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...
By Mahdi Islam, Musarrat Tabassum
The study evaluates whether overlap metrics like Dice can reliably indicate metabolic tumor volume (MTV) estimation accuracy in PET segmentation models. Across five FDG PET/CT models, higher Dice scores correlated with lower relative MTV error on average, but at the patient level, 16.8% of cases fell on opposite sides of clinically relevant thresholds. Additionally, many models missed lesions, showing that model ranking alone does not guarantee reliable individual MTV estimates.
By Tushar Kataria, Gerald Sabin, Ponnuswamy Sadayappan, Shireen Y. Elhabian
SADL is a size‑adaptive decision layer that improves chronic stroke lesion segmentation on T1‑weighted MRI by combining an eleven‑member nnU‑Net ensemble with adaptive thresholding and pruning. The ensemble incorporates dice + top‑K loss, heavy augmentation, per‑lesion volume re‑weighting, stochastic weight averaging, and a cohort‑balanced sampling block with a residual‑encoder backbone. On a 91‑case holdout, SADL achieves Dice 0.7322, lesion‑F1 0.7461, and PR‑AUC 0.8430, with a statistically significant gain in lesion‑F1 (p=0.024) especially for small lesions.
By Marek Wodzinski, Gniewosz Drwiega, Wojciech Szymanski
The paper reports the submissions of team nucli to the HECKTOR 2026 challenge, which evaluates machine learning methods for head‑and‑neck cancer imaging. The team used self‑supervised pre‑trained encoders, training a 2D DINOv2 encoder on PET scans and combining a DynUNet with a SwinUNETR for segmentation, achieving a mean Dice of 0.5409. For TN staging, they fused geometric features from segmentation masks with PET‑DINOv2‑MIPs deep features via logistic regression, obtaining balanced accuracies of 0.3962 (T‑stage) and 0.6432 (N‑stage). For recurrence‑free survival prognosis, they fine‑tuned the PET‑DINOv2‑MIPs ViT‑S encoder with an MLP head under a CoxPH loss, achieving a Harrell's C‑index of 0.6577 and a weighted challenge score of 0.5802.
The paper introduces FORESEE-MS, a protocol for evaluating prospective spatial forecasting of future multiple sclerosis lesions using baseline FLAIR MRI. It compares a fold‑specific population‑prior atlas to five deep‑learning models (3D U‑Net, Attn+ResBlock+VAE, ViT, and two SwinUNETR variants) on voxel‑level discrimination (AUROC, AUPRC) and within‑patient lesion localization (Recall@Top‑K%). While the atlas achieved the best pooled AUROC and AUPRC, the 3D U‑Net and Attn+ResBlock+VAE showed higher within‑patient recall, though confidence intervals largely overlapped zero, leaving the benefit of patient‑specific predictive information uncertain in this small cohort.
By Maryam Akhavan Aghdam, Sudip Mittal, Noorbakhsh Amiri Golilarz, Shahram Rahimi
The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.
By Haobin Liu, Xin Wang