Artificial Intelligence in Pulmonary Nodule Assessment: From Radiomics to Multimodal Integration

Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

A fully automated multimodal deep learning framework that jointly analyzes 3D contrast enhanced CT and structured clinical information to classify patients into the three National Comprehensive Cancer Network resectability categories (upfront resectable, borderline resectable, locally advanced).

Artificial Intelligence in Thyroid Nodules and Cancer: Clinical Validation and Real-World Performance.

Thyroid nodules are detected in a large proportion of adults undergoing high-resolution ultrasonography, yet only a minority harbor clinically significant cancer. The clinical problem is therefore not only cancer detection but calibrated risk stratification: avoiding delayed diagnosis of aggressive disease while limiting unnecessary biopsies, molecular testing and diagnostic surgery. Artificial intelligence (AI) has moved rapidly from experimental image classification to clinically-deployed decision support. This invited review synthesizes current evidence for AI applications in the evaluation and management of thyroid nodules and differentiated thyroid cancer, emphasizing ultrasound-based computer-aided diagnosis, indeterminate cytology, molecular integration, cytopathology and histopathology, lymph-node assessment, report quality control, surveillance and emerging multimodal large language models. Commercial and near-commercial systems including S-Detect, AmCAD-UT, Koios DS Thyroid, AIBx and newer deep-learning systems show that AI can improve consistency, support less experienced readers and, in selected settings, reduce low-yield fine-needle aspiration without unacceptable loss of sensitivity. A particularly important future role may be AI-enabled de-escalation, in which image-derived estimates of benignity help support surveillance when clinical, sonographic, cytologic or molecular risk signals are concordantly low. However, performance varies by case mix, cancer prevalence, scanner platform, operator experience, geographic cohort, reference standard and whether the model is used as a stand-alone classifier or second reader. The strongest evidence supports AI as an adjunct to standardized ultrasound risk stratification and shared decision-making, not as a replacement for expert clinical judgment. Future progress will depend on prospective multicenter validation, transparent reporting, local calibration, workflow design, regulation, post-market surveillance and assessment of patient-centered outcomes.

AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers

Accurate evaluation of adrenal masses remains a significant challenge in endocrinology and radiology, as differential diagnosis involves a wide spectrum of benign and malignant lesions. Radiomics and deep learning (DL) have emerged as promising tools to enhance the precision of adrenal mass assessment by extracting high-dimensional imaging features and enabling automated, data-driven analysis. This review summarizes the latest advancements in the application of radiomics and DL techniques for adrenal mass evaluation. We systematically describe the workflow of radiomic feature extraction and model development, emphasizing their roles in differentiating key lesions such as pheochromocytomas/paragangliomas (PPGLs), adrenal cortical adenomas, and adrenal cortical carcinomas. Additionally, the utility of these approaches in genotype prediction and prognostic evaluation is highlighted. The review further explores the advantages and potential of DL, particularly convolutional neural networks (CNNs), in automated segmentation, feature learning, and end-to-end diagnostic frameworks. Finally, current challenges including technical limitations, clinical translation barriers, and future research directions are discussed, aiming to provide a theoretical foundation for constructing intelligent and precise adrenal mass evaluation systems.

Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.

Enhanced Colon Cancer Detection and Classification with Deep Learning Techniques: A Comparative Analysis

Colon cancer represents a growing universal issue related to health, with prompt and accurate detection essential for indispensable to improving outcomes of people's health. Standard approaches, like colonoscopy and histopathology, while useful, tend to be invasive, time-intensive and prone to human interpretative bias. Recent developments in deep learning (DL) have facilitated the creation of automated systems that improve the precision, speed and uniformity of colon cancer diagnosis and categorisation. This paper offers a thorough comparative examination of various advanced DL models, including ResNet, DenseNet and MobileNet, applied to multi-modal imaging datasets consisting of colonoscopy images. Quantitative findings indicate exceptional accuracy, precision and recall in diagnostic tasks, with MobileNet DL models outperforming in tumour diagnosis and grading than other peer groups.

Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.

The evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers are highlighted, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertise.