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The study developed a multimodal ultrasound and clinical data model to predict microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC). Using data from 489 patients across eight centers, the model combined B-mode ultrasound, color Doppler flow imaging, dynamic contrast-enhanced ultrasound, and clinical information, achieving an AUC of 0.8953 in external validation. Dynamic contrast-enhanced ultrasound contributed the most predictive power, while other modalities and clinical data added complementary value.
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...
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.
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
The study introduces SynerT, a waveform-only hybrid temporal model that uses a causal dilated TCN and dilated recurrent layers to predict early intraoperative acute kidney injury (AKI). Two extensions, SynerT-MM and SynerT-Stack, incorporate hemodynamic summaries, preoperative covariates, and a leakage-safe stacked ensemble to improve discrimination and calibration. Evaluated on the VitalDB database with a strict 60‑minute prediction window, SynerT-Stack achieved the best performance across AUROC, AUPRC, and F1‑max, and demonstrated the greatest net clinical benefit after recalibration.
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.