Emerging imaging technologies are reshaping the landscape of oncology, particularly in the management of liver cancer and neuroblastoma. Innovations such as diffusion kurtosis imaging and artificial intelligence (AI) are enhancing diagnostic precision and prognostic assessments, leading to more personalized treatment strategies.
Accurate diagnosis and prognostication in hepatocellular carcinoma and neuroblastoma remain challenging due to the limitations of conventional imaging in capturing the heterogeneity of tumor microstructure and behavior. Radiomics and AI-driven analysis promise to bridge this gap by extracting quantitative features beyond visual interpretation, offering deeper insights into tumor biology.
Simultaneous multislice diffusion kurtosis imaging (SMS-DKI) probes non-Gaussian water diffusion to delineate microstructural complexity in hepatocellular carcinoma. A recent study on preoperative prediction of early recurrence demonstrated that SMS-derived mean kurtosis, combined with tumor size and radiological characteristics, independently predicted recurrence within the first year, achieving an AUC of 0.94. This level of precision enables early identification of patients at high risk for relapse who may benefit from intensified postoperative surveillance or adjuvant therapies.
Beyond advanced diffusion metrics, deep learning–based platforms are streamlining liver tumor detection and longitudinal monitoring. In Cell Reports Medicine, an AI tool automates liver tumor detection, leveraging convolutional neural networks to segment lesions and quantify volumetric changes across serial CT scans. By reducing manual segmentation time and improving detection consistency, this approach supports more efficient tracking of treatment response and earlier recognition of progression.
In high-risk neuroblastoma, integrating circulating biomarkers with imaging features further refines prognostic assessment. A two-center investigation of integrating neuron-specific enolase and CT radiomics found that a composite model combining venous-phase radiomic signatures and NSE levels stratified patients into distinct survival groups with greater discrimination than either modality alone. This multimodal strategy lays the groundwork for risk-adapted therapy intensification based on individualized survival estimates.
The prognostic power of radiomic frameworks extends across tumor types. For instance, a CT-based radiomics nomogram has enhanced progression-free survival predictions in lung cancer, underscoring the versatility of these models to inform clinical decision-making beyond their initial application.
These advancements—from microstructural imaging with SMS-DKI to AI-driven detection and multimodal prognostic modeling—enable more granular risk stratification and personalized treatment planning. Prospective multicenter validation and seamless integration into clinical workflows will determine how rapidly these imaging innovations translate into routine practice and improved patient outcomes.