Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.

OBJECTIVES: To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction. METHODS: In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55). RESULTS: iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001). CONCLUSIONS: iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection. KEY POINTS: • Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

PURPOSE: To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. RESULTS: The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.