dual-channel ResNet aneurysm classifier to further reduce false positives. Detected aneurysms are then segmented by applying a traditional 3D V-Net to their image patches. Radiomics features of aneurysms are extracted after detection and segmentation. The machine-learning-based and deep learning-based rupture classification can be used to distinguish ruptured and un-ruptured ones. Experimental results show that the dual-channel ResNet aneurysm classifier utilizing image and vesselness information helps boost sensitivity of detection compared to single image channel input. Overall, the proposed pipeline can achieve a sensitivity of 90 % for 1 false positive per image, and 95 % for 2 false positives per image. For rupture classification the area under curve (AUC) of 0.906 can be achieved for the testing dataset. The results suggest feasibility of the pipeline for potential clinical use to assist radiologists in aneurysm detection and classification of ruptured and un-ruptured aneurysms.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.

Objective: We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance. Methods: The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test. Results: The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model. Conclusion: In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Comparable Performance of Deep Learning-Based to Manual-Based Tumor Segmentation in KRAS/NRAS/BRAF Mutation Prediction With MR-Based Radiomics in Rectal Cancer.

Radiomic features extracted from segmented tumor regions have shown great power in gene mutation prediction, while deep learning-based (DL-based) segmentation helps to address the inherent limitations of manual segmentation. We therefore investigated whether deep learning-based segmentation is feasible in predicting KRAS/NRAS/BRAF mutations of rectal cancer using MR-based radiomics. In this study, we proposed DL-based segmentation models with 3D V-net architecture. One hundred and eight patients' images (T2WI and DWI) were collected for training, and another 94 patients' images were collected for validation. We evaluated the DL-based segmentation manner and compared it with the manual-based segmentation manner through comparing the gene prediction performance of six radiomics-based models on the test set. The performance of the DL-based segmentation was evaluated by Dice coefficients, which are 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI, respectively. The performance of the radiomics-based model in gene prediction based on DL-segmented VOI was evaluated by AUCs (0.714 for T2WI, 0.816 for DWI, and 0.887 for T2WI+DWI), which were comparable to that of corresponding manual-based VOI (0.637 for T2WI, P=0.188; 0.872 for DWI, P=0.181; and 0.906 for T2WI+DWI, P=0.676). The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. All-relevant radiomics-based models presented similar performances in KRAS/NRAS/BRAF prediction between the two segmentation manners.

Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy.

BACKGROUND: Whether peritumoral dilation radiomics can excellently predict early recrudescence (≤2 years) in hepatocellular carcinoma (HCC) remains unclear. METHODS: Between March 2012 and June 2018, 323 pathologically confirmed HCC patients without macrovascular invasion, who underwent liver resection and preoperative gadoxetate disodium (Gd-EOB-DTPA) MRI, were consecutively recruited into this study. Multivariate logistic regression identified independent clinicoradiologic predictors of 2-year recrudescence. Peritumoral dilation (tumor and peritumoral zones within 1cm) radiomics extracted features from 7-sequence images for modeling and achieved average but robust predictive performance through 5-fold cross validation. Independent clinicoradiologic predictors were then incorporated with the radiomics model for constructing a comprehensive nomogram. The predictive discrimination was quantified with the area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI). RESULTS: With the median recurrence-free survival (RFS) reaching 60.43 months, 28.2% (91/323) and 16.4% (53/323) patients suffered from early and delay relapse, respectively. Microvascular invasion, tumor size >5 cm, alanine aminotransferase >50 U/L, γ-glutamyltransferase >60 U/L, prealbumin ≤250 mg/L, and peritumoral enhancement independently impaired 2-year RFS in the clinicoradiologic model with AUC of 0.694 (95% CI 0.628-0.760). Nevertheless, these indexes were paucity of robustness (P >0.05) when integrating with 38 most recurrence-related radiomics signatures for developing the comprehensive nomogram. The peritumoral dilation radiomics-the ultimate prediction model yielded satisfactory mean AUCs (training cohort: 0.939, 95% CI 0.908-0.973; validation cohort: 0.842, 95% CI 0.736-0.951) after 5-fold cross validation and fitted well with the actual relapse status in the calibration curve. Besides, our radiomics model obtained the best clinical net benefits, with significant improvements of NRI (35.9%-66.1%, P <0.001) versus five clinical algorithms: the clinicoradiologic model, the tumor-node-metastasis classification, the Barcelona Clinic Liver Cancer stage, the preoperative and postoperative risks of Early Recurrence After Surgery for Liver tumor. CONCLUSION: Gd-EOB-DTPA MRI-based peritumoral dilation radiomics is a potential preoperative biomarker for early recurrence of HCC patients without macrovascular invasion.