Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

Abstract

Background: Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.

Methods: We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73^High vs. CD73^Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.

Results: TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73^High vs rad-CD73^Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).

Conclusions: Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Keywords: Adenosine pathway; CD73; Cancer; Immune checkpoint; Interpretable machine learning; Radiomic biomarker.

Fig. 1

General workflow. Isotropic spatial resampling was applied on preoperative CT-scan images to segment 160 colorectal liver metastases (CRLM) resected in 122 colorectal cancer (CRC) patients who underwent partial hepatectomy. Matching CRLM, identified by their pathology report block numbers and anatomical description, were included in tissue microarrays for automated quantification of CD73 intra-tumoral expression by immunofluorescence. Radiomic features were extracted from the resulting three-dimensional regions of interest (ROI) (18 first-order statistics, 14 shape features and 75 textural features) and preprocessed. Subsequently, an Attentive Interpretable Tabular Learning (TabNet) model was trained to predict CD73 expression dichotomized as CD73^High vs. CD73^Low based on the surface area expressing CD73 over the total surface of assessable tissue. The model was then evaluated, and its predictions were interpreted using ROC curve and the Shapley Additive Explanations technique (SHAP). The association between radiomic CD73 (rad-CD73) and oncological survival outcomes was analyzed by Kaplan–Meier and logrank test

Fig. 2

Performance of TabNet on the hold-out test set. A Comparison of the ROC curves of TabNet with the other baseline models. TabNet outperformed all the trained baseline models as mirrored by the area under the ROC curves (AUC). B Confusion matrix of TabNet for CD73 classification. LR, Logistic Regression; RF, Random Forest; SVM, Support Vector Machine; TabNet, Attentive Interpretable Tabular Learning; XGBoost, Extreme Gradient Boosting

Fig. 3

Assessment of the predicted TabNet probabilistic score, rad-CD73. A Violin plot depicting the distribution of rad-CD73 score by CD73 expression level. A statistically significant difference was observed in the radiomic score between the CD73^High and CD73^Low groups (Wilcoxon signed rank test; P < 0.0001). B Spearman’s correlation between rad-CD73 and the actual CD73 expression (the percentage of CD73 positive area per metastasis)

Fig. 4

Decision curve analysis. The red line shows the treat none as CD73^High approach while the blue line shows the treat all as CD73^High approach. For threshold values greater than 0.08, rad-CD73 had a higher net benefit than both the “treat all” and “treat none” approaches

Fig. 5

Representative cases with high and low CD73 expression. A A preoperative CT image of a CD73^High CRLM (arrow) in the right hemiliver. B A preoperative CT image of a CD73^Low CRLM (arrow) in the left hemiliver. C, D Representative tissue microarray cores of the high C and low D CD73 expression of the respective CRLM. Hematoxylin and eosin staining (left) shows the integrity and general architecture of the tissue. Immunofluorescent staining (right) shows cell nucleus (blue, DAPI), CD73 expression (red), and the cancer cells with the pan-cytokeratins epithelial cell marker (green). Scale bars represent 100 µm

Fig. 6

TabNet global interpretability analysis using the Shapley Additive Explanations (SHAP) technique. A Summary plot listing radiomics features from top to bottom in a decreasing order of their impact on the model’s decision. The top four most significant features were texture-related features. B Variation of the SHAP value of four selected features with respect to the actual feature value. Homogeneous textures were associated with a higher CD73 expression, as mirrored by the effect of the top-ranking features

Fig. 7

Visualization of TabNet learnable mask from the third decision step. The dependence non uniformity normalized (DNUN) feature, designated as the top 1 feature in SHAP analysis, was selected by the learnable mask among the most salient features for almost all test set instances. Brighter colors indicate higher feature importance

Fig. 8

Prognostic value of rad-CD73 in colorectal liver metastases (CRLM). A Disease-specific survival according to rad-CD73 and B time-to-recurrence after the initial complete surgical resection of CRLM. In patients with more than one CRLM, the mean rad-CD73 was used to classify patients as low or high. The lower tertile was used as a cut-off value (rad-CD73 > 0.362)