To preoperatively predict lymphovascular space invasion (LVSI) in early-stage cervical cancer (CC) using multi-parametric MRI (mpMRI) radiomics models. This dual-center study included 196 early-stage CC patients (Center A: 142, Dec2020-Apr2023; Center B: 54, May-Oct2023). Center A was partitioned into training (n = 99) and internal validation (n = 43) cohorts; Center B served as external validation. Radiomics features were extracted from T2WI, DWI, and CE-MRI sequences. Feature stability was assessed via intra-class correlation and Dice coefficient, with selection through linear correlation and F-tests. Seven radiomics models (single/combined sequences) were built using the top-performing algorithm among eleven machine learning methods. A combination model (CMIC) integrated the optimal mpMRI model's rad-score with clinical factors. Performance was evaluated by ROC, calibration curves, and DCA across all cohorts. The AdaBoost-based mpMRI model (CE-MRI+DWI+T2WI) utilized 12 selected features. It achieved AUCs of 0.953 (95% CI : 0.916-0.989) in training, 0.868 (0.755-0.981) in internal validation, and 0.797 (0.677-0.916) externally. The CMIC model showed comparable performance (training: 0.957; validation: 0.864; external: 0.847), with no significant differences versus the mpMRI model (p > 0.05 all cohorts). The AdaBoost-driven mpMRI radiomics model effectively predicts LVSI in early-stage CC. Both mpMRI and CMIC models demonstrate robust preoperative predictive capability. This mpMRI radiomics approach using AdaBoost outperforms single-sequence models for LVSI prediction, enabling personalized treatment strategies for early-stage CC.

Risk stratification is a core challenge in fetal growth restriction (FGR) care, in part because FGR does not represent a single diagnosis but instead is a finding that is associated with morbidity. Considerable effort has been invested in the development and study of methods to identify fetuses at risk of morbidity and who warrant intervention across multiple domains: Doppler ultrasound, maternal biomarkers, multivariable modeling, and artificial intelligence. It is likely that the most promising advances will integrate findings from across these domains, but further investigation remains necessary.

Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes. This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients. Retrospectively collected general clinical data, planning CT images, and dose files from 205 patients with advanced cervical cancer who underwent chemoradiotherapy, and classified them according to the severity of HT. Radiomics and dosiomics features were extracted from the same region of interest, and feature selection was performed using a random forest algorithm. Radiomics models, dosiomics models, and hybrid models were then constructed based on extreme gradient boosting trees. Sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the classification performance of the models. Finally, SHAP values were used to perform interpretability analysis on the best model to enhance the transparency and practicality of the model. The sensitivity, specificity, and AUC values for the radiomics model were 0.42, 0.86, and 0.78, respectively, while those for the dosiomics model were 0.50, 0.90, and 0.74. In contrast, the hybrid model exhibited superior classification performance with sensitivity, specificity, and AUC values of 0.50, 0.83, and 0.83, respectively. Compared to the standalone radiomics and dosiomics models, the hybrid model demonstrated enhanced classification capability. Interpretability analysis based on SHAP values not only provided a ranking of feature importance and the distribution of feature impacts on model outputs but also elucidated the specific decision-making processes influenced by these features and the interactions between them. This enables clinicians to gain a more intuitive understanding of the model's decisions. For patients with advanced cervical cancer undergoing chemoradiotherapy, the integration of radiomics and dosiomics features can significantly enhance the classification performance of predictive models, thereby holding considerable potential for refining patient treatment strategies. Interpretability analysis based on SHAP values can aid clinicians in more readily understanding the model's decisions, thus promoting the effective implementation of the model in clinical practice.

