Kenneth Portal Neta, Rochman Shalom, Szeskin Adi, Lederman Richard, Sosna Jacob, Joskowicz Leo
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
J Thorac Imaging. 2025 Mar 1;40(2):e0808. doi: 10.1097/RTI.0000000000000808.
Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient. It is part of a fully automatic pipeline for the detection, segmentation, matching, and classification of metastatic lung lesions in longitudinal chest CT scans. A data set of 5040 metastatic lung lesions in 344 pairs of 208 prior and current chest CT scans from 79 patients was used for training/validation (173 scans, 65 patients) and testing (35 scans, 14 patients) of a standalone 3D U-Net models and 3 simultaneous SimU-Net models. Outcome measures were the lesion detection and segmentation precision, recall, Dice score, average symmetric surface distance (ASSD), lesion matching, and classification of lesion changes from computed versus manual ground-truth annotations by an expert radiologist.
SimU-Net achieved a mean lesion detection recall and precision of 0.93±0.13 and 0.79±0.24 and a mean lesion segmentation Dice and ASSD of 0.84±0.09 and 0.33±0.22 mm. These results outperformed the standalone 3D U-Net model by 9.4% in the recall, 2.4% in Dice, and 15.4% in ASSD, with a minor 3.6% decrease in precision. The SimU-Net pipeline achieved perfect precision and recall (1.0±0.0) for lesion matching and classification of lesion changes.
Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.
肿瘤患者的放射学随访需要检测肺部转移灶，并在纵向影像学研究中对其变化进行定量分析。我们的目的是评估SimU-Net，这是一种用于自动分析肺部转移灶及其在胸部CT扫描对中的时间变化的新型深度学习方法。
SimU-Net是一种同步多通道3D U-Net模型，在患者的已配准的先前扫描和当前扫描对上进行训练。它是用于纵向胸部CT扫描中肺部转移灶检测、分割、匹配和分类的全自动流程的一部分。使用来自79例患者的344对208次先前和当前胸部CT扫描中的5040个肺部转移灶数据集，对独立的3D U-Net模型和3个同步SimU-Net模型进行训练/验证（173次扫描，65例患者）和测试（35次扫描，14例患者）。结果指标包括病灶检测和分割的精度、召回率、Dice分数、平均对称表面距离（ASSD）、病灶匹配以及由放射科专家根据计算得出的与手动标注的真实情况进行的病灶变化分类。
SimU-Net的平均病灶检测召回率和精度分别为0.93±0.13和0.79±0.24，平均病灶分割Dice分数和ASSD分别为0.84±0.09和0.33±0.22毫米。这些结果在召回率上比独立的3D U-Net模型高出9.4%，在Dice分数上高出2.4%，在ASSD上高出15.4%，精度略有下降3.6%。SimU-Net流程在病灶匹配和病灶变化分类方面实现了完美的精度和召回率（1.0±0.0）。
与对每次扫描进行单独分析相比，使用SimU-Net对先前和当前胸部CT扫描中的肺部转移灶进行同步深度学习分析可产生更高的准确性。在放射学工作流程中实施SimU-Net可以通过自动计算用于评估肺部转移灶及其时间变化的关键指标来提高效率。
