The study presents a conditional diffusion-based generative model that augments T2-weighted prostate MRI data with clinically significant prostate cancer (csPCa) and transition zone (TZ) lesions. By extending MAISI‑v2 with csPCa class, zonal, and lesion‑mask conditioning and fine‑tuning on 1,303 patients, the model achieves a substantial reduction in Fréchet inception distance (FID) from 13.28 to 2.04. Synthetic augmentation improves csPCa classification AUC across five simulated training scenarios, though the benefit does not significantly transfer to a cross‑institutional dataset.
Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity. Materials and Methods: This retrospective study used PI-CAI (5-fold cross-validation) and Prostate158 (n=158; external).
arXiv:2609.13838v1 Announce Type: new Abstract: Purpose: To develop and evaluate TotalSynth, a reusable pretrained framework for whole-body synthetic CT (sCT) generation from MRI and cone-beam CT (CB...
arXiv:2606. 29977v1 Announce Type: cross Abstract: Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity.
The paper introduces a conditional 3D diffusion framework that synthesizes complete biparametric prostate MRI (bpMRI) from anatomical masks. It supports both sequential synthesis—generating T2-weighted and diffusion modalities with separate models—and joint synthesis—producing all bpMRI modalities simultaneously in a single model. Experiments on PI-CAI, FastMRI Prostate, and PROSTATEx datasets show that joint synthesis achieves competitive distributional fidelity, while directly synthesizing ADC and high‑b‑value DWI outperforms deriving them from synthetic diffusion-weighted images.
arXiv:2608. 16233v1 Announce Type: cross Abstract: Missing or degraded sequences can limit prostate multiparametric MRI.
arXiv:2610.33554v1 Announce Type: new Abstract: Automated prostate lesion detection from diffusion-weighted MR imaging (DWI) conventionally relies on reconstructed images and does not fully use all a...
arXiv:2606. 30951v1 Announce Type: cross Abstract: Micro-ultrasound ($\mu$US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability.
The paper introduces ANT, a test‑time adaptation framework that improves prostate cancer detection in micro‑ultrasound by performing a segmentation‑guided adaptation. ANT aligns a pretrained detection encoder to the target domain’s prostate anatomy using pseudo‑masks from a frozen segmentation network, thereby correcting domain‑specific feature drift while preserving cancer‑discriminative features. In a leave‑one‑center‑out evaluation, ANT raises mean AUC by 2.9% at the biopsy‑core level and 3.6% at the patient level compared to no adaptation, outperforming existing TTA baselines.
arXiv:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.
arXiv:2608.21497v1 Announce Type: cross Abstract: Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely us...
The study introduces a computationally efficient multi‑section whole slide image analysis framework that captures the entire prostate tumor landscape to predict biochemical recurrence (BCR) after radical prostatectomy. Using a large dataset of 23,451 slides from 789 patients, the authors optimized patch and slide subsampling to reduce computational demands while achieving strong predictive performance for 1‑ and 2‑year BCR, comparable to or better than existing clinical benchmarks. The AI‑derived risk score was an independent prognostic factor, improved prognostic performance when combined with clinical variables, and showed preliminary transferability in an external cohort, with attention‑dispersion analysis confirming that the model leveraged information from multiple sections consistent with prostate cancer’s multifocal nature.
arXiv:2609.26105v1 Announce Type: cross Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...