Advanced MRI techniques, motion-correction and T2*-relaxometry, may provide information regarding functional properties of pulmonary tissue. We assessed whether lung volumes and pulmonary T2* values in fetuses with congenital diaphragmatic hernia (CDH) were lower than controls and differed between survivors and non-survivors. Women with uncomplicated pregnancies (controls) and those with a CDH had a fetal MRI on a 1.5 T imaging system encompassing T2 single shot fast spin echo sequences and gradient echo single shot echo planar sequences providing T2* data. Motion-correction was performed using slice-to-volume reconstruction, T2* maps were generated using in-house pipelines. Lungs were segmented separately using a pre-trained 3D-deep-learning pipeline. Datasets from 33 controls and 12 CDH fetuses were analysed. The mean ± SD gestation at scan was 28.3 ± 4.3 for controls and 27.6 ± 4.9 weeks for CDH cases. CDH lung volumes were lower than controls in both non-survivors and survivors for both lungs combined (5.76 ± 3.59 [cc], mean difference = 15.97, 95% CI: -24.51--12.9, p < 0.001 and 5.73 ± 2.96 [cc], mean difference = 16, 95% CI: 1.91-11.53, p = 0.008) and for the ipsilateral lung (1.93 ± 2.09 [cc], mean difference = 19.8, 95% CI: -28.48--16.45, p < 0.001 1.58 ± 1.18 [cc], mean difference=20.15, 95% CI: 5.96-15.97, p < 0.001). Mean pulmonary T2* values were lower in non-survivors in both lungs, the ipsilateral and contralateral lungs compared with the control group (81.83 ± 26.21 ms, mean difference = 31.13, 95% CI: -58.14--10.32, p = 0.006; 81.05 ± 26.84 ms, mean difference = 31.91, 95% CI: -59.02--10.82, p = 0.006; 82.62 ± 36.31 ms, mean difference = 30.34, 95% CI: -58.84--8.25, p = 0.011) but no difference was observed between controls and CDH cases that survived. Mean pulmonary T2* values were lower in CDH fetuses compared to controls and CDH cases who died compared to survivors. Mean pulmonary T2* values may have a prognostic function in CDH fetuses. This study provides original motion-corrected assessment of the morphologic and functional properties of the ipsilateral and contralateral fetal lungs in the context of CDH. Mean pulmonary T2* values were lower in CDH fetuses compared to controls and in cases who died compared to survivors. Mean pulmonary T2* values may have a role in prognostication. Reduction in pulmonary T2* values in CDH fetuses suggests altered pulmonary development, contributing new insights into antenatal assessment.

White matter hyperintensities (WMH) are commonly assessed using the Fazekas scale, a subjective visual grading system. Despite the emergence of deep learning models for automatic WMH grading, their application in stroke patients remains limited. This study aimed to develop and validate an automatic segmentation and grading model for WMH in stroke patients, utilizing spatial-probabilistic methods. We developed a two-step deep learning pipeline to predict Fazekas scale scores from T2-weighted FLAIR images. First, WMH segmentation was performed using a residual neural network based on the U-Net architecture. Then, Fazekas scale grading was carried out using a 3D convolutional neural network trained on the segmented WMH probability volumes. A total of 471 stroke patients from three different sources were included in the analysis. The performance metrics included area under the precision-recall curve (AUPRC), Dice similarity coefficient, and absolute error for WMH volume prediction. In addition, agreement analysis and quadratic weighted kappa were calculated to assess the accuracy of the Fazekas scale predictions. The WMH segmentation model achieved an AUPRC of 0.81 (95% CI, 0.55-0.95) and a Dice similarity coefficient of 0.73 (95% CI, 0.49-0.87) in the internal test set. The mean absolute error between the true and predicted WMH volumes was 3.1 ml (95% CI, 0.0 ml-15.9 ml), with no significant variation across Fazekas scale categories. The agreement analysis demonstrated strong concordance, with an R-squared value of 0.91, a concordance correlation coefficient of 0.96, and a systematic difference of 0.33 ml in the internal test set, and 0.94, 0.97, and 0.40 ml, respectively, in the external validation set. In predicting Fazekas scores, the 3D convolutional neural network achieved quadratic weighted kappa values of 0.951 for regression tasks and 0.956 for classification tasks in the internal test set, and 0.898 and 0.956, respectively, in the external validation set. The proposed deep learning pipeline demonstrated robust performance in automatic WMH segmentation and Fazekas scale grading from FLAIR images in stroke patients. This approach offers a reliable and efficient tool for evaluating WMH burden, which may assist in predicting future vascular events.