Fetal growth restriction (FGR), preeclampsia, and other placental disorders are leading contributors to perinatal morbidity and mortality, primarily due to impaired uteroplacental perfusion. Existing imaging modalities, such as Doppler ultrasound and fetal MRI, provide indirect or limited functional insights into placental and fetal perfusion, constraining timely clinical intervention. To evaluate contrast-enhanced ultrasound (CEUS) as a promising, safe, and real-time tool for assessing placental perfusion and its potential application in maternal-fetal medicine through comprehensive analysis of methodological parameters, safety profiles, and emerging computational techniques. A comprehensive synthesis of preclinical and clinical studies was conducted, focusing on the safety, efficacy, and current use of CEUS in pregnancy. Key findings were drawn from animal models (rats, sheep, macaques) and human studies involving 256 pregnant individuals, with detailed analysis of imaging protocols, contrast agent characteristics, and quantification methods. CEUS utilizes intravascular microbubble contrast agents (1-8 μm diameter) that do not cross the placental barrier, enabling safe maternal imaging. However, size distribution analysis reveals sub-micron populations (8-20% by number) requiring careful evaluation. Preclinical models confirm CEUS ability to detect placental perfusion Changes with 54% reduction in perfusion index following uterine artery ligation (p < 0.001). Human studies demonstrate zero clinically significant adverse events among 256 cases, though critical gaps exist including absent biomarker monitoring and long-term follow-up. Emerging AI-enhanced analysis achieves 73-86% diagnostic accuracy using ensemble deep learning architectures. Current limitations include significant protocol heterogeneity (MI 0.05-0.19, frequency 2-9 MHz) and absence of standardization. CEUS presents a compelling solution for perfusion imaging in pregnancy, offering functional, bedside imaging without fetal exposure to contrast agents. However, methodological limitations, knowledge gaps regarding long-term outcomes, and the distinction between conventional microbubbles and emerging nanobubble formulations demand systematic research investment. Clinical translation requires standardized protocols, comprehensive safety monitoring including biomarker assessment, ethical oversight, and long-term outcome studies to support integration into routine obstetric care.

Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we present a computational approach to detect and characterize metastatic SCLC (mSCLC) liver lesions and their associated NE-related phenotype as a method to improve patient management. This study utilized computed tomography scans of patients with hepatic lesions from two data sources for segmentation and classification of liver disease: (1) a public dataset from patients of various cancer types (segmentation; n = 131) and (2) an institutional cohort of patients with SCLC (segmentation and classification; n = 86). We developed deep learning segmentation algorithms and compared their performance for automatically detecting liver lesions, evaluating the results with and without the inclusion of the SCLC cohort. Following segmentation in the SCLC cohort, radiomic features were extracted from the detected lesions, and least absolute shrinkage and selection operator regression was utilized to select features from a training cohort (80/20 split). Subsequently, we trained radiomics-based machine learning classifiers to stratify patients based on their NE tumor profile, defined as expression levels of a preselected gene set derived from bulk RNA sequencing or circulating free DNA chromatin immunoprecipitation sequencing. Our liver lesion detection tool achieved lesion-based sensitivities of 66%-83% for the two datasets. In patients with mSCLC, the radiomics-based NE phenotype classifier distinguished patients as positive or negative for harboring NE-like liver metastasis phenotype with an area under the receiver operating characteristic curve of 0.73 and an F1 score of 0.88 in the testing cohort. We demonstrate the potential of utilizing artificial intelligence (AI)-based platforms as clinical decision support systems, which could help clinicians determine treatment options for patients with SCLC based on their associated molecular tumor profile. Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.

Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendor systems. We propose a label-efficient segmentation approach that promotes cross-modality generalization under real-world conditions, where GED4 hepatobiliary-phase annotations are limited, non-contrast sequences (T1WI, T2WI, DWI) are unlabeled, and spatial misalignment and missing phases are common. Our method integrates a foundation-scale 3D segmentation backbone adapted via fine-tuning, co-training with cross pseudo supervision to leverage unlabeled volumes, and a standardized preprocessing pipeline. Without requiring spatial registration, the model learns to generalize across MRI phases and vendors, demonstrating robust segmentation performance in both labeled and unlabeled domains. Our results exhibit the effectiveness of our proposed label-efficient baseline for liver segmentation in multi-phase, multi-vendor MRI and highlight the potential of combining foundation model adaptation with co-training for real-world clinical imaging tasks.

