Habitat imaging radiomics increases the accuracy of a nomogram for predicting Ki-67-positivity in laryngeal squamous cell carcinoma.

Purpose: To investigate the value of applying habitat imaging (HI) radiomics on venous-phase computed tomography (CT) images from laryngeal squamous cell carcinoma (LSCC) patients, as part of a nomogram to predict Ki-67 positivity, an indicator of poorer LSCC prognoses. Methods: Clinical and CT imaging data from 128 LSCC patients, divided into training (89) and testing (39) groups, were analyzed. Conventional and HI radiomics features were extracted from enhanced venous phase images, either from the entire tumor (conventional) or 3 sub-regions (HI). Radiomics models were established, based on 5 machine learning algorithms, while clinical characteristics were analyzed by both uni- and multi-variate logistic regression analyses for their associations with Ki-67 positivity. Afterwards, a predictive nomogram was constructed by combining clinical characteristics, conventional radiomics, and HI radiomics. Results: The only clinical characteristic strongly predictive for Ki-67-positivity is the degree of differentiation (low/medium vs. high). Additionally, HI radiomics was significantly more accurate than conventional for predicting Ki-67-positivity. The most accurate model, though, was the predictive nomogram, with areas under the curve of 0.945 (training) and 0.871 (testing), which was significantly higher than for clinical characteristics, conventional radiomics and HI radiomics models alone; it also had the highest net benefit, and thus greatest clinical utility under decision curve analysis. Conclusions: HI radiomics features were more accurate for predicting Ki-67-positivity in LSCC than conventional radiomics. However, the combination of those features with conventional radiomics and the degree of differentiation in a predictive nomogram yields the most accurate model for Ki-67-positivity.