Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma

Abstract

Background: The aim of this study was to evaluate the prognostic value of radiomics signature and nomogram based on contrast-enhanced computed tomography (CT) in patients after surgical resection of laryngeal squamous cell carcinoma (LSCC). Methods: All patients (n = 136) were divided into the training cohort (n = 96) and validation cohort (n = 40). The LASSO regression method was performed to construct radiomics signature from CT texture features. Then a radiomics nomogram incorporating the radiomics signature and clinicopathologic factors was established to predict overall survival (OS). The validation of nomogram was evaluated by calibration curve, concordance index (C-index) and decision curve. Results: Based on three selected texture features, the radiomics signature showed high C-indexes of 0.782 (95%CI: 0.656-0.909) and 0.752 (95%CI, 0.614-0.891) in the two cohorts. The radiomics nomogram had significantly better discrimination capability than cancer staging in the training cohort (C-index, 0.817 vs. 0.682; P = 0.009) and validation cohort (C-index, 0.913 vs. 0.699; P = 0.019), as well as a good agreement between predicted and actual survival in calibration curves. Decision curve analysis also suggested improved clinical utility of radiomics nomogram. Conclusions: Radiomics signature and nomogram showed favorable prediction accuracy for OS, which might facilitate the individualized risk stratification and clinical decision-making in LSCC patients.

Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging

“…Radiomic signatures have demonstrated promise for predicting tumor characteristics associated with OS in multiple cohorts of patients with HNSCC. 10,[35][36][37][38][39][40][41] Chen et al 36 Most constructed models have performed better in the training cohort than the validation cohort, suggesting that overfitting may be occurring. Furthermore, most studies have used data sets from a single institution, which may poorly represent the larger patient population.…”

“…35 The 7 studies predicting OS had good accuracies, with most models achieving an AUC or c-index above 0.7. 10,[36][37][38][39][40] Most models achieved the highest AUC when evaluating on a combination model, encompassing both clinical and radiomic features. Second-order texture features are most commonly correlated with outcomes.…”

“…Second-order texture features are most commonly correlated with outcomes. 10,36,[38][39][40] However, the survival studies presented are exploratory, with models built on small retrospective cohorts of patients and limited validation. Construction and validation of models remains challenging due to small sample sizes and wide heterogeneity within head and neck subsites.…”

Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass

“…Each transverse slice consisted of cuts made along the primary tumor contour. A total of fifty-two quantified texture features were extracted, including features from histogram-based matrix and shape-based matrix from the first order and features from gray-level co-occurrence matrix (GLCM), gray-level zone length matrix (GLZLM), neighborhood gray-level dependence matrix (NGLDM), and gray-level run length matrix (GLRLM) from second or higher order (20). A detailed description of all these characteristics can be found in https://www.lifexsoft.org/index.…”

CT-based radiomics features in the prediction of thyroid cartilage invasion from laryngeal and hypopharyngeal squamous cell carcinoma

“…It can be used to analyze the heterogeneity of an entire tumor based on hundreds of quantitative features and also analyze the relationship between the biological and imaging characteristics of the tumor quantitatively [15][16][17]. It is widely used in research on tumor diagnosis, prognosis, and the prediction of treatment response [17][18][19][20][21]. To the best of our knowledge, there is no study in the literature that has evaluated the application of CT radiomics for the prediction of thyroid cartilage invasion of LHSCC.…”