OA06.07 Discrimination of Lung Invasive Adenocarcinoma with Micropapillary Pattern Based on CT Radiomics

Xu, Yaping; Ji, Wenbin; Hou, Liqiao; Zhou, Chao; Wang, W.; Zhou, Suna; Kong, Feng-Ming; Yang, Haihua

doi:10.1016/j.jtho.2019.08.440

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“…We have studied the diagnosis of micropapillary type of lung IAC by means of radiomics before (17), and this study is a supplement and extension on the basis of the previous research. In this study, we investigated radiomics features to differentiate lung IAC with MPP from lung IAC with non-MPP based on enhanced CT images.…”

Section: Discussionmentioning

confidence: 99%

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Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma

Hou

et al. 2021

Front. Oncol.

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ObjectiveWe aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.Methods170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics.ResultsUsing quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance.ConclusionThe use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

“…Previously, we investigated the diagnosis of micropapillary type of lung adenocarcinoma to some extent by radiomic methods (17) and the purpose of the current study is to further study the use of CT-based radiomics to preoperationally predict MPP in lung invasive adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma

Hou

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

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OA06.07 Discrimination of Lung Invasive Adenocarcinoma with Micropapillary Pattern Based on CT Radiomics

Cited by 1 publication

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Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma

Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma

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