Glioblastoma multiforme (GBM) is a lethal brain tumor with limited therapies. NUF2, a kinetochore protein involved in cell cycle regulation, shows oncogenic potential in various cancers; however, its role in GBM pathogenesis remains unclear. In this study, we investigated NUF2's function and mechanisms in GBM and developed an MRI-based machine learning model to predict its expression non-invasively, and evaluated its potential as a therapeutic target and prognostic biomarker. Functional assays (proliferation, colony formation, migration, and invasion) and cell cycle analysis were conducted using NUF2-knockdown U87/U251 cells. Western blotting was performed to assess the expression levels of β-catenin and MMP-9. Bioinformatic analyses included pathway enrichment, immune infiltration, and single-cell subtype characterization. Using preoperative T1CE Magnetic Resonance Imaging sequences from 61 patients, we extracted 1037 radiomic features and developed a predictive model using Least Absolute Shrinkage and Selection Operator regression for feature selection and random forest algorithms for classification with rigorous cross-validation. NUF2 overexpression in GBM tissues and cells was correlated with poor survival (p < 0.01). Knockdown of NUF2 significantly suppressed malignant phenotypes (p < 0.05), induced G0/G1 arrest (p < 0.01), and increased sensitivity to TMZ treatment via the β-catenin/MMP9 pathway. The radiomic model achieved superior NUF2 prediction (AUC = 0.897) using six optimized features. Key features demonstrated associations with MGMT methylation and 1p/19q co-deletion, serving as independent prognostic markers. NUF2 drives GBM progression through β-catenin/MMP9 activation, establishing its dual role as a therapeutic target and a prognostic biomarker. The developed radiogenomic model enables precise non-invasive NUF2 evaluation, thereby advancing personalized GBM management. This study highlights the translational value of integrating molecular biology with artificial intelligence in neuro-oncology.

Lung cancer is the most common cause of cancer-related deaths worldwide, and early detection is extremely important for improving survival. According to the National Institute of Health Sciences, lung cancer has the highest rate of cancer mortality, according to the National Institute of Health Sciences. Medical professionals are usually based on clinical imaging methods such as MRI, X-ray, biopsy, ultrasound, and CT scans. However, these imaging techniques often face challenges including false positives, false negatives, and sensitivity. Deep learning approaches, particularly folding networks (CNNS), have arisen as they tackle these issues. However, traditional CNN models often suffer from high computing complexity, slow inference times and over adaptation in real-world clinical data. To overcome these limitations, we propose an optimized sequential folding network (SCNN) that maintains a high level of classification accuracy, simultaneously reducing processing time and computing load. The SCNN model consists of three folding layers, three maximum pooling layers, flat layers and dense layers, allowing for efficient and accurate classification. In the histological imaging dataset, three categories of lung cancer models are adenocarcinoma, benign and squamous cell carcinoma. Our SCNN achieves an average accuracy of 95.34%, an accuracy of 95.66%, a recall of 95.33%, and an F1 score of over 60 epochs within 1000 seconds. These results go beyond traditional CNN, R-CNN, and custom inception classifiers, indicating superior speed and robustness in histological image classification. Therefore, SCNN offers a practical and scalable solution to improve lung cancer awareness in clinical practice.

Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation. From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT). Machine learning predicted the presence of CAD on CCTA (area under the curve (AUC) 0.80, 95% CI 0.74 to 0.85) better than the 10-year cardiovascular risk score alone (AUC 0.75, 95% CI 0.70, 0.81, p=0.004). The most important features in this model were the 10-year cardiovascular risk score, age, sex, total cholesterol and an abnormal ETT. In contrast, the second model used to predict an increased LAP burden performed similarly to the 10-year cardiovascular risk score (AUC 0.75, 95% CI 0.70 to 0.80 vs AUC 0.72, 95% CI 0.66 to 0.77, p=0.08) with the most important features being the 10-year cardiovascular risk score, age, body mass index and total and high-density lipoprotein cholesterol concentrations. Machine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.

