In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity. A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations. Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R² = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm²), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R² = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations. MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

This study considers the application of ultrasound placental image texture analysis for the prediction of hypertensive disorders of pregnancy (HDP) using deep learning (DL) algorithm. In this prospective observational study, placental ultrasound images were taken serially at 11-14 weeks (T1), 20-24 weeks (T2), and 28-32 weeks (T3). Pregnant women with blood pressure at or above 140/90 mmHg on two occasions 4 h apart were considered to have HDP. The image data of women with HDP were compared with those with a normal outcome using DL techniques such as convolutional neural networks (CNN), transfer learning, and a Vision Transformer (ViT) with a TabNet classifier. The accuracy and the Cohen kappa scores of the different DL techniques were compared. A total of 600/1008 (59.5%) subjects had a normal outcome, and 143/1008 (14.2%) had HDP; the reminder, 265/1008 (26.3%), had other adverse outcomes. In the basic CNN model, the accuracy was 81.6% for T1, 80% for T2, and 82.8% for T3. Using the Efficient Net B0 transfer learning model, the accuracy was 87.7%, 85.3%, and 90.3% for T1, T2, and T3, respectively. Using a TabNet classifier with a ViT, the accuracy and area under the receiver operating characteristic curve scores were 91.4% and 0.915 for T1, 90.2% and 0.904 for T2, and 90.3% and 0.907 for T3. The sensitivity and specificity for HDP prediction using ViT were 89.1% and 91.7% for T1, 86.6% and 93.7% for T2, and 85.6% and 94.6% for T3. Ultrasound placental image texture analysis using DL could differentiate women with a normal outcome and those with HDP with excellent accuracy and could open new avenues for research in this field.

Glial tumors and especially glioblastoma present a major challenge in neuro-oncology due to their infiltrative growth, resistance to therapy, and poor overall survival-despite aggressive treatments such as maximal safe resection and chemoradiotherapy. These tumors typically manifest through neurological symptoms such as seizures, headaches, and signs of increased intracranial pressure, prompting urgent neuroimaging. At initial diagnosis, MRI plays a central role in differentiating true neoplasms from tumor mimics, including inflammatory or infectious conditions. Advanced techniques such as perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) enhance diagnostic specificity and may prevent unnecessary surgical intervention. In the preoperative phase, MRI contributes to surgical planning through the use of functional MRI (fMRI) and diffusion tensor imaging (DTI), enabling localization of eloquent cortex and white matter tracts. These modalities support safer resections by informing trajectory planning and risk assessment. Emerging MR techniques, including magnetic resonance spectroscopy, amide proton transfer imaging, and 2HG quantification, offer further potential in delineating tumor infiltration beyond contrast-enhancing margins. Postoperatively, MRI is important for evaluating residual tumor, detecting surgical complications, and guiding radiotherapy planning. During treatment surveillance, MRI assists in distinguishing true progression from pseudoprogression or radiation necrosis, thereby guiding decisions on additional surgery, changes in systemic therapy, or inclusion into clinical trials. The continued evolution of MRI hardware, software, and image analysis-particularly with the integration of machine learning-will be critical for supporting precision neurosurgical oncology. This review highlights how advanced MRI techniques can inform clinical decision-making at each stage of care in patients with high-grade gliomas.

Diseases underlying osteolytic lesions in jaws are characterized by the absorption of bone tissue and are often asymptomatic, delaying their diagnosis. Well-defined lesions (benign cyst-like lesions) and ill-defined lesions (osteomyelitis or malignancy) can be detected early in a panoramic radiograph (PR) by an experienced examiner, but most dentists lack appropriate training. To support dentists, this study aimed to develop and evaluate deep learning models for the detection of osteolytic lesions in PRs. A dataset of 676 PRs (165 well-defined, 181 ill-defined, 330 control) was collected from the Department of Oral and Maxillofacial Surgery at Charité Berlin, Germany. The osteolytic lesions were pixel-wise segmented and labeled as well-defined or ill-defined. Four model architectures for instance segmentation (Mask R-CNN with a Swin-Tiny or ResNet-50 backbone, Mask DINO, and YOLOv5) were employed with five-fold cross-validation. Their effectiveness was evaluated with sensitivity, specificity, F1-score, and AUC and failure cases were shown. Mask R-CNN with a Swin-Tiny backbone was most effective (well-defined F1 = 0.784, AUC = 0.881; ill-defined F1 = 0.904, AUC = 0.971) and the model architectures including vision transformer components were more effective than those without. Model mistakes were observed around the maxillary sinus, at tooth extraction sites, and for radiolucent bands. Promising deep learning models were developed for the detection of osteolytic lesions in PRs, particularly those with vision transformer components (Mask R-CNN with Swin-Tiny and Mask DINO). These results underline the potential of vision transformers for enhancing the automated detection of osteolytic lesions, offering a significant improvement over traditional deep learning models.

The therapeutic management of neoplasm employs high level energy beam to ablate malignant cells, which can cause collateral damage to adjacent normal tissue. Furthermore, respiration-induced organ motion, during radiotherapy can lead to significant displacement of neoplasms. In this work, a non-invasive ultrasound-based deep learning algorithm for respiratory motion compensation system (RMCS) was developed to mitigate the effect of respiratory motion induced neoplasm movement in radiotherapy. The deep learning algorithm generated based on modified YOLOv8n (You Only Look Once), by incorporating squeeze and excitation blocks for channel wise recalibration and enhanced attention mechanisms for spatial channel focus (SE-ATT-YOLO) to cope up with enhanced ultrasound image detection in real time scenario. The trained model was inferred with ultrasound movement of human diaphragm and tracked the bounding box coordinates using BoT-Sort, which drives the RMCS. The SE-ATT-YOLO model achieved mean average precision (mAP) of 0.88 which outperforms YOLOv8n with the value of 0.85. The root mean square error (RMSE) obtained from prerecorded respiratory signals with the compensated RMCS signal was calculated. The model achieved an inference speed of approximately 50 FPS. The RMSE values recorded were 4.342 for baseline shift, 3.105 for sinusoidal signal, 1.778 for deep breath, and 1.667 for slow signal. The SE-ATT-YOLO model outperformed all the results of previous models. The loss function uncertainty in YOLOv8n model was rectified in SE-ATT YOLO depicting the stability of the model. The model' stability, speed and accuracy of the model optimized the performance of the RMCS.

