A method for predictive modeling of tumor regression for lung adaptive radiotherapy

Purpose The purpose of this work is to create a decision support methodology to predict when patients undergoing radiotherapy treatment for locally advanced lung cancer would potentially benefit from adaptive radiotherapy. The proposed methodology seeks to eliminate the manual subjective review by developing an automated statistical learning model to predict when tumor regression would trigger implementation of adaptive radiotherapy based on quantified anatomic changes observed in individual patients on‐treatment cone beam computed tomographies (CTs). This proposed process seeks to improve the efficacy and efficiency of both the existing manual and automated adaptive review processes for locally advanced stage III lung cancer. Methods A predictive algorithm was developed as a decision support tool to determine the potential utility of mid‐treatment adaptive radiotherapy based on anatomic changes observed on 1158 daily CBCT images across 43 patients. The anatomic changes on each axial slice within specified regions‐of‐interest were quantified into a single value utilizing imaging similarity criteria comparing the daily CBCT to the initial simulation CT. The range of the quantified metrics for each fraction across all axial slices are reduced to specified quantiles, which are used as the predictive input to train a logistic regression algorithm. A “ground‐truth” of the need for adaptive radiotherapy based on tumor regression was evaluated systematically on each of the daily CBCTs and used as the classifier in the logistic regression algorithm. Accuracy of the predictive model was assessed utilizing both a tenfold cross validation and an independent validation dataset, with the sensitivity, specificity, and fractional accuracy compared to the ground‐truth. Results The sensitivity and specificity for the individual daily fractions ranged from 87.9%–94.3% and 91.9%‐98.6% for a probability threshold of 0.2–0.5, respectively. The corresponding average treatment fraction difference between the model predictions and assessed ART “ground‐truth” ranged from −2.25 to −0.07 fractions, with the model predictions consistently predicting the potential need for ART earlier in the treatment course. By initially utilizing a lower probability threshold, the higher sensitivity minimizes the chance of false negative by alerting the clinician to review a higher number of questionable cases. Conclusions The proposed methodology accurately predicted the first fraction at which individual patients may benefit from ART based on quantified anatomic changes observed in the on‐treatment volumetric imaging. The generalizability of the proposed method has potential to expand to additional modes of adaptive radiotherapy for lung cancer patients with observed underlying anatomic changes.