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Wenbin Ji
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OA06 - Refining Lung Cancer Screening (ID 131)
- Event: WCLC 2019
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Tomasz Grodzki, Lluis Esteban Tejero
- Coordinates: 9/09/2019, 11:00 - 12:30, Hilton Head (1978)
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OA06.07 - Discrimination of Lung Invasive Adenocarcinoma with Micropapillary Pattern Based on CT Radiomics (Now Available) (ID 399)
11:00 - 12:30 | Author(s): Wenbin Ji
- Abstract
- Presentation
Background
To develop and validate the radiomics nomogram on the discrimination of lung invasive adenocarcinoma (IAC) with micropapillary pattern from non-micropapillary pattern lesion and improve the diagnostic accuracy rate of lung invasive adenocarcinoma with micropapillary pattern before operations and provide guidance for follow-up treatments.
Method
Forty-one pathologically confirmed lung invasive adenocarcinomas with micropapillary pattern from January 2014 to December 2018 were included. Eighty-two pathologically confirmed lung invasive adenocarcinomas without micropapillary pattern from January 2018 to December 2018 were collected. Select 86 patients (70%) randomly from the 123 patients as the primary cohort, and the other 37 patients (30%) were set as an independent validation cohort. Least absolute shrinkage and selection operator (Lasso) was used for feature selection based on contrast enhancement CT images and then radiomics signature building. ROC analysis and AUC were used to value the ability to identify the lung invasive adenocarcinomas with micropapillary pattern.
Result
According to GrayLevelCooccurenceMatrix3, Intensity Histogram and Shape, nine hundred and eighty-five radiomics features were extracted by IBEX. And after data pre-processing such as eliminating missing items, strong correlation variables and multicollinear variables, the features were reduced to 40 features. Based on Mann-Whitney U Test, 28 features were figured out from the 40 features. Then Lasso was used to reduce the features to 3 features (10-1clusterprominenc, -333-4clusterprominence, 8-1contrast) as the most meaningful discriminators to build the radiomics signatures (Table 1). According to SPSS21.0 binary logistic regression analysis, ROC analysis and AUC show that the radiomics signature have effective discrimination performance of lung invasive adenocarcinoma with micropapillary pattern from non- micropapillary pattern lesion (AUC=0.766) and it reflects better in the independent validation cohort (AUC=0.807) (Figure 1).
Table 1 Three characteristic prediction parameters in radiomics label
prediction parameter P value U value W value AUC 10-1clusterprominence <0.005 765.000 4168.000 0.772 -333-4clusterpromise <0.005 790.000 4193.000 0.765 8-1contrast <0.005 919.000 4322.000 0.727
Conclusion
The radiomics signature established in this study have effective prediction of lung invasive adenocarcinoma with micropapillary pattern and non- micropapillary pattern lesion.
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