让医生参与AI肺癌影像分割，提升预测准确性和临床信任度。
Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework
- 医生协同AI分割，用VNet+半监督学习提高准确性与一致性。
- 模型在999例数据上达Dice=0.83，半监督比监督学习更优。
- 医生偏好AI初稿用于修改，而非完全替代，适合临床落地。
肺癌是癌症死亡主因，CT影像在筛查、预后和治疗中至关重要。人工分割耗时且变异大，深度学习虽可自动化但难被临床采纳。本研究基于知识到行动框架，构建医生协同的深度学习分割流程，以提升可重复性、预后准确性和临床信任。分析来自12个公开数据集的999例患者多中心CT数据，采用五种模型（3D Attention U-Net、ResUNet、VNet、ReconNet、SAM-Med3D），在全图和点击点裁剪图像上对比专家标注。通过497个PySERA提取的放射组学特征评估分割一致性，采用斯皮尔曼相关、ICC、威尔科克斯检验和多元方差分析；预后建模比较监督学习（SL）与半监督学习（SSL），涵盖38种降维策略和24种分类器。六名医生从七个维度定性评价分割掩膜，包括临床意义、边界质量、预后价值、信任度及工作流融合。VNet表现最佳（Dice=0.83，IoU=0.71），放射组学稳定性高（均值相关=0.76，ICC=0.65），SSL下预测准确率最高（准确率=0.88，F1=0.83）。SSL在所有模型中均优于SL。放射科医生偏好VNet在瘤周区域的表示和更平滑边界，更倾向于使用AI生成的初始掩膜进行人工修正而非直接替换。结果表明，结合VNet与SSL的方案可实现精准、可重复且临床可信的肺癌CT预后评估，为以医生为中心的AI转化提供可行路径。
原文摘要 · Abstract (English)
Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while deep learning (DL) offers automation but faces barriers to clinical adoption. Guided by the Knowledge-to-Action framework, this study develops a clinician-in-the-loop DL pipeline to enhance reproducibility, prognostic accuracy, and clinical trust. Multi-center CT data from 999 patients across 12 public datasets were analyzed using five DL models (3D Attention U-Net, ResUNet, VNet, ReconNet, SAM-Med3D), benchmarked against expert contours on whole and click-point cropped images. Segmentation reproducibility was assessed using 497 PySERA-extracted radiomic features via Spearman correlation, ICC, Wilcoxon tests, and MANOVA, while prognostic modeling compared supervised (SL) and semi-supervised learning (SSL) across 38 dimensionality reduction strategies and 24 classifiers. Six physicians qualitatively evaluated masks across seven domains, including clinical meaningfulness, boundary quality, prognostic value, trust, and workflow integration. VNet achieved the best performance (Dice = 0.83, IoU = 0.71), radiomic stability (mean correlation = 0.76, ICC = 0.65), and predictive accuracy under SSL (accuracy = 0.88, F1 = 0.83). SSL consistently outperformed SL across models. Radiologists favored VNet for peritumoral representation and smoother boundaries, preferring AI-generated initial masks for refinement rather than replacement. These results demonstrate that integrating VNet with SSL yields accurate, reproducible, and clinically trusted CT-based lung cancer prognosis, highlighting a feasible path toward physician-centered AI translation.
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