Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT

Abstract

Purpose: ¹²³I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using ¹²³I-Ioflupane SPECT images at year 0.

Methods: In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and ¹²³I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.

Results: For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.

Conclusion: The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using ¹²³I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Keywords: 123I-Ioflupane; Deep feature; Deep learning; Parkinson’s disease; Radiomics; SPECT.

Fig. 1

¹²³I-Ioflupane SPECT images at 0–2 HYS stages of PD at year 0 after first diagnosis. The white box corresponds to the reference region in the occipital cortex with non-specific uptake and excluding ventricular regions

Fig. 2

The architecture of DenseNet for HYS prediction and DF extraction

Fig. 3

The workflow of this study

Fig. 4

The (a) accuracy and (b) AUC values of prediction models of HYS at year 0 after first diagnosis. The white box corresponds to the highest value, and the differences between this highest value and those of other models were analyzed (not applicable to the DL model). The proportion of IF (green), RaF (light blue) and DF (dark blue) in each model was shown. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001

Fig. 5

The (a) accuracy and (b) AUC values of prediction models of HYS at year 4 after first diagnosis. The white box corresponds to the highest value, and the differences between this highest value and those of other models were analyzed. The proportion of IF (green), RaF (light blue) and DF (dark blue) in each model was shown. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001