Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability.
arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.
By Nils Neukirch, Martin Maurer, Nils Strodthoff
arXiv:2607. 12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift.
By Md. Sadibul Hasan Sadib, Md. Mohayminul Mukit, Rahmatul Kabir Rasel Sarker, Tahmid Alam Tamim, Md. Monir Hossain Shimul
arXiv:2608. 15004v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screening and followup assessment.
By Pramit Dutta, Jenita Manokaran, Richa Mittal, Ryan Appleby, Eranga Ukwatta
arXiv:2608. 03079v1 Announce Type: cross Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions.
By Ting Yin, Danning Li, Chen Shu, Xiaoxia Yao, Boyu Fu, Yujing Chang, Tianyu Shi, Mengna Feng, Jie Chen, Jing Fu, Xiuli Xiao, Tianlin Li, Mumin Shao, Jiaxin Bi, Wenchuan Zhang, Xiaoyan Wu, Xiao Han, Zhang Zhang, Yuhao Yi, Hong Bu
arXiv:2607. 03466v1 Announce Type: cross Abstract: This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026.
By Joseph Itopa Abubakar, Jorge Jarme, Favour Igwezeke, Mary Adewunmi
arXiv:2607. 25164v1 Announce Type: cross Abstract: A CT examination captures multiple organs, but many biomedical questions concern abnormalities, prognosis, or longitudinal change in a specific organ.
By Zhixuan Ge, Anqi Li, Sadeer Al-Kindi, Hanwen Xu, Wei Qiu
arXiv:2608. 05960v1 Announce Type: cross Abstract: Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume.
By Maulik Chevli, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Philip M\"uller
arXiv:2605. 25050v2 Announce Type: replace-cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets.
By Mohamed Boussena, Florence Monville, Jacques Fieschi-Meric, Frederic Vely, Pierre Milpied, Julien Mazieres, Maurice Perol, Eric Vivier, Laurent Greillier, Fabrice Barlesi, Sebastien Benzekry
Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings.
arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.
By Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev, Oleg I. Samovarov
arXiv:2607. 07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous.
By Alejandro Vergara-Richart (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain, Universitat Polit\`ecnica de Val\`encia, Val\`encia, Spain), Xavier Rafael-Palou (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain), Almudena Fuster-Matanzo (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain), Ignacio Iborra Roncales (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain), \'Angel Alberich-Bayarri (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain), Ana Jim\'enez-Pastor (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain)