A graph-theoretic approach for the analysis of lesion changes and lesions detection review in longitudinal oncological imaging

IF 10.7 1区医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Medical image analysis Pub Date : 2024-07-14 DOI:10.1016/j.media.2024.103268

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引用次数: 0

Abstract

Radiological follow-up of oncology patients requires the detection of lesions and the quantitative analysis of lesion changes in longitudinal imaging studies of patients, which is time-consuming and requires expertise.

We present here a new method and workflow for the analysis and review of lesions and volumetric lesion changes in longitudinal scans of a patient. The generic graph-based method consists of lesion matching, classification of changes in individual lesions, and detection of patterns of lesion changes computed from the properties of the graph and its connected components. The workflow guides clinicians in the detection of missed lesions and wrongly identified lesions in manual and computed lesion annotations using the analysis of lesion changes. It serves as a heuristic method for the automatic revision of ground truth lesion annotations in longitudinal scans.

The methods were evaluated on longitudinal studies of patients with three or more examinations of metastatic lesions in the lung (19 patients, 83 CT scans, 1178 lesions), the liver (18 patients, 77 CECT scans, 800 lesions) and the brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions) with ground-truth lesion annotations. Lesion matching yielded a precision of 0.92–1.0 and recall of 0.91–0.99. The classification of changes in individual lesions yielded an accuracy of 0.87–0.97. The classification of patterns of lesion changes yielded an accuracy of 0.80–0.94. The lesion detection review workflow applied to manual and computed lesion annotations yielded 120 and 55 missed lesions and 20 and 164 wrongly identified lesions for all longitudinal studies of patients, respectively.

The automatic analysis of lesion changes and review of lesion detection in longitudinal studies of oncological patients helps detect missed lesions and wrongly identified lesions. This method may help improve the accuracy of radiological interpretation and the disease status evaluation.

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在纵向肿瘤成像中分析病变变化和病变检测回顾的图论方法

肿瘤患者的放射学随访需要在患者的纵向成像研究中检测病变并对病变变化进行定量分析，这既耗时又需要专业知识。我们在此介绍一种新方法和工作流程，用于分析和审查患者纵向扫描中的病变和病变体积变化。基于图形的通用方法包括病灶匹配、单个病灶变化分类以及根据图形及其连接组件的属性计算出的病灶变化模式检测。该工作流程利用病变变化分析，指导临床医生检测人工病变注释和计算病变注释中遗漏的病变和错误识别的病变。在对肺部（19 名患者，83 次 CT 扫描，1178 个病灶）、肝部（18 名患者，77 次 CECT 扫描，800 个病灶）和脑部（30 名患者，102 次 T1W-Gad MRI 扫描，317 个病灶）转移性病灶进行三次或三次以上检查并带有地面实况病灶注释的患者进行纵向研究时，对这些方法进行了评估。病灶匹配的精确度为 0.92-1.0，召回率为 0.91-0.99。对单个病灶变化的分类准确率为 0.87-0.97。病变变化模式分类的准确率为 0.80-0.94。病灶检测审查工作流程应用于手动和计算病灶注释，在所有患者的纵向研究中分别发现了 120 个和 55 个遗漏病灶以及 20 个和 164 个错误识别的病灶。这种方法有助于提高放射学解释和疾病状态评估的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical image analysis 工程技术-工程：生物医学

CiteScore

22.10

自引率

6.40%

发文量

309

审稿时长

6.6 months

期刊介绍： Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.