High-Intensity Focused Ultrasound (HIFU) ablation has emerged as a non-invasive treatment option for uterine fibroids that preserves fertility and offers faster recovery. Pre-intervention prediction of HIFU efficacy can augment clinical decision-making and patient management. This systematic review and meta-analysis aims to evaluate the performance of MRI-based radiomics machine learning (ML) models in predicting the efficacy of HIFU ablation in uterine fibroids. Studies were retrieved by conducting a thorough literature search across databases including PubMed, Scopus, Embase, and Web of Science, up to June 2025. The quality of the included studies was assessed using the QUADAS-2 and METRICS tools. A meta-analysis of the radiomics models was conducted to pool sensitivity, specificity, and AUC using a bivariate random-effects model. A total of 13 studies were incorporated in the systematic review and meta-analysis. Meta-analysis of 608 patients from 7 internal and 6 external validation cohorts showed pooled AUC, sensitivity, and specificity of 0.84, 77%, and 78%, respectively. QUADAS-2 was notable for significant methodological biases in the index test and flow and timing domains. Across all studies, the mean METRICS score was 76.93%-with a range of 54.9%-90.3%-denoting good overall quality and performance in most domains but with notable gaps in the open science domain. MRI-based radiomics models show promise in predicting the effectiveness of HIFU ablation for uterine fibroids. However, limitations such as limited geographic diversity, inconsistent reporting standards, and poor open science practices hinder broader application. Therefore, future research should focus on standardizing imaging protocols, using multi-center designs with external validation, and integrating diverse data sources.

Cancer is a leading, highly complex, and deadly disease that has become a major concern in modern medicine. Hepatocellular carcinoma is the most common primary liver cancer and a leading cause of global cancer mortality. Its development is predominantly associated with chronic liver diseases such as hepatitis B and C infections, cirrhosis, alcohol consumption, and non-alcoholic fatty liver disease. Molecular mechanisms underlying HCC involve genetic mutations, epigenetic changes, and disrupted signalling pathways, including Wnt/β-catenin and PI3K/AKT/mTOR. Early diagnosis remains challenging, as most cases are detected at advanced stages, limiting curative treatment options. Diagnostic advancements, including biomarkers like alpha-fetoprotein and cutting-edge imaging techniques such as CT, MRI, and ultrasound-based radiomics, have improved early detection. Treatment strategies depend on the disease stage, ranging from curative options like surgical resection and liver transplantation to palliative therapies, including transarterial chemoembolization, systemic therapies, and immunotherapy. Immune checkpoint inhibitors targeting PD-1/PD-L1 and CTLA-4 have shown promise for advanced HCC. In this review we discuss about emerging technologies, including artificial intelligence and multi-omics platforms for HCC management by enhancing diagnostic accuracy, identifying novel therapeutic targets, and enabling personalized treatments. Despite these advancements, the prognosis for HCC patients remains poor, underscoring the need for continued research into early detection, innovative therapies, and translational applications to effectively address this global health challenge.

Medical image segmentation is a critical task for the early detection and diagnosis of various conditions, such as skin cancer, polyps, thyroid nodules, and pancreatic tumors. Recently, deep learning architectures have achieved significant success in this field. However, they face a critical trade-off between local feature extraction and global context modeling. To address this limitation, we present DCM-Net, a dual-encoder architecture that integrates pretrained CNN layers with Visual State Space (VSS) blocks through a Cross-Branch Feature Fusion Module (CBFFM). A Decoder Feature Enhancement Module (DFEM) combines depth-wise separable convolutions with MLP-based semantic rectification to extract enhanced decoded features and improve the segmentation performance. Additionally, we present a new 2D pancreas and pancreatic tumor dataset (CCH-PCT-CT) collected from Chongqing University Cancer Hospital, comprising 3,547 annotated CT slices, which is used to validate the proposed model. The proposed DCM-Net architecture achieves competitive performance across all datasets investigated in this study. We develop a novel DCM-Net architecture that generates robust features for tumor and organ segmentation in medical images. DCM-Net significantly outperforms all baseline models in segmentation tasks, with higher Dice Similarity Coefficient (DSC) and mean Intersection over Union (mIoU) scores. Its robustness confirms strong potential for clinical use.

