AI Article Synopsis

  • The study aimed to evaluate the consistency of the QIDS tool and radiomic analysis in lung cancer by analyzing CT scan metrics from 150 patients.
  • Results showed strong correlation between QIDS and radiologists for measuring lesion size and density, with high inter-observer agreement, particularly in RECIST classification (80-84%).
  • Significant predictors of treatment response were identified from 594 radiomic metrics, including energy and histogram entropy, indicating the potential of the QIDS tool for reliable cancer assessment.

Article Abstract

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

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482708PMC
http://dx.doi.org/10.1177/1073274820985786DOI Listing

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