Interpretable AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases.

Purpose: AI modeling physicians' clinical decision-making (CDM) can improve the efficiency and accuracy of clinical practice or serve as a surrogate to provide initial consultations to patients seeking secondary opinions. In this study, we developed an interpretable AI model that predicts dose fractionation for patients receiving radiation therapy for brain metastases with an interpretation of its decision-making process.

Materials/methods: 152 patients with brain metastases treated by radiosurgery from 2017 to 2021 were obtained. CT images and target and organ-at-risk (OAR) contours were extracted. Eight non-image clinical parameters were also extracted and digitized, including age, the number of brain metastasis, ECOG performance status, presence of symptoms, sequencing with surgery (pre- or post-operative radiation therapy), de novo vs. re-treatment, primary cancer type, and metastasis to other sites. 3D convolutional neural networks (CNN) architectures with encoding paths were built based on the CT data and clinical parameters to capture three inputs: (1) Tumor size, shape, and location; (2) The spatial relationship between tumors and OARs; (3) The clinical parameters. The models fuse the features extracted from these three inputs at the decision-making level to learn the input independently to predict dose prescription. Models with different independent paths were developed, including models combining two independent paths (IM-2), three independent paths (IM-3), and ten independent paths (IM-10) at the decision-making level. A class activation score and relative weighting were calculated for each input path during the model prediction to represent the role of each input in the decision-making process, providing an interpretation of the model prediction. The actual prescription in the record was used as ground truth for model training. The model performance was assessed by 19-fold cross-validation, with each fold consisting of randomly selected 128 training, 16 validation, and 8 testing subjects.

Result: The dose prescriptions of 152 patient cases included 48 cases with 1 × 24 Gy, 48 cases with 1 × 20-22 Gy, 32 cases with 3 × 9 Gy, and 24 cases with 5 × 6 Gy prescribed by 8 physicians. IM-2 achieved slightly superior performance than IM-3 and IM-10, with 131 (86%) patients classified correctly and 21 (14%) patients misclassified. IM-10 provided the most interpretability with a relative weighting for each input: target (34%), the relationship between target and OAR (35%), ECOG (6%), re-treatment (6%), metastasis to other sites (6%), number of brain metastases (3%), symptomatic (3%), pre/post-surgery (3%), primary cancer type (2%), age (2%), reflecting the importance of the inputs in decision making. The importance ranking of inputs interpreted from the model also matched closely with a physician's own ranking in the decision process.

Conclusion: Interpretable CNN models were successfully developed to use CT images and non-image clinical parameters to predict dose prescriptions for brain metastases patients treated by radiosurgery. Models showed high prediction accuracy while providing an interpretation of the decision process, which was validated by the physician. Such interpretability makes the model more transparent, which is crucial for the future clinical adoption of the models in routine practice for CDM assistance.