arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...
arXiv:2609.26463v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced computed tomography remains clinically challenging. R...
arXiv:2607. 20583v1 Announce Type: cross Abstract: The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely affected.
The study introduces RCC-Align, a cross‑modal contrastive learning framework that aligns paired histopathology whole‑slide images and CT scans to enhance noninvasive grading of clear cell renal cell carcinoma (ccRCC). Using patient‑level five‑fold cross‑validation on TCGA and CPTAC cohorts, RCC‑Align achieved an AUC of 0.601 and AUPRC of 0.599 for low‑ versus high‑grade ccRCC classification, outperforming CT‑only baselines and showing stronger WSI‑CT embedding alignment. The approach relies solely on CT at inference, potentially aiding grading when biopsy is unsafe or limited by tumor heterogeneity.
Clear cell renal cell carcinoma (CCRCC) grading is essential for treatment planning, yet existing approaches either analyze patch-level images directly or focus solely on nuclei-level classification,...
The paper introduces a semantic‑guided multimodal preprocessing technique that fuses nuclei classification maps with RGB histopathology images for Vision Transformer‑based grading of clear cell renal cell carcinoma. By concatenating classification map channels and applying multiplicative modulation, the method achieves a balanced accuracy of 0.916, markedly surpassing an RGB‑only baseline (0.707) and prior max‑voting approaches (0.427). Sensitivity analysis shows the 21‑percentage‑point improvement remains robust under simulated perturbations matching current nuclei classifier error rates, indicating effective use of imperfect nuclear‑level information.
arXiv:2607. 28423v1 Announce Type: cross Abstract: Radiomics and imaging foundation models promise non-invasive biomarkers of tumour biology, yet predictive signatures may reflect tumour volume or acquisition artifacts rather than meaningful image structure.
arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.
arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.
arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.
Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models.
arXiv:2607. 03593v1 Announce Type: cross Abstract: Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning.