AI and Cancer Research ── Cancer biology ── 2026-10-19
Cancer molecular biology and AI
- Abstract
Radiomics extracts quantitative imaging features from medical images to develop risk prediction models that support clinical decision-making. Owing to the high dimensionality and potential instability of radiomic features, accurate survival prediction depends critically on the selection of appropriate feature engineering and survival modelling methods. However, comprehensive benchmark studies comparing these methods remain limited, particularly for recently developed deep learning models. Here, we systematically benchmarked 16 survival prediction methods and 12 feature selection/extraction methods using three groups of publicly available radiomics datasets from non-small cell lung cancer and ovarian cancer cohorts, comprising 1160 patients and representing different levels of feature stability. Model performance was evaluated using repeated random training/testing splits and independent external validation with the C-index, integrated area under the curve (IAUC), and integrated Brier score (IBS). Ridge-Cox, glmboost, CoxBoost, and sslasso were consistently ranked among the top-performing methods according to the C-index and IAUC, whereas deep learning models, including CoxTime, DeepHit, and PCHazard, performed better when evaluated by IBS, highlighting that different evaluation metrics capture complementary aspects of survival prediction performance. Among feature engineering methods, supervised principal component analysis achieved the best overall performance, particularly for datasets with unstable radiomic features, whereas the Cox proportional hazards p -value-based method may be preferred when discrimination performance, as measured by the C-index or IAUC, is the primary objective. Furthermore, we investigated factors affecting the performance of deep learning models and assessed the influence of the proportional hazards and linear assumptions on survival prediction. These benchmark results provide practical guidance for selecting feature engineering and survival modelling strategies according to the characteristics of radiomics datasets, thereby facilitating the development of more robust radiomics-based survival prediction models.
Journal IF-equivalent: 9.5 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Liu W, Wang W. A comparative study of survival prediction and feature selection/extraction methods for quantitative radiomics risk modelling. Heliyon. 2026 Nov;12(16):e45602. doi:10.1016/j.heliyon.2026.e45602.Checked: Abstract only - Abstract
Brain diseases, such as Alzheimer's disease and brain tumors, present profound challenges due to their complexity and societal impact. Recent advancements in brain foundation models have shown significant promise in addressing a range of brain-related tasks. However, current brain foundation models are limited by task and data homogeneity, restricted generalization beyond segmentation or classification, and inefficient adaptation to diverse clinical tasks. In this work, we propose SAM-Brain3D, a brain-specific foundation model trained on over 66,000 brain image-label pairs across 14 MRI sub-modalities, and Hypergraph Dynamic Adapter (HyDA), a lightweight adapter for efficient and effective downstream adaptation. SAM-Brain3D captures detailed brain-specific anatomical and modality priors for segmenting diverse brain targets and broader downstream tasks. HyDA leverages hypergraphs to fuse complementary multi-modal data and dynamically generate patient-specific convolutional kernels for multi-scale feature fusion and personalized patient-wise adaptation. Together, our framework excels across a broad spectrum of brain disease segmentation and classification tasks. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art approaches, offering a new paradigm for brain disease analysis through multi-modal, multi-scale, and dynamic foundation modeling.
Journal IF-equivalent: 7.0 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Deng Z, Wang H, Huang Z, Zhang L, Aviles-Rivero AI, Liu C, et al. Brain foundation models with hypergraph dynamic adapter for brain disease analysis. Pattern Recognit. 2026 Apr;172:112595. doi:10.1016/j.patcog.2025.112595; PMCID: PMC7619582.Checked: Full text checked