Problem
Research questions and friction points this paper is trying to address.
radiological Peritoneal Cancer Index
inter-observer variability
model variability
clinical decision-making
Evaluating the Effects of Inter-Observer and Model Variability on Radiological Peritoneal Cancer Index Assessment
🤖 AI Summary
研究通过对比专家和模型在rPCI评分上的差异，评估了深度学习分割模型的临床决策影响，发现rPCI评分对分割变异性具有鲁棒性，但在临界值附近仍需专家审查。
📝 Abstract
Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent improvements in these metrics translate into clinically meaningful changes in downstream decision-making. The metric-to-decision gap is examined using radiological Peritoneal Cancer Index (rPCI) region segmentation on contrast-enhanced CT, where a consensus definition provides anatomically grounded 3D regions and the clinically used PCI 20 threshold enables decision-level evaluation. Inter-observer variability is quantified across four experts on ten abdominal CT scans, and a published nnU-Net based rPCI segmentation model is benchmarked against this human reference using Dice, HD95, and ASD across all 13 regions. To relate geometric differences to clinical impact, a probabilistic peritoneal metastasis simulation is implemented on majority-vote rPCI maps, propagating region-boundary variability into variability of derived (r)PCI scores and classification at the PCI 20 cutoff. Observers showed high agreement (mean Dice $0.87$), while the model matched human performance in most regions but deviated more in regions 4, 8, and the small-bowel regions (9-12). Across simulations, score differences were typically small (mean $Δ$rPCI $\approx 0.3$-$0.6$) for both observers and the model, and decision flips occurred predominantly when the reference score was near 20. These results suggest that rPCI-derived scoring is generally robust to typical segmentation variability, while highlighting borderline cases as the main setting where expert review remains essential.
Innovation
Methods, ideas, or system contributions that make the work stand out.
probabilistic peritoneal metastasis simulation
metric-to-decision gap
inter-observer variability
rPCI segmentation
🔎 Similar Papers
No similar papers found.