Prostate cancer (PCa) is one of the most common malignancies threatening men's health globally. A comprehensive and integrated approach is essential for its early screening, diagnosis, risk stratification, treatment guidance, and efficacy assessment. Radiomics, leveraging multi-parametric magnetic resonance imaging (mpMRI) and positron emission tomography/computed tomography (PET/CT), has demonstrated significant clinical value in the non-invasive diagnosis, aggressiveness assessment, and prognosis prediction of PCa, with substantial potential when combined with artificial intelligence. In genomics, mutations or deletions in genes such as TMPRSS2-ERG, PTEN, RB1, TP53, and DNA damage repair genes (e.g., BRCA1/2) are closely associated with disease development and progression, holding profound implications for diagnosis, treatment, and prognosis. Concurrently, biomarkers like prostate-specific antigen (PSA), novel urinary markers (e.g., PCA3), and circulating tumor cells (CTCs) are widely utilized in PCa research and management. Integrating these technologies into personalized treatment plans and the broader framework of precision medicine allows for an in-depth exploration of the relationship between specific biomarkers and disease pathogenesis. This review summarizes the current research on radiomics, genomics, and biomarkers in PCa, and discusses their future potential and applications in advancing individualized patient care.

To evaluate a commercial deep-learning based auto-contouring software specifically trained for high-dose-rate gynecological brachytherapy. We collected CT images from 30 patients treated with gynecological brachytherapy (19.5-28 Gy in 3-4 fractions) at our institution from January 2018 to December 2022. Clinical and artificial intelligence (AI) generated contours for bladder, bowel, rectum, and sigmoid were obtained. Five patients were randomly selected from the test set and manually re-contoured by 4 radiation oncologists. Contouring was repeated 2 weeks later using AI contours as the starting point ("AI-assisted" approach). Comparisons amongst clinical, AI, AI-assisted, and manual retrospective contours were made using various metrics, including Dice similarity coefficient (DSC) and unsigned D2cc difference. Between clinical and AI contours, DSC was 0.92, 0.79, 0.62, 0.66, for bladder, rectum, sigmoid, and bowel, respectively. Rectum and sigmoid had the lowest median unsigned D2cc difference of 0.20 and 0.21 Gy/fraction respectively between clinical and AI contours, while bowel had the largest median difference of 0.38 Gy/fraction. Agreement between fully automated AI and clinical contours was generally not different compared to agreement between AI-assisted and clinical contours. AI-assisted interobserver agreement was better than manual interobserver agreement for all organs and metrics. The median time to contour all organs for manual and AI-assisted approaches was 14.8 and 6.9 minutes/patient (p < 0.001), respectively. The agreement between AI or AI-assisted contours against the clinical contours was similar to manual interobserver agreement. Implementation of the AI-assisted contouring approach could enhance clinical workflow by decreasing both contouring time and interobserver variability.

Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for testis volume segmentation. This paper presents an evaluation of segmentation methods for testicular volume using Magnetic Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of 0.89, compared to median dice score of 0.85 for human interrater reliability on the same dataset, enabling large-scale annotation on a population scale for the first time. Our overall aim is to provide a trained model, comparative baseline methods, and annotated training data to enhance accessibility and reproducibility in testis MRI segmentation research.

