Potential of Radiomics, Dosiomics, and Dose Volume Histograms for Tumor Response Prediction in Hepatocellular Carcinoma following 90Y-SIRT.

Abstract

Purpose: We evaluate the role of radiomics, dosiomics, and dose-volume constraints (DVCs) in predicting the response of hepatocellular carcinoma to selective internal radiation therapy with ⁹⁰Y with glass microspheres.

Methods: ^99mTc-macroagregated albumin (^99mTc-MAA) and ⁹⁰Y SPECT/CT images of 17 patients were included. Tumor responses at three months were evaluated using modified response evaluation criteria in solid tumors criteria and patients were categorized as responders or non-responders. Dosimetry was conducted using the local deposition method (Dose) and biologically effective dosimetry. A total of 264 DVCs, 321 radiomic features, and 321 dosiomic features were extracted from the tumor, normal perfused liver (NPL), and whole normal liver (WNL). Five different feature selection methods in combination with eight machine learning algorithms were employed. Model performance was evaluated using area under the AUC, accuracy, sensitivity, and specificity.

Results: No statistically significant differences were observed between neither the dose metrics nor radiomicas or dosiomics features of responders and non-responder groups. ⁹⁰Y-dosiomics models with any given set of inputs outperformed other models. This was also true for ⁹⁰Y-radiomics from SPECT and SPECT-clinical features, achieving an AUC, accuracy, sensitivity, and specificity of 1. Among MAA-dosiomic and radiomic models, two models showed AUC ≥ 0.91. While the performance of MAA-dose volume histogram (DVH)-based models were less promising, the ⁹⁰Y-DVH-based models showed strong performance (AUC ≥ 0.91) when considered independently of clinical features.

Conclusion: This study demonstrated the potential of ^99mTc-MAA and ⁹⁰Y SPECT-derived radiomics, dosiomics, and dosimetry metrics in establishing predictive models for tumor response.