A Pilot Study Examining the Prognostic Utility of Tumor Shrinkage on Cone-Beam Computed Tomography (CBCT) for Stage III Locally Advanced Non-Small Cell Lung Cancer Patients Treated with Definitive Chemoradiation

There has been growing interest in utilizing information from cone-beam computed tomography (CBCT) to help guide both treatment delivery and prognosis. In this assessment of locally advanced unresectable stage III non-small cell lung cancer (NSCLC) treated with definitive chemoradiation, we aimed to determine the survival advantage associated with using CBCT to measure tumor regression. Patient, tumor, and treatment characteristics were collected. The serial tumor shrinkage for each patient was determined from tumor volume contours on weekly CBCTs. Survival analysis was performed using the Kaplan-Meier technique and a Cox proportional hazards model. At least two-thirds of patients had a tumor volume reduction of at least 5% after each week of chemoradiation. A weekly reduction in tumor volume of 5% or greater seen on the CBCT images during radiation therapy was significantly associated with improved overall survival, which remained significant when adjusted for age, histology, grade, and T- and N-stages ( p = 0.0036). Additionally, the presence of N3 disease was associated with a five-fold increased risk of recurrence ( p = 0.0006) and a nearly three-fold increased risk of death ( p = 0.053) compared with N0-N2 disease. Tumor volume shrinkage observed in the CBCT images during definitive chemoradiation holds promise as a prognostic indicator of stage III NSCLC, especially given its affordability, availability, and applicability. Further evaluation in a prospective fashion is warranted to validate the tumor volume shrinkage and its clinical utility

Cancer · Chemoradiotherapy · Computed tomography · Cone beam computed tomography · Lung cancer · Nuclear medicine · Proportional hazards model · Prospective cohort study · Radiation therapy · Radiology · Shrinkage · Stage (stratigraphy · Survival analysis · Lung Cancer Diagnosis and Treatment · Medical Imaging Techniques and Applications · Medicine · Radiomics and Machine Learning in Medical Imaging · Internal Medicine · Oncology