Enhanced prognostic stratification of neoadjuvant treated lung squamous cell carcinoma by computationally-guided tumor regression scoring. (September 2020)

• Record Type: Journal Article
• Authors: Casanova, Ruben; Leblond, Anne-Laure; Wu, Chengguang; Haberecker, Martina; Burger, Irene A.; Soltermann, Alex
• Is Part Of: Lung cancer. Volume 147(2020)
• Page Start: 49
• Page End: 55
• Publication Date: 2020-09
• Subjects: Lung -- Squamous cell carcinoma -- Neoadjuvant chemotherapy -- Computational pathology -- Prognosis; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2020.07.003
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: Histological residual tumor burden after neoadjuvant chemotherapy is an important prognosticator for NSCLC. Computationally assessed histological residual tumor parameters correlate with metabolic response assessed by FDG PET/CT. Residual tumor burden computed in sections with the least regression is an independent prognostic factor. Residual tumor percentage is the best predictor of survival among other computational parameters. The proposed computational approach could aid current assessment of tumor regression. Abstract: Introduction: The amount of residual tumor burden after neoadjuvant chemotherapy is an important prognosticator, but for non-small cell lung carcinoma (NSCLC), no official regression scoring system is yet established. Computationally derived histological regression scores could provide unbiased and quantitative readouts to complement the clinical assessment of treatment response. Methods: Histopathologic tumor regression was microscopically assessed on whole cases in a neoadjuvant chemotherapy-treated cohort (NAC, n = 55 patients) of lung squamous cell carcinomas (LSCC). For each patient, the slide showing the least pathologic regression was selected for subsequent computational analysis and histological features were quantified: percentage of vital tumor cells (cTu.Percentage), total surface covered by vital tumor cells (cTu.Area), area of the largest vital tumor fragment (cTu.Size.max), and total number of vital tumor fragments (cTu.Fragments). A chemo-naïve LSCC cohort (CN, n = 104) was used for reference. For 23 of the 55 patients [ 18 F]-Fluorodeoxyglucose (FDG) PET/CT measurements of maximum standard uptake value (SUVmax ), background subtracted lesion activity (BSL) and background subtracted volume (BSV) were correlated with pathologic regression. Survival analysis was carried out using Cox regression and receiver operating characteristic (ROC) curve analysis using a 3-years cutoff. Results: All computational regression parameters significantly correlated with relative changes of BSV FDG PET/CT values after neoadjuvant chemotherapy. ROC curve analysis of histological parameters of NAC patients showed that cTu.Percentage was the most accurate prognosticator of overall survival (ROC curve AUC = 0.77, p-value = 0.001, Cox regression HR = 3.6, p = 0.001, variable cutoff < = 30 %). Conclusions: This study demonstrates the prognostic relevance of computer-derived histopathologic scores. Additionally, the analysis carried out on slides displaying the least pathologic regression correlated with overall pathologic response and PET/CT values. This might improve the objective histopathologic assessment of tumor response in neoadjuvant setting.