Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. [18F]FDG PET/CT Imaging
2.2. Radiomics Analysis
- Four predictive models per-lesion and -patient analysis: Performances of radiomics features extracted from PET and PET/CT, respectively, in assessing the treatment response for each lesion (without considering the patient treatment response) and in assessing the patient treatment response;
- Four models per-patient and -lesion analysis considering the only subset of liver lesions;
- Two models to evaluate the performances of PET and PET/CT radiomics features in discriminating liver metastasis from the rest of the other lesions.
2.3. Diagnostic Performance Evaluation
3. Results
3.1. [18F]FDG PET/CT Findings
3.2. Follow-Up
3.3. Radiomics Features Analysis
- For lesion analysis, GLRLM-based feature gray-level non-uniformity (GLZLM_GLNU) was selected [15,16] considering the only PET data set obtaining a Sensitivity 90.11%, Specificity 36.78%, Accuracy 66.72%, and AUROC 56.52% for the predictive DA classifier, while three features (GLZLM_ Zone Length Non-Uniformity—GLZLM_ ZLNU, and GLRLM_Short Run High Gray-Level Emphasis—GLRLM_SRHGE—between the CT features and GLZLM_GLNU between the PET features) were selected considering the PET/CT data set with Sensitivity 78.22%, Specificity 51.75%, Accuracy 66.63%, and AUROC 65.22%;
- For patient analysis, three features (GLZLM_ZLNU, GLZLM_High Gray-level Zone -GLZLM_HGZ-, Conventional Radial Intensity Mean Standardized Uptake Value body weight standard deviation squared -CONVENTIONAL_RIM_SUVbwstdev2-) were selected considering the PET-only data set with Sensitivity 32.07%, Specificity 92.11%, Accuracy 73.95% and AUROC 47.97%, and one feature (Conventional Hounsfield Unit Kurtosis -CONVENTIONAL_HUKurtosis-) was selected considering the PET/CT data set with Sensitivity 33.81%, Specificity 83.76%, Accuracy 68.70%, and AUROC 61%.
- For lesion analysis, one PET feature (GLZLM_GLNU) with Sensitivity 70.15%, Specificity 23.48%, Accuracy 54.21%, and AUROC 39.94%, and three PET/CT features (GLZLM_ZLNU, and GLRLM_SRHGE between the CT features and GLZLM_GLNU between the PET features) with Sensitivity 64.39%, Specificity 76.71%, Accuracy 68.69%, and AUROC 55.26%;
- For patient analysis, three PET features (GLZLM_ZLNU, GLZLM_HGZ, CONVENTIONAL_RIM_SUVbwstdev2) with Sensitivity 44.42%, Specificity 84.37%, Accuracy 59.03%, and AUROC 60.11%, and one PET/CT feature (CONVENTIONAL_HUKurtosis) with Sensitivity 33.12%, Specificity 73.74%, Accuracy 47.88%, and AUROC 43.48%.
- For PET images, one feature (Discretized SUVbw minimum—DISCRETIZED_SUVbwmin-) was extracted with Sensitivity 73.78%, Specificity 83.02%, Accuracy 76.91%, and AUROC 88.91%;
- For PET/CT images, two features (Discretized histogram energy—DISCRETIZED_HISTO_Energy—between the CT features and DISCRETIZED_SUVbwmin between the PET features) were extracted with Sensitivity 89.46%, Specificity 93.63%, Accuracy 91.02%, and AUROC 95.33%.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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All Patients (n = 52) | |
---|---|
Age (Mean ± SD) | 62.28 ± 11.23 y |
Sex | |
Male | 41 (77.35%) |
Female | 11 (22.65%) |
Grading | |
G1 | 2 (3.85%) |
G2 | 23 (44.23%) |
G2–G3 | 2 (3.85%) |
G3 | 10 (19.23%) |
Unknown | 15 (28.84%) |
First Adjuvant Therapy | |
Radiotherapy | 1 (1.92%) |
Chemotherapy | 49 (94.2%) |
Cht+RT | 2 (3.85%) |
PET Lesions | |
Liver | 23 (36.51%) |
Lymph nodes | 13 (19.05%) |
Lungs | 8 (12.7%) |
Presacral | 7 (11.11%) |
Peritoneum | 4 (6.35%) |
Rectum | 3 (4.76%) |
Spleen | 2 (3.17%) |
Bones | 2 (3.17%) |
Thorax | 1 (1.59%) |
Stages At Diagnosis | |
Stage I | 4 (7.69%) |
Stage II | 9 (17.30%) |
Stage III | 13 (25%) |
Stage IV | 10 (19.23%) |
Unknown | 16 (30.76%) |
Sensitivity | Specificity | Accuracy | AUROC | Features Selected | |||
---|---|---|---|---|---|---|---|
PET per-lesion | 90.11% | 36.78% | 66.72% | 56.52% | GLZLM_GLNU | ||
PET/CT per-lesion | 78.22% | 51.75% | 66.63% | 65.22% | GLZLM_ZLNU (CT) | GLRLM_SRHGE (CT) | GLZLM_GLNU (PET) |
PET per-patient | 32.07% | 92.11% | 73.95% | 47.97% | GLZLM_ZLNU | GLZLM_HGZ | CONVENTIONAL_RIM_SUVbwstdev2 |
PET/CT per-patient | 33.81% | 83.76% | 68.70% | 61.00% | CONVENTIONAL_HUKurtosis |
Sensitivity | Specificity | Accuracy | AUROC | Features Selected | |||
---|---|---|---|---|---|---|---|
PET per-lesion | 70.15% | 23.48% | 54.21% | 39.94% | GLZLM_GLNU | ||
PET/CT per-lesion | 64.39% | 76.71% | 68.69% | 55.26% | GLZLM_ZLNU (CT) | GLRLM_SRHGE (CT) | GLZLM_GLNU (PET) |
PET per-patient | 44.42% | 84.37% | 59.03% | 60.11% | GLZLM_ZLNU | GLZLM_HGZ | CONVENTIONAL_RIM_SUVbwstdev2 |
PET/CT per-patient | 33.12% | 73.74% | 47.88% | 43.48% | CONVENTIONAL_HUKurtosis |
Sensitivity | Specificity | Accuracy | AUROC | Features Selected | ||
---|---|---|---|---|---|---|
PET liver | 73.38% | 83.02% | 76.91% | 88.91% | DISCRETIZED_SUVbwmin | |
PET/CT liver | 89.46% | 93.63% | 91.02% | 95.33% | DISCRETIZED_HISTO_Energy (CT) | DISCRETIZED_SUVbwmin (PET) |
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Alongi, P.; Stefano, A.; Comelli, A.; Spataro, A.; Formica, G.; Laudicella, R.; Lanzafame, H.; Panasiti, F.; Longo, C.; Midiri, F.; et al. Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Appl. Sci. 2022, 12, 2941. https://doi.org/10.3390/app12062941
Alongi P, Stefano A, Comelli A, Spataro A, Formica G, Laudicella R, Lanzafame H, Panasiti F, Longo C, Midiri F, et al. Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Applied Sciences. 2022; 12(6):2941. https://doi.org/10.3390/app12062941
Chicago/Turabian StyleAlongi, Pierpaolo, Alessandro Stefano, Albert Comelli, Alessandro Spataro, Giuseppe Formica, Riccardo Laudicella, Helena Lanzafame, Francesco Panasiti, Costanza Longo, Federico Midiri, and et al. 2022. "Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome" Applied Sciences 12, no. 6: 2941. https://doi.org/10.3390/app12062941