Prognostic value of histogram analysis in advanced non-small cell lung cancer: a radiomic study

• Published in: Oncotarget 2018-01, Vol.9 (2), p.1906-1914
• Main Authors: Virginia, Bluthgen Maria, Laura, Faivre, Silvia, Rosellini, Roberto, Ferrara, Francesco, Facchinetti, Eva, Haspinger, Charles, Ferte, Samy, Ammari, Stefan, Michiels, Jean-Charles, Soria, Caroline, Caramella, Benjamin, Besse
• Format: Article
• Language: English
• Subjects: Research Paper
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.22316

Quantitative assessment of heterogeneity by histogram analysis (HA) of tumor images can potentially provide a non-invasive prognostic biomarker. We assessed the prognostic value of HA and evaluated a correlation with molecular signature. CT scans performed between July 2009 and January 2015 from 692 patients were reviewed. HA was performed on scans from 313 patients in the training dataset and 108 in the validation dataset. Median follow-up were 33.7 months [range: 1.7 - 65.5] and 29 months [range: 1.1 - 35.6] with a median overall survival (OS) of 11.7 months [95%CI: 10.7 - 13.1] and 9.5 months [95%CI: 7.9 - 12.7] respectively. Primary mass entropy in coarse texture with spatial filter 3.3 was prognostic for OS in a multivariate Cox analysis (HR: 1.3 [95%CI: 1.1 - 1.5], =0.001). Results were not reproduced in our validation set and no correlation with molecular signature was identified. HA using filtration-histogram method was applied to the region of interest on the primary tumor in enhanced-CT acquired as diagnostic/staging routine, from a cohort of patients with advanced non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. The resultants parameters were prospectively applied to a validation dataset. CT scans, clinical and molecular data were retrospectively collected. Cox proportional hazard models were used for survival analysis and Wilcoxon test for correlations. Primary mass entropy was significantly associated with survival in the training set but was not validated in the validation cohort, raising doubt over the reliability of published data from small cohorts.