Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. PET/CT Acquisition Protocol
2.2. Qualitative Evaluation of FDG PET
2.3. Image Pre-Processing and ROI Selection
2.4. Extraction of Radiomics Features and Machine Learning Classification
3. Results
Analysis of Radiomics Features
- −
- ROI 1
- original_glcm_Idmn
- original_glcm_Id: with the following values of SS 84.92%, SP 75.13%, PR 73.75%, AC 79.56% (p < 0.001).
- −
- ROI 2
- original_glcm_MaximumProbability: with the following values of SS 88.67%, SP 46.81%, PR 59.47%, AC 65.57% (p < 0.001).
- −
- ROI 3
- original_glcm_Id: with the following values of SS 93.83%, SP 61.80%, PR 67.51%, AC 76.15% (p < 0.001).
- −
- ROI 4
- original_glcm_MaximumProbability
- original_firstorder_Maximum: with the following values of SS 86.33%, SP 64.93%, PR 66.88%, AC 74.58% (p < 0.001).
- −
- ROI 1
- original_glcm_Idmn: with the following values of SS 66.39%, SP 57.51%, PR 58.46%, AC 61.51% and (p = 0.004).
- −
- ROI 2
- original_glcm_MCC
- original_glcm_MaximumProbability: with the following values of SS 75.16%, SP 80.50%, PR 77.68%, AC 78.05% and (p = 0.002).
- −
- ROI 3
- original_glcm_Idmn: with the following values of SS 80.88%, SP 76.85%, PR 75.63%, AC 78.76% and (p < 0.001).
- −
- ROI 4
- original_glcm_MaximumProbability: with the following values of SS 75.50%, SP 55.25%, PR 59.53%, AC 64.96% (p = 0.0040).
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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ROI 1 | Areas | Label Index | ROI 2 | Areas | Label Index | ROI 3 | Areas | Label Index | ROI 4 | Areas | Label Index |
---|---|---|---|---|---|---|---|---|---|---|---|
Right Hippocampus | 47 | Right (AOrG anterior orbital gyrus | 104 | Right FuG fusiform gyrus | 122 | Right PO parietal operculum | 174 | ||||
Right PHG parahippocampal gyrus | 170 | Right MOrG medial orbital gyrus | 146 | Right GRe gyrus rectus | 124 | Right PoG postcentral gyrus | 176 | ||||
Right Ent entorhinal area | 116 | Right OpIFGopercular part of the inferior frontal gyrus | 162 | Right ITG inferior temporal gyrus | 132 | Right SPL superior parietal lobule | 198 | ||||
Right MTG middle temporal gyrus | 154 | Right OrIFG orbital part of the inferior frontal gyrus | 164 | Right TMP temporal pole | 202 | Right PCgG posterior cingulate gyrus | 166 | ||||
Left Hippocampus | 48 | Right MFC medial frontal cortex | 140 | Left FuG fusiform gyrus | 123 | Right PCuprecuneus | 168 | ||||
Left PHG parahippocampal gyrus | 171 | Right MFG middle frontal gyrus | 142 | Left GRe gyrus rectus | 125 | Left PoG postcentral gyrus | 177 | ||||
Left Ent entorhinal area | 117 | Left MOrG medial orbital gyrus | 147 | Left ITG inferior temporal gyrus | 133 | Left PO parietal operculum | 175 | ||||
Left MTG middle temporal gyrus | 155 | Left AOrG anterior orbital gyrus | 105 | Left TMP temporal pole | 203 | Left SPL superior parietal lobule | 199 | ||||
Left OpIFGopercular part of the inferior frontal gyrus | 163 | Left PCuprecuneus | 169 | ||||||||
Left OrIFG orbital part of the inferior frontal gyrus | 165 | Left PCgG posterior cingulate gyrus | 167 | ||||||||
Left MFC medial frontal cortex | 141 | ||||||||||
Left MFG middle frontal gyrus | 143 |
pt N° | Sex | Age | Schooling | MMSE | CDR | MRI | FDG PET | Amy-PET | Final Diagnosis (MDT) |
---|---|---|---|---|---|---|---|---|---|
1 | F | 64 | 21 | 19 | 1 | 1 | 1 | 1 | 1 |
2 | M | 81 | 5 | 27 | 0 | 0 | 0 | 0 | 0 |
3 | F | 59 | 8 | 23 | 0.5 | 1 | 0 | 0 | 0 |
4 | M | 63 | 18 | 21 | 1 | 1 | 1 | 1 | 1 |
5 | F | 79 | 5 | 20 | 0.5 | 1 | 0 | 0 | 0 |
