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Khateri M, Babapour Mofrad F, Geramifar P, Jenabi E. Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade. Phys Eng Sci Med 2024:10.1007/s13246-024-01402-3. [PMID: 38526647 DOI: 10.1007/s13246-024-01402-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 03/27/2024]
Abstract
Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.
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Affiliation(s)
- Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshid Babapour Mofrad
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Cheng JA, Lin YC, Lin Y, Wu RC, Lu HY, Yang LY, Chiang HJ, Juan YH, Lai YC, Lin G. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics (Basel) 2023; 13:3632. [PMID: 38132216 PMCID: PMC10742777 DOI: 10.3390/diagnostics13243632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. METHODS We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. RESULTS Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. CONCLUSIONS CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
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Affiliation(s)
- Jih-An Cheng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Ren-Chin Wu
- Department of Pathology, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ying Lu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Lan-Yan Yang
- Clinical Trial Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ju Chiang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yu-Hsiang Juan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
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