A spectral CT-derived metrics-based radiomics model in differentiating de novo osteoblastic bone metastasis and bone islands in newly diagnosed cancer patients

TL;DR

This study uses spectral CT radiomics to better distinguish between bone metastases and bone islands in newly diagnosed cancer patients.

Contribution

The novel contribution is a combined radiomics model using spectral CT metrics that improves differentiation of osteoblastic lesions.

Findings

The combined model achieved an AUC of 0.874, outperforming individual models.

Radiomics features from spectral CT metrics significantly enhance lesion differentiation.

Shape-based models performed worse than spectral CT-based models.

Abstract

To investigate the value of radiomics features extracted from plain and enhanced spectral CT-derived metrics in differentiating osteoblastic bone metastasis (OBM) and bone island (BI) in newly diagnosed cancer patients.

From January to November 2020, 51 newly diagnosed cancer patients with 204 bone lesions (OBM = 116, BI = 88) receiving spectral CT were retrospectively enrolled. 40–140 keV mono-energy images were generated from plain CT and contrast-enhanced CT, and material-decomposition images, including water (calcium) and calcium (water) substrate density images from plain CT and Iodine (calcium) substrate density images from contrast-enhanced CT. Radiomics features were extracted from the manually segmented lesions, including shape feature set, material-separation feature set, plain spectral CT feature set, and enhanced spectral CT feature set. U-test and LASSO analysis were sequentially used to select the most relevant features. The shape model, material-separation model, plain CT model, contrast-enhanced CT model, and combined model were built using Random Forest with model performance evaluated using ROC analysis and compared using the Delong test.

After feature selection, four features were selected for the shape set, seven features for the material-separation set, seven features for the plain spectral CT set, and nine features for the enhanced spectral CT set. The AUC of the shape model was significantly smaller than that of the other four models (all P < 0.05). The combined model (AUC = 0.874, 95%CI: 0.821-0.916) outperformed the material-separation model (AUC = 0.828, 95%CI: 0.769-0.877, P = 0.005), the plain spectral CT model (AUC = 0.820 95%CI: 0.760-0.870, P = 0.005) and the enhanced spectral CT model (AUC = 0.838, 95%CI: 0.780-0.886, P = 0.005).

The radiomics features derived from spectral CT metrics will enhance the differentiation of de novo OBM and BI in newly diagnosed cancer patients.

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