Quantitative imaging decision support (QIDS <sup>TM</sup> ) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan
Roberta Fusco, Vincenza Granata, Maria Antonietta Mazzei, Nunzia Di Meglio, Davide Del Roscio, Chiara Moroni, Riccardo Monti, Carlotta Cappabianca, Carmine Picone, Emanuele Neri, Francesca Coppola, Agnese Montanino, Roberta Grassi, Antonella Petrillo, Vittorio Miele
Abstract
Objective: To evaluate the consistency of the quantitative imaging decision support (QIDS TM ) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan. Materials and Methods: We included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDS TM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman’s correlation coefficient were performed. QIDS TM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change. Results: Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDS TM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDS TM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDS TM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared. Conclusion: In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDS TM were significant and robust predictors of RECIST response.