Quantitative imaging decision support (QIDSTM) 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

doi:10.1177/1073274820985786

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

Cancer Control. 2021 Jan-Dec:28:1073274820985786. doi: 10.1177/1073274820985786.

Authors

Affiliations

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, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy.
⁴ Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy.
⁵ Division of Radiodiagnostic, 9257"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 (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.

Keywords: CHOI; RECIST; chest CT; pulmonary carcinoma; radiomic; segmentation.

Publication types

Multicenter Study
Observational Study

MeSH terms

Adult
Aged
Aged, 80 and over
Benchmarking
Carcinoma / diagnostic imaging*
Female
Humans
Lung Neoplasms / diagnostic imaging*
Male
Middle Aged
Radiographic Image Interpretation, Computer-Assisted / methods*
Radiography, Thoracic
Response Evaluation Criteria in Solid Tumors
Retrospective Studies
Software Validation*
Tomography, X-Ray Computed*
Young Adult