CT-based radiomics model to predict spread through air space in resectable lung cancer

Jialin Gong; Rui Yin; Leina Sun; Na Gao; Xiaofei Wang; Lianmin Zhang; Zhenfa Zhang

doi:10.1002/cam4.6496

CT-based radiomics model to predict spread through air space in resectable lung cancer

Cancer Med. 2023 Sep;12(18):18755-18766. doi: 10.1002/cam4.6496. Epub 2023 Sep 7.

Authors

Jialin Gong¹, Rui Yin², Leina Sun³, Na Gao³, Xiaofei Wang¹, Lianmin Zhang¹, Zhenfa Zhang¹

Affiliations

¹ Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
² School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China.
³ Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.

Abstract

Background: Spread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS.

Method: There were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation.

Results: The incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834.

Conclusion: CT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.

Keywords: contrast-enhanced CT; lung cancer; operation type; radiomics; spread through air space.

Abstract

Grants and funding