To compare the accuracy and reproducibility of lesion-diameter measurements performed by three state-of-the-art LLMs with those obtained by radiologists. In this retrospective study using a public database, 83 patients with solitary colorectal-cancer liver metastases were identified. From each CT series, a radiologist extracted the single axial slice showing the maximal tumor diameter and converted it to a 512 × 512-pixel PNG image (window level 50 HU, window width 400 HU) with pixel size encoded in the filename. Three LLMs-ChatGPT-o3 (OpenAI), Gemini 2.5 Pro (Google), and Claude 4 Opus (Anthropic)-were prompted to estimate the longest lesion diameter twice, ≥ 1 week apart. Two board-certified radiologists (12 years' experience each) independently measured the same single slice images and one radiologist repeated the measurements after ≥ 1 week. Agreement was assessed with intraclass correlation coefficients (ICC); 95% confidence intervals were obtained by bootstrap resampling (5 000 iterations). Radiologist inter-observer agreement was excellent (ICC = 0.95, 95% CI 0.86-0.99); intra-observer agreement was 0.98 (95% CI 0.94-0.99). Gemini achieved good model-to-radiologist agreement (ICC = 0.81, 95% CI 0.68-0.89) and intra-model reproducibility (ICC = 0.78, 95% CI 0.65-0.87). GPT-o3 showed moderate agreement (ICC = 0.52) and poor reproducibility (ICC = 0.25); Claude showed poor agreement (ICC = 0.07) and reproducibility (ICC = 0.47). LLMs do not yet match radiologists in measuring colorectal cancer liver metastasis; however, Gemini's good agreement and reproducibility highlight the rapid progress of image interpretation capability of LLMs.

Premature diagnosis of kidney cancer is crucial for saving lives and enabling better treatment. Medical experts utilize radiological images, such as CT, MRI, US, and histopathological analysis, to identify kidney tumors and cysts, providing valuable information on their size, shape, location, and metabolism, thus aiding in diagnosis. In radiological image processing, precise segmentation remains difficult when done manually, despite numerous noteworthy efforts and encouraging results in this field. Thus, there's an emergent need for automatic methods for renal and renal mass segmentation. In this regard, this article reviews studies on utilizing deep learning models to detect renal masses early in medical imaging examinations, particularly various CNN (Convolutional Neural Network) models that have demonstrated excellent outcomes in the segmentation of radiological images. Furthermore, we addressed the detailed dataset characteristics that the researchers adapted, as well as the accuracy and efficiency metrics obtained using various parameters. However, several studies employed datasets with limited images, whereas only a handful used hundreds of thousands of images. Those examinations did not fully determine the tumor and cyst diagnosis. The key goals are to describe recent accomplishments, examine the methodological approaches used by researchers, and recommend potential future research directions.

Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such as medical imaging, where labeled data is limited and expensive. We introduce LEAML, a label-efficient adaptation framework that leverages both scarce labeled VQA samples and abundant unlabeled images. Our approach generates domain-relevant pseudo question-answer pairs for unlabeled data using a QA generator regularized by caption distillation. Importantly, we selectively update only those neurons most relevant to question-answering, enabling the QA Generator to efficiently acquire domain-specific knowledge during distillation. Experiments on gastrointestinal endoscopy and sports VQA demonstrate that LEAML consistently outperforms standard fine-tuning under minimal supervision, highlighting the effectiveness of our proposed LEAML framework.

Polycystic Ovarian Disease (PCOD), also known as Polycystic Ovary Syndrome (PCOS), is a prevalent hormonal and metabolic condition primarily affecting women of reproductive age worldwide. It is typically marked by disrupted ovulation, an increase in circulating androgen hormones, and the presence of multiple small ovarian follicles, which collectively result in menstrual irregularities, infertility challenges, and associated metabolic disturbances. This study presents an automated diagnostic framework for PCOD detection from transvaginal ultrasound images, leveraging an Enhanced [Formula: see text] convolutional neural network architecture. The model incorporates attention mechanisms, batch normalization, and dropout regularization to improve feature learning and generalization. Bayesian Optimization was employed to fine-tune critical hyperparameters, including learning rate, batch size, and dropout rate, ensuring optimal model performance. The proposed system was trained and validated on a curated ovarian ultrasound image dataset, applying data augmentation and SMOTE techniques to address class imbalance. Experimental evaluation demonstrated that the Enhanced [Formula: see text] model achieved a classification accuracy of 94.8%, sensitivity of 93.2%, specificity of 95.5%, precision of 94.0%, and an F1-score of 93.6% on the independent test set. Interpretability was enhanced through Grad-CAM visualization, which effectively localized diagnostically significant regions within the ultrasound images, corroborating clinical findings. These results highlight the potential of the proposed deep learning-based framework to serve as a reliable, scalable, and interpretable decision-support tool for PCOD diagnosis, offering improved diagnostic consistency and reducing operator dependency in clinical workflows.