A New Predictive Scoring System Based on Clinical Data and Computed Tomography Features for Diagnosing EGFR-mutated Lung Adenocarcinoma

Cao, Ye; Xu, Hui

doi:10.3747/co.25.3805

Cited by 7 publications

(9 citation statements)

References 33 publications

(37 reference statements)

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“… 40 , 41 Calcification in lung cancer may be primary and engulfed by the tumor, occur within areas of tumor necrosis, or manifest as calcium deposition following calcium secretion by the carcinoma itself. Cao et al 42 demonstrated that the sign of calcification in CT images showed no statistical significance between wild-type group and mutated group, which supported our results. However, another study found that calcification was an independent negative predictor of EGFR mutations and that, in adenocarcinoma, EGFR mutation is vital to the pathogenesis of nonmucinous but not mucinous BAC-type tumors, with the former leading to calcium deposition.…”

Section: Discussionsupporting

confidence: 90%

EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

Qin¹,

Gu²,

Lu³

et al. 2018

CMAR

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BackgroundEpidermal growth factor receptor (EGFR) mutation testing is restricted to several limitations. In this study, we examined the relationship between EGFR mutation status and clinicoradiological characteristics in a Chinese cohort of patients.Materials and methodsThe data of patients who were diagnosed with lung carcinoma and underwent both EGFR testing and chest computed tomography (CT) at our hospital between January 1, 2011, and November 31, 2015, were retrospectively analyzed. The age, sex, and smoking index of the patients, the size, margin, and density of the tumor, and the presence of specific signs visible on the CT images were assessed.ResultsThe results showed a higher rate of EGFR-tyrosine kinase inhibitor (TKI)-sensitive group than nonsensitive group in female patients and patients with a low smoking index (P<0.001, both). In logistic regression analyses, tumor size (P<0.001), smooth margins (P=0.015), and angular margins (P<0.001) were independent negative predictors of EGFR-TKI-sensitive group. Pleural indentation (P<0.001) and air bronchogram (P=0.025) were independent positive predictors of EGFR-TKI-sensitive group. Patients with squamous cell carcinoma had fewer sensitive mutations than those with either adenocarcinoma (P<0.001) or adenosquamous carcinoma (P<0.001).ConclusionClinical and CT characteristics differed significantly between EGFR-TKI-sensitive and nonsensitive groups. Our findings may be useful in deciding therapeutic strategies for patients in whom EGFR testing is not possible.

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Section: Discussionsupporting

confidence: 90%

EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

Qin¹,

Gu²,

Lu³

et al. 2018

CMAR

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“…This study had a small sample, including only 15 cases of EGFR(+) and 103 cases of EGFR(−). Persistent subsolid nodules are the most common manifestations of early lung adenocarcinoma, and some previous studies also showed that LADCs with EGFR mutations showed significantly higher frequencies of subsolid nodules than those without EGFR mutations. Therefore, we focused on the relationship between CT‐based radiomics features and EGFR mutations in LADCs with subsolid nodules in a relatively large cohort.…”

Section: Discussionmentioning

confidence: 94%

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule

et al. 2019

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Background Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR‐targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)‐based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. Materials and Methods A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT‐based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. Results In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT‐based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05). Conclusion As a noninvasive method, the CT‐based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. Implications for Practice Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR‐targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography‐based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible.

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“…A total of 34 retrospective diagnostic accuracy studies were included in the final analysis [9] , [12] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] . These articles assessed 10355 individuals; the sample size fluctuated between 64 and 864 patients per study.…”

Section: Resultsmentioning

confidence: 99%

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis

Ortiz

Camacho

Vásquez

et al. 2022

European Journal of Radiology Open

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A New Predictive Scoring System Based on Clinical Data and Computed Tomography Features for Diagnosing EGFR-mutated Lung Adenocarcinoma

Abstract: The mutation risk scoring system based on clinical data and ct features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.

Cited by 7 publications

References 33 publications

EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis

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