Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.
Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.
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DOI：
10.21037/jtd-23-133
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发表时间：
2023-07-31
影响因子：
2.5
通讯作者：
中科院分区：
文献类型：
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同时性双原发性肺腺癌（SDPLA）与肺癌肺内转移（IPM）的鉴别诊断具有重要的治疗和预后价值。本研究旨在开发和验证基于CT的放射组学模型，以区分SDPLA和IPM。回顾性研究了153例患者（93例SDPLA和60例IPM）的306个病理证实的病变。并记录CT形态学特征。感兴趣区域（ROI）半自动分割，每个分割病灶提取1,037个放射组学特征，放射组学特征的差异定义为CT上两个病灶放射组学特征的相对净差异。用组内相关系数（ICC）和Pearson相关系数排除低可靠性（ICC <0.75）和冗余性（r>0.9）特征。采用多元逻辑回归（LR）算法，根据所选特征建立分类模型。放射组学模型基于放射组学特征的四个最重要的差异。临床-CT模型和混合模型分别基于仅选定的临床和CT特征以及临床-CT和Rad-score的组合。在训练和测试队列中，放射组学模型的曲线下面积（AUC）大于临床CT模型的曲线下面积（训练和测试队列分别为0.944 vs. 0.793和0.886 vs. 0.735），并且发现测试集中两个模型之间存在统计学显著差异（P<0.001）。同时，三名放射科医师在测试集中区分19例SDPLA和13例IPM的敏感性分别为84.2%、63.9%和68.4%，特异性分别为76.9%、69.2%和76.9%。与三位放射科医生的表现相比，放射组学模型在训练和测试队列中对患者表现出更好的准确性。在这三种模式中，放射组学模式显示出最好的净效益。放射组学特征的差异显示了对同时性双原发肺腺癌与肺间转移的术前鉴别诊断的良好性能，上级优于临床模型和放射科医生的决策。
Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. A total of 153 patients (93 SDPLA and 60 IPM) with 306 pathologically confirmed lesions were retrospectively studied. CT morphological features were also recorded. Region of interest (ROI) segmentation was performed semiautomatically, and 1,037 radiomics features were extracted from every segmented lesion The differences of radiomics features were defined as the relative net difference in radiomics features between the two lesions on CT. Those low reliable (ICC <0.75) and redundant (r>0.9) features were excluded by intraclass correlation coefficients (ICC) and Pearson’s correlation. Multivariate logistic regression (LR) algorithm was used to establish the classification model according to the selected features. The radiomics model was based on the four most contributing differences of radiomics features. Clinical-CT model and MixModel were based on selected clinical and CT features only and the combination of clinical-CT and Rad-score, respectively. In both the training and testing cohorts, the area under the curves (AUCs) of the radiomics model were larger than those of the clinical-CT model (0.944 vs. 0.793 and 0.886 vs. 0.735 on training and testing cohorts, respectively), and statistically significant differences between the two models in the testing set were found (P<0.001). Meanwhile, three radiologists had sensitivities of 84.2%, 63.9%, and 68.4%, and specificities of 76.9%, 69.2%, and 76.9% in differentiating 19 SDPLA cases from 13 cases of IPM in the testing set. Compared with the performance of the three radiologists, the radiomics model showed better accuracy to the patients in both the training and testing cohorts. Among the three models, the radiomics model showed the best net benefits. The differences of radiomics features showed excellent diagnostic performance for preoperative differentiation between synchronous double primary lung adenocarcinoma from interpulmonary metastasis, superior to the clinical model and decisions made by radiologists.
影响因子：
2.5
作者：
Han X;Fan J;Liu T;Li N;Alwalid O;Gu J;Shi H
通讯作者：
Shi H