6 | F | 80 | 5 | 18 | 2 | 1 | 1 | 1 | 1 |
7 | F | 75 | 5 | 22 | 1 | 1 | 1 | 1 | 1 |
8 | F | 72 | 5 | 12 | 1 | 1 | 1 | 1 | 1 |
9 | F | 77 | 5 | 19 | 2 | 1 | 0 | 0 | 0 |
10 | F | 71 | 13 | 20 | 2 | 1 | 1 | 1 | 1 |
11 | F | 75 | 5 | 17 | 2 | 1 | 1 | 0 | 0 |
12 | F | 83 | 5 | 20 | 1 | 1 | 0 | 0 | 0 |
13 | M | 58 | 18 | 9 | 2 | 1 | 1 | 1 | 1 |
14 | F | 61 | 13 | 22 | 2 | 0 | 0 | 1 | 1 |
15 | M | 66 | 13 | 21 | 1 | 0 | 1 | 1 | 1 |
16 | F | 75 | 8 | 26 | 0.5 | 1 | 0 | 0 | 0 |
17 | F | 53 | 13 | 13 | 1 | 1 | 1 | 1 | 1 |
18 | M | 66 | 8 | 28 | 0.5 | 1 | 1 | 1 | 1 |
19 | M | 72 | 18 | 24 | 0.5 | 1 | 0 | 0 | 0 |
20 | M | 79 | 13 | 17 | 1 | 1 | 1 | 1 | 1 |
21 | M | 69 | 13 | 28 | 0.5 | 1 | 1 | 0 | 0 |
22 | F | 73 | 13 | 25 | 1 | 1 | 1 | 1 | 1 |
23 | M | 76 | 8 | 28 | 0.5 | 1 | 1 | 0 | 0 |
24 | M | 74 | 5 | 29 | 0.5 | 1 | 0 | 0 | 0 |
25 | M | 61 | 18 | 22 | 2 | 0 | 1 | 1 | 1 |
26 | F | 70 | 8 | 25 | 1 | 1 | 1 | 0 | 0 |
27 | F | 68 | 13 | 15 | 2 | 1 | 1 | 1 | 1 |
28 | M | 65 | 8 | 25 | 0,5 | 1 | 1 | 1 | 1 |
29 | M | 80 | 8 | 18 | 1 | 1 | 0 | 0 | 0 |
30 | F | 71 | 5 | 4 | 3 | 0 | 1 | 1 | 1 |
31 | M | 78 | 8 | 13 | 1 | 1 | 1 | 0 | 0 |
32 | F | 74 | 8 | 10 | 2 | 1 | 1 | 1 | 1 |
33 | M | 80 | 0 | 18 | 1 | 1 | 0 | 0 | 0 |
34 | M | 78 | 5 | 22 | 0.5 | 1 | 0 | 0 | 0 |
35 | M | 71 | 0 | 17 | 1 | 1 | 1 | 0 | 0 |
36 | M | 58 | 8 | 21 | 1 | 1 | 1 | 1 | 1 |
37 | F | 63 | 18 | 24 | 1 | 1 | 0 | 0 | 0 |
38 | F | 74 | 5 | 28 | 0.5 | 1 | 1 | 1 | 1 |
39 | M | 77 | 5 | 30 | 0.5 | 1 | 0 | 0 | 0 |
40 | M | 65 | 8 | 20 | 1 | 0 | 1 | 1 | 1 |
41 | M | 62 | 17 | 21,46 | 0.5 | 1 | 1 | 1 | 0 |
42 | F | 77 | 5 | 22 | 1 | 1 | 1 | 1 | 1 |
43 | F | 66 | 8 | 26 | 0.5 | 1 | 0 | 0 | 0 |
Features Selected for Each ROI | Sensitivity [%] | Specificity [%] | Precision [%] | Accuracy [%] | p-Value |
---|---|---|---|---|---|
ROI 1 | |||||
original_glcm_Idmn original_glcm_Id | 84.92 | 75.13 | 73.75 | 79.56 | <0.05 |
ROI 2 | |||||
original_glcm_MCC | 88.67 | 46.81 | 59.47 | 65.57 | <0.05 |
ROI 3 | |||||
original_glcm_Id | 93.83 | 61.80 | 67.51 | 76.15 | <0.05 |
ROI 4 | |||||
original_glcm_Maximum Probability | 86.33 | 64.93 | 66.88 | 74.58 | <0.05 |
Features Selected for Each ROI | Sensitivity [%] | Specificity [%] | Precision [%] | Accuracy [%] | p-Value |
---|---|---|---|---|---|
ROI 1 | |||||
original_glcm_Idmn | 66.39 | 57.51 | 58.46 | 61.51 | 0.004 |
ROI 2 | |||||
original_glcm_MCC original_glcm_MaximumProbability | 75.16 | 80.50 | 77.68 | 78.05 | 0.002 |
ROI 3 | |||||
original_glcm_Idmn | 80.88 | 76.85 | 75.63 | 78.76 | <0.05 |
ROI 4 | |||||
original_glcm_Maximum Probability original_firstorder_Maximum | 75.50 | 55.25 | 59.53 | 64.96 | 0.004 |
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Alongi, P.; Laudicella, R.; Panasiti, F.; Stefano, A.; Comelli, A.; Giaccone, P.; Arnone, A.; Minutoli, F.; Quartuccio, N.; Cupidi, C.; et al. Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis. Diagnostics 2022, 12, 933. https://doi.org/10.3390/diagnostics12040933
Alongi P, Laudicella R, Panasiti F, Stefano A, Comelli A, Giaccone P, Arnone A, Minutoli F, Quartuccio N, Cupidi C, et al. Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis. Diagnostics. 2022; 12(4):933. https://doi.org/10.3390/diagnostics12040933
Chicago/Turabian StyleAlongi, Pierpaolo, Riccardo Laudicella, Francesco Panasiti, Alessandro Stefano, Albert Comelli, Paolo Giaccone, Annachiara Arnone, Fabio Minutoli, Natale Quartuccio, Chiara Cupidi, and et al. 2022. "Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis" Diagnostics 12, no. 4: 933. https://doi.org/10.3390/diagnostics12040933