Oncology/Hematology

Cells Keep Diverse Company in Diseased Tissues.

Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribution of cellular diversity remains underexplored. In this issue, Ding and colleagues introduce multiomics and ecological spatial analysis (MESA), an ecology-inspired framework that integrates spatial and single-cell expression data to quantify tissue ...

Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality.

The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques provide standardized assistance to clinicians, in which many diagnostic and predictive tasks are manually conducted, causing low reproducibility. These A...

Leveraging Whole-Exome Sequencing and Mutational Signatures to Detect Homologous Recombination Deficiency in Cancer.

Homologous recombination is a high-fidelity DNA repair mechanism essential for maintaining genome stability. Impairment of this pathway, often due to ...

Paving a Path to Clinical Impact with Radiomics: Enabling Reproducibility and Reach.

Radiomics, the extraction of quantitative data from images, holds promise for noninvasively characterizing tumor phenotypes. Tools like LIFEx have imp...

Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer.

The rapid advancement of artificial intelligence (AI) technologies has opened new avenues for advancing personalized immunotherapy in cancer treatment...

Advancing Lung Disease Diagnosis in 3D CT Scans

To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the chara...

Zero-shot segmentation of spinal vertebrae with metastatic lesions: an analysis of Meta's Segment Anything Model 2 and factors affecting learning free segmentation.

OBJECTIVE: Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metasta...

Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic Modelling.

BACKGROUND: The global increase in thyroid cancer prevalence, particularly among female populations, underscores critical gaps in our understanding of...

Correction to: Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

Radiomics analysis of intraoral ultrasonographic images for prediction of late cervical lymph node metastasis in patients with tongue cancer: influence of marginal region.

OBJECTIVES: To investigate the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue can...

Radiation and contrast dose reduction in coronary CT angiography for slender patients with 70 kV tube voltage and deep learning image reconstruction.

OBJECTIVE: To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction (DLIR) in corona...

Quantitative CT biomarkers for renal cell carcinoma subtype differentiation: a comparison of DECT, PCT, and CT texture analysis.

OBJECTIVE: To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distingu...

Differentiating benign and hepatocellular carcinoma cirrhotic nodules: radiomics analysis of water restriction patterns with diffusion MRI.

OBJECTIVES: Current study aimed to investigate radiomics features derived from 2-centre diffusion-MRI to differentiate benign and hepatocellular carci...

[Radiotherapy of non-Hodgkin lymphoma-discussion of modern treatment concepts and innovations].

BACKGROUND: Radiotherapy is an established treatment modality for malignant non-Hodgkin lymphoma. However, the complexity of the treatment situations ...

Acquisition and Reconstruction Techniques for Coronary CT Angiography: Current Status and Trends over the Past Decade.

Coronary CT angiography (CCTA) has been widely used as a noninvasive modality for accurate assessment of coronary artery disease (CAD) in clinical set...

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, iden...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communi...

[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].

To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（N...

Accurate Diagnosis of Colorectal Cancer Using a Combination of Lectin-Induced Recombinase Polymerase Amplification and CRISPR/Cas12a Assay on a Point-of-Care Testing Platform with Deep Learning Assistant.

Specific glycosylation patterns on exosome surfaces represent novel diagnostic biomarkers for cancer liquid biopsy. Lectins can induce exosome aggrega...

The Interplay of Cross-Organ Immune Regulation in Inflammation and Cancer.

Organs dynamically interact with each other through immunomodulation to create a systemic immune response and influence disease progression. While tra...