A pairwise radiomics algorithm - lesion pair relation estimation (PRE) model for distinguishing multiple primary lung cancer (MPLC) from intrapulmonary metastasis (IPM)

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Ting-Fei Chen, Lei Yang, Hai-Bin Chen, Zhi-Guo Zhou, Zhen-Tian Wu, Hong-He Luo, Qiong Li, Ying Zhu
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引用次数: 0

Abstract

Abstract Background Distinguishing multiple primary lung cancer (MPLC) from intrapulmonary metastasis (IPM) is critical for their disparate treatment strategy and prognosis. This study aimed to establish a non-invasive model to make the differentiation pre-operatively. Methods We retrospectively studied 168 patients with multiple lung cancers (307 pairs of lesions) including 118 cases for modeling and internal validation, and 50 cases for independent external validation. Radiomic features on computed tomography (CT) were extracted to calculate the absolute deviation of paired lesions. Features were then selected by correlation coefficients and random forest classifier five-fold cross-validation, based on which the lesion pair relation estimation (PRE) model was developed. A major voting strategy was used to decide diagnosis for cases with multiple pairs of lesions. Cases from another institute were included as the external validation set for the PRE model to compete with two experienced clinicians. Results Seven radiomic features were selected for the PRE model construction. With major voting strategy, the mean area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the training vs. internal validation vs. external validation cohort to distinguish MPLC were 0.983 vs. 0.844 vs. 0.793, 0.942 vs. 0.846 vs. 0.760, 0.905 vs. 0.728 vs. 0.727, and 0.962 vs. 0.910 vs. 0.769, respectively. AUCs of the two clinicians were 0.619 and 0.580. Conclusions The CT radiomic feature-based lesion PRE model is potentially an accurate diagnostic tool for the differentiation of MPLC and IPM, which could help with clinical decision making.
两两放射组学算法-病变对关系估计(PRE)模型鉴别多发性原发性肺癌(MPLC)与肺内转移(IPM)
背景区分多发原发性肺癌(MPLC)和肺内转移(IPM)对于其不同的治疗策略和预后至关重要。本研究旨在建立无创模型进行术前鉴别。方法回顾性研究168例多发性肺癌患者(307对病变),其中118例进行建模和内部验证,50例进行独立外部验证。提取计算机断层扫描(CT)上的放射学特征来计算成对病变的绝对偏差。然后通过相关系数和随机森林分类器五重交叉验证选择特征,在此基础上建立病变对关系估计(PRE)模型。一个主要的投票策略被用来决定多对病变病例的诊断。来自另一个研究所的病例被纳入PRE模型的外部验证集,与两位经验丰富的临床医生竞争。结果选取7个放射学特征进行PRE模型构建。在主要投票策略下,训练组、内部验证组和外部验证组区分MPLC的平均受试者工作特征曲线下面积(AUC)、准确度、灵敏度和特异性分别为0.983、0.844、0.793、0.942、0.846、0.760、0.905、0.728、0.727、0.962、0.910、0.769。两名临床医生的auc分别为0.619和0.580。结论基于CT放射学特征的病变PRE模型可作为MPLC与IPM鉴别的准确诊断工具,有助于临床决策。
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来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
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