Fully Automated Online Adaptive Radiation Therapy Decision-Making for Cervical Cancer Using Artificial Intelligence.

Abstract

Background: Interfraction variations during radiation therapy pose a challenge for patients with cervical cancer, highlighting the benefits of online adaptive radiation therapy (oART). However, adaptation decisions rely on subjective image reviews by physicians, leading to high interobserver variability and inefficiency. This study explores the feasibility of using artificial intelligence for decision-making in oART.

Methods and materials: A total of 24 patients with cervical cancer who underwent 671 fractions of daily fan-beam computed tomography (FBCT) guided oART were included in this study, with each fraction consisting of a daily FBCT image series and a pair of scheduled and adaptive plans. Dose deviations of scheduled plans exceeding predefined criteria were labeled as "trigger," otherwise as "nontrigger." A data set comprising 588 fractions from 21 patients was used for model development. For the machine learning model (ML), 101 morphologic, gray-level, and dosimetric features were extracted, with feature selection by the least absolute shrinkage and selection operator (LASSO) and classification by support vector machine (SVM). For deep learning, a Siamese network approach was used: the deep learning model of contour (DL_C) used only imaging data and contours, whereas a deep learning model of contour and dose (DL_D) also incorporated dosimetric data. A 5-fold cross-validation strategy was employed for model training and testing, and model performance was evaluated using the area under the curve (AUC), accuracy, precision, and recall. An independent data set comprising 83 fractions from 3 patients was used for model evaluation, with predictions compared against trigger labels assigned by 3 experienced radiation oncologists.

Results: Based on dosimetric labels, the 671 fractions were classified into 492 trigger and 179 nontrigger cases. The ML model selected 39 key features, primarily reflecting morphologic and gray-level changes in the clinical target volume (CTV) of the uterus (CTV_U), the CTV of the cervix, vagina, and parametrial tissues (CTV_C), and the small intestine. It achieved an AUC of 0.884, with accuracy, precision, and recall of 0.825, 0.824, and 0.827, respectively. The DL_C model demonstrated superior performance with an AUC of 0.917, accuracy of 0.869, precision of 0.860, and recall of 0.881. The DL_D model, which incorporated additional dosimetric data, exhibited a slight decline in performance compared with DL_C. Heatmap analyses indicated that for trigger fractions, the deep learning models focused on regions where the reference CT's CTV_U did not fully encompass the daily FBCT's CTV_U. Evaluation on an independent data set confirmed the robustness of all models. The weighted model's prediction accuracy significantly outperformed the physician consensus (0.855 vs 0.795), with comparable precision (0.917 vs 0.925) but substantially higher recall (0.887 vs 0.790).

Conclusion: This study proposes machine learning and deep learning models to identify treatment fractions that may benefit from adaptive replanning in radical radiation therapy for cervical cancer, providing a promising decision-support tool to assist clinicians in determining when to trigger the oART workflow during treatment.