Automated image analysis tool for tumor volume growth rate to guide precision cancer therapy: EGFR-mutant non-small-cell lung cancer as a paradigm

Purpose: To develop an automated analytic module for calculation of tumor growth rate from serial CT scans and to apply the module and evaluate reproducibility in a pilot cohort of advanced NSCLC patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Materials and methods: The module utilized a commercially available image-processing workstation equipped with a validated tumor volume measurement tool. An automated analytic software module was programmed with the capability to record and display serial tumor volume changes and to calculate tumor volume growth rate over time and added to the workstation. The module was applied to evaluate the tumor growth rate in a pilot cohort of 24 EGFR-mutant patients treated with EGFR inhibitors, and reproducibility references as tested by two independent thoracic radiologists. Results: The module analyzed chest CT scans from 24 patients (5 males, 19 females; median age: 61) with a median of 8 scans per patient, totaling 227 scans and provided a graphical display with an automated and instant calculation of tumor growth rate after the nadir volume for each patient. High inter and intraobserver agreements were noted for tumor growth rates, with concordance correlation coefficients of 0.9323 and 0.9668, respectively. Interpretation of slow versus fast tumor growth using previously identified threshold of ≤0.15/month had a perfect interobserver agreement (κ = 1.00), and an excellent intraobserver agreement (κ = 0.895). Conclusions: The present study describes the development of an image analytic module for assessing tumor growth rate and the data demonstrates the functionality and reproducibility of the module in a pilot cohort of EGFR-mutant NSCLC patients treated with EGFR-TKI. The image analytic module is an initial step for clinical translation of the tumor growth rate approach to guide cancer treatment in precision oncology.