To develop a radiomics risk-predictive model for differentiating the different stages of Hashimoto's thyroiditis (HT). Data from patients with HT who underwent definitive surgical pathology between January 2018 and December 2023 were retrospectively collected and categorized into early HT (HT patients with simple positive antibodies or simultaneously accompanied by elevated thyroid hormones) and late HT (HT patients with positive antibodies and beginning to present subclinical hypothyroidism or developing hypothyroidism). Ultrasound images and five clinical and 12 laboratory indicators were obtained. Six classifiers were used to construct radiomics models. The gradient boosting decision tree (GBDT) classifier was used to screen for the best features to explore the main risk factors for differentiating early HT. The performance of each model was evaluated by receiver operating characteristic (ROC) curve. The model was validated using one internal and two external test cohorts. A total of 785 patients were enrolled. Extreme gradient boosting (XGBOOST) showed best performance in the training cohort, with an AUC of 0.999 (0.998, 1), and AUC values of 0.993 (0.98, 1), 0.947 (0.866, 1), and 0.98 (0.939, 1), respectively, in the internal test, first external, and second external cohorts. Ultrasound radiomic features contributed to 78.6% (11/14) of the model. The first-order feature of traverse section of thyroid ultrasound image, texture feature gray-level run length matrix (GLRLM) of longitudinal section of thyroid ultrasound image and free thyroxine showed the greatest contributions in the model. Our study developed and tested a risk-predictive model that effectively differentiated HT stages to more precisely and actively manage patients with HT at an earlier stage.

In silico analysis is key to understanding bone structure-function relationships in orthopedics and evolutionary biology, but its potential is limited by a lack of standardized, high-quality human bone morphology datasets. This absence hinders research reproducibility and the development of reliable computational models. To overcome this, BoneDat has been developed. It is a comprehensive database containing standardized bone morphology data from 278 clinical lumbopelvic CT scans (pelvis and lower spine). The dataset includes individuals aged 16 to 91, balanced by sex across ten age groups. BoneDat provides curated segmentation masks, normalized bone geometry (volumetric meshes), and reference morphology templates organized by sex and age. By offering standardized reference geometry and enabling shape normalization, BoneDat enhances the repeatability and credibility of computational models. It also allows for integrating other open datasets, supporting the training and benchmarking of deep learning models and accelerating their path to clinical use.

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a valuable tool for assessing IPN by evaluating the location and quantity of microbubbles within the carotid plaque. However, this task is typically performed by experienced radiologists. Here we propose a deep learning-based multi-task model for the automatic segmentation and IPN grade classification of carotid plaque on CEUS images and videos. We also compare the performance of our model with that of radiologists. To simulate the clinical practice of radiologists, who often use CEUS videos with dynamic imaging to track microbubble flow and identify IPN, we develop a workflow for plaque vulnerability assessment using CEUS videos. Our multi-task model outperformed individually trained segmentation and classification models, achieving superior performance in IPN grade classification based on CEUS images. Specifically, our model achieved a high segmentation Dice coefficient of 84.64% and a high classification accuracy of 81.67%. Moreover, our model surpassed the performance of junior and medium-level radiologists, providing more accurate IPN grading of carotid plaque on CEUS images. For CEUS videos, our model achieved a classification accuracy of 80.00% in IPN grading. Overall, our multi-task model demonstrates great performance in the automatic, accurate, objective, and efficient IPN grading in both CEUS images and videos. This work holds significant promise for enhancing the clinical diagnosis of plaque vulnerability associated with IPN in CEUS evaluations.

Breast cancer is the most prevalent malignancy in women and a leading cause of mortality. Accurate assessment of axillary lymph node metastasis (LNM) is critical for breast cancer management. Exploring non-invasive methods such as radiomics for the detection of LNM is highly important. We systematically searched Pubmed, Embase, Scopus, Web of Science and google scholar until 11 March 2024. To assess the risk of bias and quality of studies, we utilized the quality assessment of diagnostic accuracy studies (QUADAS) tool as well as the radiomics quality score (RQS). Area under the curve (AUC), sensitivity, specificity and accuracy were determined for each study to evaluate the diagnostic accuracy of radiomics in magnetic resonance imaging (MRI) for detecting LNM in patients with breast cancer. This meta-analysis of 20 studies (5072 patients) demonstrated an overall AUC of 0.83 (95% confidence interval (CI): 0.80-0.86). Subgroup analysis revealed a trend towards higher specificity when radiomics was combined with clinical factors (0.83) compared to radiomics alone (0.79). Sensitivity analysis confirmed the robustness of the findings and publication bias was not evident. The radiomics models increased the likelihood of a positive LNM outcome from 37% to 73.2% when initial probability was positive and decreased the likelihood to 8% when initial probability was negative, highlighting their potential clinical utility. Radiomics as a non-invasive method demonstrates strong potential for detecting LNM in breast cancer, offering clinical promise. However, further standardization and validation are needed in future studies.