Radiomics approach for preoperative identification of stages I - II and III - IV of esophageal cancer

Abstract

Objective: To predict preoperative staging using a radiomics approach based on computed tomography (CT) images of patients with esophageal squamous cell carcinoma (ESCC).

Methods: This retrospective study included 154 patients (primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator (Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages I-II and III-IV was evaluated and compared using the receiver operating characteristics (ROC) curves and net reclassification improvement (NRI).

Results: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging (P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve (AUC): 0.795vs. 0.694, P=0.003; NRI=0.424)] and validation cohorts (AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834).

Conclusions: The quantitative approach has the potential to identify stage I-II and III-IV ESCC before treatment.

Keywords: Esophageal cancer; diagnostic imaging; tumor staging; tumor volume.

1

Flowchart of radiomics features extraction. An example of imaging segmentation and features extraction in a poorly differentiated, middle thoracic and stage III ESCC patient. (A) Original CT imaging; (B) Region of interest (ROI) manual segmentation on slice (A); (C) Features extraction from ROI, quantifying tumor intensity, shape, texture and wavelet texture.

2

Radiomics feature selection using the least absolute shrinkage and selection operator (Lasso) logistic regression model. (A) Turning penalization parameter lambda (λ) using 5-fold cross-validation and minimum criterion in Lasso model. The area under the receiver operating characteristic (AUC) curve was plotted versus log (λ). Log (λ)=−3.006, with λ=0.049 was chosen; (B) Lasso coefficient profiles of the 218 radiomics features. The vertical gray line was drawn at the value selected using 5-fold cross-validation in (A), where the optimal λ yield 10 features with non-zero coefficients.

3

Bar charts of Rad-score for each patient in primary cohort (A) and validation cohorts (B). Red bars indicate the rad-score of stage I−II ESCC, while light green bars indicate the rad-score of stage III−IV ESCC. Blue dotted line shows the cut-off value (0.054) of rad-score; above the line indicates stage III−IV, below the line indicates stage I−II. Red bars above the blue dotted line or light green bars below the blue dotted line mean misclassification.

S1

Feature weight in radiomics signature.

S2

Receiver operating characteristic (ROC) curves in primary cohort (A) and validation cohort (B). The blue, red and green curves show the ROC related to volume, radiomics signature and combined model, respectively. The values of the area under the curve (AUC) and 95% confidence interval (95% CI) are presented at the bottom right corner of the figure.