2026· Academic Journal of Computing & Information Science· Vol 9· 0 citations· 5 references
TL;DR
This review summarizes key developments across radiomics, deep learning, pathomics, vision-language models, and liquid biopsy, highlighting the transition from single-modality analysis to multimodal integration.
Abstract
: Accurate characterization of pulmonary nodules is critical for early lung cancer diagnosis and treatment planning. Recent advances in artificial intelligence (AI) have demonstrated substantial potential in automating nodule detection, segmentation, malignancy classification, and invasiveness prediction. This review summarizes key developments across radiomics, deep learning, pathomics, vision-language models, and liquid biopsy, highlighting the transition from single-modality analysis to multimodal integration. Representative studies are discussed to illustrate the performance gains and remaining challenges in clinical translation, including generalizability, interpretability, and implementation feasibility.
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).
V. Ochs, C. Kuemmerli, Florentin Bieder et al.· 0 citations
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.
Mustafa Şahin, Nicholas Angelopoulos, R. Paparodis· Endocrine Connections· 0 citations
The workflow of radiomic feature extraction and model development is systematically described, emphasizing their roles in differentiating key lesions such as pheochromocytomas/paragangliomas, adrenal cortical adenomas, and adrenal cortical carcinomas.
Chaohui Liang, Hao Zhu· Frontiers in Endocrinology· 0 citations
This review summarizes the deep-learning architectures, fusion strategies, representative applications, and implementation challenges of mpMRI-centered multimodal AI.
M. Fujiwara, Soichiro Yoshida, Imon Banerjee et al.· Abdominal Radiology· 0 citations
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...
Sivakumar Rajendran· 2026 4th International Confe...· 0 citations
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 expertis...
Yiting Wang, Chao Cheng, Bing-Sheng Huang et al.· Japanese Journal of Radiolog...· 0 citations
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