Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan
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 ch...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-02-01
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| Series: | Cancer Control |
| Online Access: | https://doi.org/10.1177/1073274820985786 |
| _version_ | 1829533285729959936 |
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| author | Roberta Fusco PhD Vincenza Granata MD Maria Antonietta Mazzei MD Nunzia Di Meglio MD Davide Del Roscio MD Chiara Moroni MD Riccardo Monti MD Carlotta Cappabianca MD Carmine Picone MD Emanuele Neri MD Francesca Coppola MD Agnese Montanino MD Roberta Grassi MD Antonella Petrillo MD Vittorio Miele MD |
| author_facet | Roberta Fusco PhD Vincenza Granata MD Maria Antonietta Mazzei MD Nunzia Di Meglio MD Davide Del Roscio MD Chiara Moroni MD Riccardo Monti MD Carlotta Cappabianca MD Carmine Picone MD Emanuele Neri MD Francesca Coppola MD Agnese Montanino MD Roberta Grassi MD Antonella Petrillo MD Vittorio Miele MD |
| author_sort | Roberta Fusco PhD |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T18:53:13Z |
| format | Article |
| id | doaj.art-84eaeeeca83943ac9aeff2d69e92adf6 |
| institution | Directory Open Access Journal |
| issn | 1073-2748 |
| language | English |
| last_indexed | 2024-12-16T18:53:13Z |
| publishDate | 2021-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Control |
| spelling | doaj.art-84eaeeeca83943ac9aeff2d69e92adf62022-12-21T22:20:36ZengSAGE PublishingCancer Control1073-27482021-02-012810.1177/1073274820985786Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scanRoberta Fusco PhD0Vincenza Granata MD1Maria Antonietta Mazzei MD2Nunzia Di Meglio MD3Davide Del Roscio MD4Chiara Moroni MD5Riccardo Monti MD6Carlotta Cappabianca MD7Carmine Picone MD8Emanuele Neri MD9Francesca Coppola MD10Agnese Montanino MD11Roberta Grassi MD12Antonella Petrillo MD13Vittorio Miele MD14 Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli”, Naples, Italy Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli”, Naples, Italy Department of Radiological Sciences, Diagnostic Imaging Unit, “Azienda Ospedaliera Universitaria Senese,” Siena, Italy Department of Radiological Sciences, Diagnostic Imaging Unit, “Azienda Ospedaliera Universitaria Senese,” Siena, Italy Department of Radiological Sciences, Diagnostic Imaging Unit, “Azienda Ospedaliera Universitaria Senese,” Siena, Italy Division of Radiodiagnostic, Firenze, Italy Division of Radiodiagnostic, “Università degli Studi della Campania Luigi Vanvitelli,” Naples, Italy Division of Radiodiagnostic, “Università degli Studi della Campania Luigi Vanvitelli,” Naples, Italy Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli”, Naples, Italy Division of Radiodiagnostic, ,” Pisa, Italy Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine (DIMES), “S. Orsola Hospital, University of Bologna,” Bologna, Italy Thoracic Medical Oncology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli,” Naples, Italy Division of Radiodiagnostic, “Università degli Studi della Campania Luigi Vanvitelli,” Naples, Italy Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli”, Naples, Italy Division of Radiodiagnostic, Firenze, ItalyObjective: 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.https://doi.org/10.1177/1073274820985786 |
| spellingShingle | Roberta Fusco PhD Vincenza Granata MD Maria Antonietta Mazzei MD Nunzia Di Meglio MD Davide Del Roscio MD Chiara Moroni MD Riccardo Monti MD Carlotta Cappabianca MD Carmine Picone MD Emanuele Neri MD Francesca Coppola MD Agnese Montanino MD Roberta Grassi MD Antonella Petrillo MD Vittorio Miele MD Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan Cancer Control |
| title | Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan |
| title_full | Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan |
| title_fullStr | Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan |
| title_full_unstemmed | Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan |
| title_short | Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan |
| title_sort | quantitative imaging decision support qids tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest ct scan |
| url | https://doi.org/10.1177/1073274820985786 |
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