The aim of this study is to develop a multimodal machine learning model that integrates magnetic resonance imaging (MRI) radiomics, deep learning features, and clinical indexes to predict the 3-year postoperative disease-free survival (DFS) in pediatric patients with malignant tumors. A cohort of 260 pediatric patients with brain tumors who underwent R0 resection (aged ≤ 14 y) was retrospectively included in the study. Preoperative T1-enhanced MRI images and clinical data were collected. Image preprocessing involved N4 bias field correction and Z-score standardization, with tumor areas manually delineated using 3D Slicer. A total of 1130 radiomics features (Pyradiomics) and 511 deep learning features (3D ResNet-18) were extracted. Six machine learning models (eg, SVM, RF, LightGBM) were developed after dimensionality reduction through Lasso regression analysis, based on selected clinical indexes such as tumor diameter, GCS score, and nutritional status. Bayesian optimization was applied to adjust model parameters. The evaluation metrics included AUC, sensitivity, and specificity. The fusion model (LightGBM) achieved an AUC of 0.859 and an accuracy of 85.2% in the validation set. When combined with clinical indexes, the final model's AUC improved to 0.909. Radiomics features, such as texture heterogeneity, and clinical indexes, including tumor diameter ≥ 5 cm and preoperative low albumin, significantly contributed to prognosis prediction. The multimodal model demonstrated effective prediction of the 3-year postoperative DFS in pediatric brain tumors, offering a scientific foundation for personalized treatment.

To evaluate a deep-learning algorithm for automated coronary artery analysis on ultrahigh-resolution photon-counting detector coronary computed tomography (CT) angiography and compared its performance to expert readers using invasive coronary angiography as reference. Thirty-two patients (mean age 68.6 years; 81 ​% male) underwent both energy-integrating detector and ultrahigh-resolution photon-counting detector CT within 30 days. Expert readers scored each image using the Coronary Artery Disease-Reporting and Data System classification, and compared to invasive angiography. After a three-month wash-out, one reader reanalyzed the photon-counting detector CT images assisted by the algorithm. Sensitivity, specificity, accuracy, inter-reader agreement, and reading times were recorded for each method. On 401 arterial segments, inter-reader agreement improved from substantial (κ ​= ​0.75) on energy-integrating detector CT to near-perfect (κ ​= ​0.86) on photon-counting detector CT. The algorithm alone achieved 85 ​% sensitivity, 91 ​% specificity, and 90 ​% accuracy on energy-integrating detector CT, and 85 ​%, 96 ​%, and 95 ​% on photon-counting detector CT. Compared to invasive angiography on photon-counting detector CT, manual and automated reads had similar sensitivity (67 ​%), but manual assessment slightly outperformed regarding specificity (85 ​% vs. 79 ​%) and accuracy (84 ​% vs. 78 ​%). When the reader was assisted by the algorithm, specificity rose to 97 ​% (p ​< ​0.001), accuracy to 95 ​%, and reading time decreased by 54 ​% (p ​< ​0.001). This deep-learning algorithm demonstrates high agreement with experts and improved diagnostic performance on photon-counting detector CT. Expert review augmented by the algorithm further increases specificity and dramatically reduces interpretation time.

