Abstract
This study aimed to investigate the performance of 18F-DCFPyL positron emission tomography/computerized tomography (PET/CT) models for predicting benign-vs-malignancy, high pathological grade (Gleason score > 7), and clinical D'Amico classification with machine learning. The study included 138 patients with treatment-naïve prostate cancer presenting positive 18F-DCFPyL scans. The primary lesions were delineated on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying five different binning approaches. Three layer-machine learning approaches were used to identify relevant in vivo features and patient characteristics and their relative weights for predicting high-risk malignant disease. The weighted features were integrated and implemented to establish individual predictive models for malignancy (Mm), high path-risk lesions (by Gleason score) (Mgs), and high clinical risk disease (by amico) (Mamico). The established models were validated in a Monte Carlo cross-validation scheme. In patients with all primary prostate cancer, the highest areas under the curve for our models were calculated. The performance of established models as revealed by the Monte Carlo cross-validation presenting as the area under the receiver operator characteristic curve (AUC): 0.97 for Mm, AUC: 0.73 for Mgs, AUC: 0.82 for Mamico. Our study demonstrated the clinical potential of 18F-DCFPyL PET/CT radiomics in distinguishing malignant from benign prostate tumors, and high-risk tumors, without biopsy sampling. And in vivo 18F-DCFPyL PET/CT can be considered a noninvasive tool for virtual biopsy for personalized treatment management.
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Abbreviations
- PET:
-
Positron emission tomography
- PCa:
-
Prostate cancer
- GS:
-
Gleason score
- PSA:
-
Prostate-specific antigen
- CT:
-
Computed tomography
- PSMA:
-
Prostate-specific membrane antigen
- TNM:
-
Tumor node metastasis
- MRI:
-
Magnetic resonance imaging
- CDSS:
-
Clinical decision support system
- SUV:
-
Standardized uptake value
- SUVmean:
-
Standardized uptake mean value
- SUVmax:
-
Standardized uptake max value
- SUVpeak:
-
Standardized uptake peak value
- SUVR:
-
Ratio of standardized uptake max value
- VOIs:
-
Volumes of interest
- PSMA-TV:
-
Total volume of PSMA
- TL-PSMA:
-
Total lesion of PSMA
- SMOTE:
-
Synthetic minority over-sampling technique
- IBSI:
-
Imaging biomarker standardization initiatives
- MC:
-
Monte Carlo
- ML:
-
Machine learning
- GLCM:
-
Gray-level cooccurrence matrix
- GLDZM:
-
Gray-level distance zone matrix
- NGLDM:
-
Neighboring grey level dependence matrix
- NGTDM:
-
Neighborhood grey tone difference matrix
- GLRLM:
-
Gray-level run-length matrix
- BYS:
-
Bayesian classification
- MGWC:
-
Multi-Gaussian weighting
- RF:
-
Random forest
- SVM:
-
Support vector machine
- WHO:
-
World Health Organization
- ISUP:
-
International Society of Urological Pathology
- UICC:
-
Union for International Cancer Control
- ACC:
-
Accuracy
- SENS:
-
Sensitivity
- SPEC:
-
Specificity
- PPV:
-
Positive predictive value
- NPV:
-
Negative predictive value
- AUC:
-
Area under the receiver operating characteristic curve
- DREs:
-
Digital rectal examinations
- M m :
-
Predictive models for malignancy
- M gs :
-
Predictive models for high path-risk lesions (by Gleason score)
- M amico :
-
Predictive models for high clinical risk disease (by amico)
- ROI:
-
Region of interest
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Acknowledgements
The authors would like to thank Qilu Hospital of China, Vienna General Hospital of Austria, and Evomics Medical Technology Co., Ltd. for the support.
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YKL performed data curation, image analysis, investigation, methodology, original and revised draft writing and uploading; SLH and SWW performed machine learning analysis and original draft writing; FL and JN performed original draft writing; PS, JFL and SH performed radioactive tracer synthesis and quality control; LLQ performed image analysis; XL and XL performed methodology, writing, reviewing and editing.
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This study was approved by the Ethical Committee of Shandong University Qilu Hospital, Jinan, Shandong, China (KYLL-2017–573). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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The datasets generated during analysis in the current study are available from the corresponding author.
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Li, Y., Li, F., Han, S. et al. Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study. Phenomics 3, 576–585 (2023). https://doi.org/10.1007/s43657-023-00108-y
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DOI: https://doi.org/10.1007/s43657-023-00108-y