arXiv:2606. 30313v1 Announce Type: cross Abstract: Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO.
CFB-GBM v2.0 is an expanded longitudinal dataset of 264 glioblastoma patients, providing complete Gross Tumour Volume (GTV) delineations across all timepoints and derived volumetric RANO 2.0 response labels. The dataset includes brain masks, pre‑computed radiomic features, and WHO classification guidelines, all validated by radiation oncologists. It is publicly available on TCIA for use in computational methods for treatment response prediction and disease progression modeling.
arXiv:2603. 05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines.
The paper introduces Observation‑Anchored Selective Assimilation (OASA) for forecasting tumor‑state proxies in post‑treatment glioma patients using serial MRI observations. OASA anchors the patient‑specific state with an intermediate observation and selectively updates it via a tiered rule and voxel‑wise soft gate, outperforming baseline methods in Dice score at certain thresholds. The approach is validated on 120 patient triplets and the code is publicly released.
arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.
arXiv:2605.28397v2 Announce Type: replace Abstract: Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied...
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
Brain-PACE is a deep Siamese MRI framework that directly estimates the pace of structural brain ageing from paired T1‑weighted MRI scans, extending the LILAC model with spatial attention, soft label distribution learning, and a Cramér distance objective. In a study of participants with mild cognitive impairment, 42.6 % showed accelerated ageing, and faster Brain‑PACE scores correlated with greater functional and cognitive impairment as well as higher regional tau burden in key brain regions. The method improves probabilistic performance, reduces prediction bias, and provides predictive uncertainty, offering a complementary longitudinal imaging phenotype sensitive to early neurodegeneration.
arXiv:2609.24769v1 Announce Type: new Abstract: Brain metastases are the most common intracranial malignancy, occurring in roughly 30% of patients with primary solid tumors and carrying a median surv...
arXiv:2607. 13877v1 Announce Type: new Abstract: Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term prediction challenging.
arXiv:2606. 24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is.