Proliferative hepatocellular carcinoma (HCC) is an aggressive tumor with varying prognosis depending on the different disease stages and subsequent treatment. This study aims to develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced CT to predict proliferative HCC and to implement risk prediction in patients treated with transarterial chemoembolization (TACE) and radiofrequency ablation (RFA). 312 patients (mean age, 58 years ± 10 [SD]; 261 men and 51 women) with HCC undergoing surgery at two medical centers were included, who were divided into a training set (<i>n</i> = 182), an internal test set (<i>n</i> = 46) and an external test set (<i>n</i> = 84). DLR features were extracted from preoperative contrast-enhanced CT images. Multiple machine learning algorithms were used to develop and validate proliferative HCC prediction models in training and test sets. Subsequently, patients from two independent new sets (RFA and TACE sets) were divided into high- and low-risk groups using the DLR score generated by the optimal model. The risk prediction value of DLR scores in recurrence-free survival (RFS) and time to progression (TTP) was examined separately in RFA and TACE sets. The DLR proliferative HCC prediction model demonstrated excellent predictive performance with an AUC of 0.906 (95% CI 0.861–0.952) in the training set, 0.901 (95% CI 0.779–1.000) in the internal test set and 0.837 (95% CI 0.746–0.928) in the external test set. The DLR score effectively enables risk prediction for patients in RFA and TACE sets. For the RFA set, the low-risk group had significantly longer RFS compared to the high-risk group (<i>P</i> = 0.037). Similarly, the low-risk group showed a longer TTP than the high-risk group for the TACE set (<i>P</i> = 0.034). The DLR-based contrast-enhanced CT model enables non-invasive prediction of proliferative HCC. Furthermore, the DLR risk prediction helps identify high-risk patients undergoing RFA or TACE, providing prognostic insights for personalized management. The online version contains supplementary material available at 10.1186/s12880-025-01913-9.

Assessing lymph node metastasis (LNM) involvement in patients with rectal cancer (RC) is fundamental in disease management. In this study, we used artificial intelligence (AI) technology to develop a segmentation model that automatically segments the tumor core area and mesangial tissue from magnetic resonance T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) images collected from 122 RC patients to improve the accuracy of LNM prediction, after which omics machine modeling was performed on the segmented ROI. An automatic segmentation model was developed using nn-UNet. This pipeline integrates deep learning (DL), specifically 3D U-Net, for semantic segmentation and image processing techniques such as resampling, normalization, connected component analysis, image registration, and radiomics features coupled with machine learning. The results showed that the DL segmentation method could effectively segment the tumor and mesangial areas from MR sequences (the median dice coefficient: 0.90 ± 0.08; mesorectum segmentation: 0.85 ± 0.36), and the radiological characteristics of rectal and mesangial tissues in T2WI and ADC images could help distinguish RC treatments. The nn-UNet model demonstrated promising preliminary results, achieving the highest area under the curve (AUC) values in various scenarios. In the evaluation encompassing both tumor lesions and mesorectum involvement, the model exhibited an AUC of 0.743, highlighting its strong discriminatory ability to predict a combined outcome involving both elements. Specifically targeting tumor lesions, the model achieved an AUC of 0.731, emphasizing its effectiveness in distinguishing between positive and negative cases of tumor lesions. In assessing the prediction of mesorectum involvement, the model displayed moderate predictive utility with an AUC of 0.753. The nn-UNet model demonstrated impressive performance across all evaluated scenarios, including combined tumor lesions and mesorectum involvement, tumor lesions alone, and mesorectum involvement alone. The online version contains supplementary material available at 10.1186/s12880-025-01878-9.

Accurate virtual orbital reconstruction is crucial for preoperative planning. Traditional methods, such as the mirroring technique, are unsuitable for orbital defects involving both sides of the midline and are time-consuming and labor-intensive. This study introduces a modified 3D U-Net+++ architecture for orbital defects reconstruction, aiming to enhance precision and automation. The model was trained and tested with 300 synthetic defects from cranial spiral CT scans. The method was validated in 15 clinical cases of orbital fractures and evaluated using quantitative metrics, visual assessments, and a 5-point Likert scale, by 3 surgeons. For synthetic defect reconstruction, the network achieved a 95% Hausdorff distance (HD95) of<2.0 mm, an average symmetric surface distance (ASSD) of ∼0.02 mm, a surface Dice similarity coefficient (Surface DSC)>0.94, a peak signal-to-noise ratio (PSNR)>35 dB, and a structural similarity index (SSIM)>0.98, outperforming the compared state-of-the-art networks. For clinical cases, the average 5-point Likert scale scores for structural integrity, edge consistency, and overall morphology were>4, with no significant difference between unilateral and bilateral/trans-midline defects. For clinical unilateral defect reconstruction, the HD95 was ∼2.5 mm, ASSD<0.02 mm, Surface DSC>0.91, PSNR>30 dB, and SSIM>0.99. The automatic reconstruction process took ∼10 seconds per case. In conclusion, this method offers a precise and highly automated solution for orbital defect reconstruction, particularly for bilateral and trans-midline defects. We anticipate that this method will significantly assist future clinical practice.