Monte Carlo–consistent dose prediction for clinical CyberKnife radiotherapy using a physics- and spatially-informed diffusion model
July 2026
Although Monte Carlo (MC) dose calculation is the gold standard for CyberKnife radiotherapy, its clinical integration is hindered by prohibitive computational latency arising from stochastic particle...
Application of artificial intelligence in cancer rehabilitation: A scoping review
July 2026
Cancer rehabilitation faces challenges, including resource limitations, workforce shortages, and a lack of personalized care. Artificial intelligence (AI) offers promising solutions with the potential...
Artificial intelligence in robot-assisted radical prostatectomy: From technical innovation to clinical translation
July 2026
Prostate cancer remains one of the most common malignancies in men, and robot-assisted radical prostatectomy (RARP) is widely used for localized and selected locally advanced disease. However, balancing...
Artificial intelligence in neuro-oncology imaging: Advancing brain tumor detection, grading, and treatment response evaluation
July 2026
This narrative review synthesizes how artificial intelligence (AI) is reshaping neuro-oncology imaging through automated detection, segmentation, grading, and longitudinal monitoring of brain tumors...
The role of artificial intelligence in cancer epidemiology: Challenges and opportunities
July 2026
Artificial intelligence (AI) is rapidly reshaping cancer research, but high technical performance alone is not sufficient for cancer epidemiology, which requires population representativeness, measurement...
Comprehensive guidelines for establishing sample diversity and data sufficiency in artificial intelligence diagnostic datasets for cervical liquid-based cytology
July 2026
Liquid-based cytology (LBC) has become a core technology in cervical cancer screening, and artificial intelligence (AI) shows great potential in addressing issues such as the global shortage of cytopathologists...
From cells to patients: Multiscale computational pathology in the era of foundation models and vision-language systems
July 2026
Computational pathology is fundamentally defined by its inherent hierarchical structure, spanning from nuclear morphology and cellular interactions to tissue microenvironments, ultimately integrating...
Deep learning-based segmentation of small-volume brain metastases in lung cancer patients
April 2026
Brain metastases from lung cancer typically present as multiple small lesions, creating considerable challenges for accurate segmentation. While existing datasets and models have primarily focused on...
AI-based diagnosis of clear-cell renal cell carcinoma based on non-contrast CT
April 2026
The accurate characterization of renal tumors, particularly clear-cell renal cell carcinoma (ccRCC), traditionally requires contrast-enhanced computed tomography (CECT), which is contraindicated in...
From morphology to function: Advances in multimodal imaging for pulmonary function prediction
April 2026
Pulmonary function test (PFT) is a vital noninvasive method for evaluating respiratory system function and is widely used in the diagnosis, surgical risk assessment, and prognostic prediction of chronic...
Advancing precision oncology through hNQO1-activatable NIR-II probes: Integrating molecular imaging with artificial intelligence
April 2026
Traditional imaging modalities often lack the molecular specificity and spatial resolution required for real-time tumor visualization, particularly in complex surgical settings. This narrative review...
A multiscale residual dense fusion network for nuclear medical image fusion
April 2026
Multimodal medical image fusion technology generates new images containing more accurate disease information by fusing different modal images. It not only improves the accuracy and efficiency of diagnosis...
Integrative machine learning-driven prioritization of ceRNA networks in adrenocortical carcinoma
April 2026
Adrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy in urgent need of robust biomarkers and novel therapeutic targets. In this study, a machine learning (ML)-driven framework...
Analysis of key technologies and intelligent development trends for tumor surgery navigation platforms
April 2026
With the rapid development of artificial intelligence (AI) and multimodal imaging technology, intelligent surgical navigation systems have become a research hotspot in the field of precision tumor treatment....
TSMIL: Transformer-based structured low-rank end-to-end multi-instance learning network for renal cell carcinoma classification in whole-slide images
April 2026
The pathological classification of renal cell carcinoma (RCC) is a critical indicator of its accurate diagnosis, treatment, and prognosis. Pathologists typically focus on a single subtype when determining...
Integrating multi-omic liquid biopsies and artificial intelligence: The next frontier in early cancer detection
January 2026
The integration of multi-omic liquid biopsies with artificial intelligence (AI) represents a rapidly evolving frontier in early cancer detection, offering the potential to enhance personalized medicine...
Predicting the effectiveness of neoadjuvant therapy in rectal cancer patients: Model construction based on radiomics and carcinoembryonic antigens
January 2026
This study aimed to develop a multimodal imaging histological model based on computed tomography (CT) images and carcinoembryonic antigen (CEA) values to predict the efficacy of preoperative neoadjuvant...
Multimodal medical imaging AI for breast cancer diagnosis: A comprehensive review
January 2026
Traditional artificial intelligence (AI)-based methods for breast cancer diagnosis often rely on a single modality, such as ultrasound images. With the rise of multimodal approaches, multiple data sources,...
Integrative multi-omics clustering for identifying novel breast cancer subtypes with distinct molecular and clinical characteristics
January 2026
As a heterogeneous disease, breast cancer requires refined classification frameworks that can effectively guide targeted therapies. However, traditional methods fail to capture the comprehensive molecular...
Harnessing computational power for intelligent oncology in the age of large models: Status, challenges, and prospects
January 2026
The integration of large-scale foundation models (e.g., GPT series and AlphaFold) into oncology is fundamentally transforming both research methodologies and clinical practices, driven by unprecedented...
Decision-making performance of large language models vs. human physicians in challenging lung cancer cases: A real-world case-based study
January 2026
Despite the promise shown by large language models (LLMs) for standardized tasks, their multidimensional performance in real-world oncology decision-making remains unevaluated. This study aims to introduce...