Quantitative imaging decision support (QIDSTM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan

Author:

Fusco Roberta1,Granata Vincenza1ORCID,Mazzei Maria Antonietta2,Di Meglio Nunzia2,Del Roscio Davide2,Moroni Chiara3,Monti Riccardo4,Cappabianca Carlotta4,Picone Carmine1,Neri Emanuele5,Coppola Francesca6,Montanino Agnese7,Grassi Roberta4,Petrillo Antonella1,Miele Vittorio3

Affiliation:

1. Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli”, Naples, Italy

2. Department of Radiological Sciences, Diagnostic Imaging Unit, “Azienda Ospedaliera Universitaria Senese,” Siena, Italy

3. Division of Radiodiagnostic, “Azienda Ospedaliero-Universitaria Careggi,” Firenze, Italy

4. Division of Radiodiagnostic, “Università degli Studi della Campania Luigi Vanvitelli,” Naples, Italy

5. Division of Radiodiagnostic, “Azienda Ospedaliera Universitaria Pisana,” Pisa, Italy

6. Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine (DIMES), “S. Orsola Hospital, University of Bologna,” Bologna, Italy

7. Thoracic Medical Oncology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli,” Naples, Italy

Abstract

Objective: To evaluate the consistency of the quantitative imaging decision support (QIDSTM) 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 QIDSTM 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. QIDSTM 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 QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM 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 QIDSTM were significant and robust predictors of RECIST response.

Publisher

SAGE Publications

Subject

Oncology,Hematology,General Medicine

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