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.
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
arXiv:2608.28716v1 Announce Type: cross Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impr...
The study introduces Spatial Atlas-Informed Loss Ensembles (SAILS) for whole-body PSMA PET/CT metastasis segmentation, integrating a population metastasis atlas into nnU-Net training via voxel-wise mean and standard deviation maps. SAILS improves lesion detection, notably in high-risk bone regions, achieving higher Dice scores and sensitivity on AutoPET~III and Deep-PSMA datasets compared to baseline methods, though it also increases false-positive volume. The approach eliminates the need for atlas registration at inference time.
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.
The paper presents a pragmatic segmentation pipeline for brain metastases in the BraTS 2026 Task 1, using a 5‑fold nnU-Net ResEnc‑L ensemble trained for 1,000 epochs on 1,296 four‑modality cases. A rule‑based post‑processing cascade tuned for the lesion‑wise Dice similarity coefficient (LW‑DSC) improves performance, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 on enhancing tumour, tumour core, whole tumour, and resection cavity, respectively. The authors audit each post‑processing stage with a five‑fold out‑of‑fold analysis, confirm two stages as robust, and provide a mechanistic analysis of LW‑DSC, along with thirteen negative results that challenge common intuitions.
A specialized large multimodal model, LLaVA‑NeXT, was fine‑tuned on a curated two‑level curriculum of PET/CT image‑conversation pairs to interpret head and neck cancer scans. In external validation across four institutions, the model achieved high ROUGE and similarity scores and outperformed generalist models such as ChatGPT, with primary tumor classification accuracy of 83.14% internally and 69.03% externally. The study demonstrates that domain‑specific LMMs can provide fast, accurate diagnostic support for PET/CT imaging.
arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.
arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
The paper introduces Pancreas‑MedNeXt, a unified MRI segmentation model that simultaneously delineates the entire pancreas, its subregions, pancreatic tumors, and surrounding organs at risk (OARs) from a single MRI volume. Using a shared MedNeXt encoder‑decoder and an annotation‑aware loss, the model is trained on partially labeled data and fine‑tuned for multitask output heads. In cross‑validation on 142 PANTHER patients and external test sets, Pancreas‑MedNeXt outperforms state‑of‑the‑art methods, achieving higher Dice similarity coefficients for pancreas, tumor, and OARs. "whyItMatters":"The model’s superior performance and unified approach could streamline contouring workflows for pancreatic cancer radiotherapy, potentially improving treatment planning efficiency."
arXiv:2609.15524v1 Announce Type: new Abstract: BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold c...
In clinical oncology studies, metastatic cancer is commonly evaluated using "Response Evaluation Criteria in Solid Tumors" (RECIST), in which the diameter of up to five lesions is measured and followed over the course of treatment. However, RECIST shows limited correlation with overall survival.
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...