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Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis—usefulness of density correction of volumetric CT data

  • CHEST RADIOLOGY
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Abstract

The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1 year 0.72; 3 years 0.82; 5 years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.

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Acknowledgements

The authors thank Dr. Luca Boldrini for his advice on revision of the manuscript.

Funding

The research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli 8, Largo Francesco Vito 1, 00168, Rome, Italy

    Alessandra Farchione, Anna Rita Larici, Giuseppe Cicchetti, Stefano Lo Cicero, Vincenzo Valentini, Lorenzo Bonomo & Riccardo Manfredi

  2. Dipartimento Universitario Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy

    Anna Rita Larici, Giuseppe Cicchetti, Stefano Lo Cicero, Vincenzo Valentini, Lorenzo Bonomo & Riccardo Manfredi

  3. Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli 8, Largo Francesco Vito 1, 00168, Rome, Italy

    Carlotta Masciocchi

  4. Dipartimento Scienze Cardiovascolari e Toraciche, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli 8, Largo Francesco Vito 1, 00168, Rome, Italy

    Maria Teresa Congedo

  5. UOC Radiologia, Ospedale G. Mazzini, ASL Teramo, Piazza Italia, 64100, Teramo, Italy

    Paola Franchi

  6. Dipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, C/o Piazzale spedali civili 1, 25123, Brescia, Italy

    Roberto Gatta

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  1. Alessandra Farchione
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Correspondence to Alessandra Farchione.

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The study is part of an institutional review board-approved protocol. All the procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. According to the retrospective design of the study, when provided by ethical and organizational reasons, formal consent was waived.

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Farchione, A., Larici, A.R., Masciocchi, C. et al. Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis—usefulness of density correction of volumetric CT data. Radiol med 125, 625–635 (2020). https://doi.org/10.1007/s11547-020-01157-3

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