The purpose of this study was to profile elemental composition of calcium-based urinary stones using laser-induced breakdown spectroscopy (LIBS) and develop a machine learning model to distinguish recurrence-associated profiles by integrating elemental and clinical data. A total of 122 calcium-based stones (41 calcium oxalate, 11 calcium phosphate, 49 calcium oxalate/calcium phosphate, 8 calcium oxalate/uric acid, 13 calcium phosphate/struvite) were analyzed via LIBS. Elemental intensity ratios (H/Ca, P/Ca, Mg/Ca, Sr/Ca, Na/Ca, K/Ca) were calculated using Ca (396.847 nm) as reference. Clinical variables (demographics, laboratory and imaging results, recurrence status) were retrospectively collected. A back propagation neural network (BPNN) model was trained using four data strategies: clinical-only, spectral principal components (PCs), combined PCs plus clinical, and merged raw spectral plus clinical data. The performance of these four models was evaluated. Sixteen stone samples from other medical centers were used as external validation sets. Mg and Sr were detected in most of stones. Significant correlations existed among P, Mg, Sr, and K ratios. Recurrent patients showed elevated elemental ratios (p < 0.01), higher urine pH (p < 0.01), and lower stone CT density (p = 0.044). The BPNN model with merged spectral plus clinical data achieved optimal performance in classification (test set accuracy: 94.37%), significantly outperforming clinical-only models (test set accuracy: 73.37%). The results of external validation indicate that the model has good generalization ability. LIBS reveals ubiquitous Mg and Sr in calcium-based stones and elevated elemental ratios in recurrent cases. Integration of elemental profiles with clinical data enables high-accuracy classification of recurrence-associated profiles, providing insights for potential risk stratification in urolithiasis management.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model. Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator. Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9-54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809-0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]). The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Timely and accurate diagnosis is crucial in addressing the global rise in thyroid cancer, ensuring effective treatment strategies and improved patient outcomes. We present an intelligent classification method that couples an Adaptive Convolutional Neural Network (CNN) with Cohen-Daubechies-Feauveau (CDF9/7) wavelets whose detail coefficients are modulated by an n-scroll chaotic system to enrich discriminative features. We evaluate on the public DDTI thyroid ultrasound dataset (n = 1,638 images; 819 malignant / 819 benign) using 5-fold cross-validation, where the proposed method attains 98.17% accuracy, 98.76% sensitivity, 97.58% specificity, 97.55% F1-score, and an AUC of 0.9912. A controlled ablation shows that adding chaotic modulation to CDF9/7 improves accuracy by +8.79 percentage points over a CDF9/7-only CNN (from 89.38% to 98.17%). To objectively position our approach, we trained state-of-the-art backbones on the same data and splits: EfficientNetV2-S (96.58% accuracy; AUC 0.987), Swin-T (96.41%; 0.986), ViT-B/16 (95.72%; 0.983), and ConvNeXt-T (96.94%; 0.987). Our method outperforms the best of these by +1.23 points in accuracy and +0.0042 in AUC, while remaining computationally efficient (28.7 ms per image; 1,125 MB peak VRAM). Robustness is further supported by cross-dataset testing on TCIA (accuracy 95.82%) and transfer to an ISIC skin-lesion subset (n = 28 unique images, augmented to 2,048; accuracy 97.31%). Explainability analyses (Grad-CAM, SHAP, LIME) highlight clinically relevant regions. Altogether, the wavelet-chaos-CNN pipeline delivers state-of-the-art thyroid ultrasound classification with strong generalization and practical runtime characteristics suitable for clinical integration.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries, with incidence rates rising globally. Early and accurate diagnosis is essential for improving outcomes. Transvaginal ultrasound (TVUS) remains a cost-effective first-line tool, and emerging techniques such as three-dimensional (3D) ultrasound (US), contrast-enhanced US (CEUS), elastography, and artificial intelligence (AI)-enhanced imaging may further improve diagnostic performance. To systematically review recent advances in US-based imaging techniques for the diagnosis and staging of EC, and to compare their performance with magnetic resonance imaging (MRI). A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was performed to identify studies published between January 2010 and March 2025. Eligible studies evaluated TVUS, 3D-US, CEUS, elastography, or AI-enhanced US in EC diagnosis and staging. Methodological quality was assessed using the QUADAS-2 tool. Sensitivity, specificity, and area under the curve (AUC) were extracted where available, with narrative synthesis due to heterogeneity. Forty-one studies met the inclusion criteria. TVUS demonstrated high sensitivity (76%-96%) but moderate specificity (61%-86%), while MRI achieved higher specificity (84%-95%) and superior staging accuracy. 3D-US yielded accuracy comparable to MRI in selected early-stage cases. CEUS and elastography enhanced tissue characterization, and AI-enhanced US achieved pooled AUCs up to 0.91 for risk prediction and lesion segmentation. Variability in performance was noted across modalities due to patient demographics, equipment differences, and operator experience. TVUS remains a highly sensitive initial screening tool, with MRI preferred for definitive staging. 3D-US, CEUS, elastography, and AI-enhanced techniques show promise as complementary or alternative approaches, particularly in low-resource settings. Standardization, multicenter validation, and integration of multi-modal imaging are needed to optimize diagnostic pathways for EC.