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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Probing Neuronal Activity using Membrane Interfacial Water
摘要： The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection of prostate tumors, since it provides a plethora of mineable data, which can be used for both detection and prognosis of prostate cancer. While current voxel-resolution radiomics-driven prostate tumor detection approaches utilize quantitative radiomics features associated with individual voxels on an independent basis, the incorporation of additional information regarding the spatial and radiomics feature relationships between voxels has significant potential for achieving a more reliable detection performance. Motivated by this, we present a novel approach for automatic prostate cancer detection using a radiomics-driven conditional random field (RD-CRF) framework. In addition to the high-throughput extraction and utilization of a comprehensive set of voxel-level quantitative radiomics features, the proposed RD-CRF framework leverages inter-voxel spatial and radiomics feature relationships to ensure that the autodetected tumor candidates exhibit interconnected tissue characteristics reflective of cancerous tumors. We evaluated the performance of the proposed framework using clinical prostate MP-MRI data of 20 patients, and the results of RD-CRF framework demonstrated a clear improvement with respect to the state-of-the-art in quantitative radiomics for automatic voxel-resolution prostate cancer detection.
关键词： multi-parametric magnetic resonance imaging (MP-MRI),Automatic prostate cancer detection,radiomics,feature model,conditional random fields (CRF)
更新于2025-09-23 15:21:01
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Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
摘要： Purpose: To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment. Results: Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively. Materials and Methods: A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades. Conclusions: Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.
关键词： heterogeneity,dual energy CT,lung adenocarcinoma,radiomics,texture analysis
更新于2025-09-23 15:21:01
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Optimized feature extraction for radiomics analysis of <sup>18</sup> F-FDG-PET imaging
摘要： Radiomics analysis of 18F-FDG-PET/CT images promises for an improved in-vivo disease characterization. To date, several studies reported significant variations in textural features due to differences in patient preparation, imaging protocols, lesion delineation and feature extraction. Our objective was to study variations of features prior to a radiomics analysis of 18F-FDG-PET data and to identify those feature extraction and imaging protocol parameters that minimize radiomic feature variations across PET imaging systems. Methods. A whole-body National Electrical Manufacturers Association image quality phantom was imaged with 13 PET/CT systems at 12 different sites following local protocols. We selected 37 radiomic features related to the four largest spheres (17-37 mm) in the phantom. Based on a combined analysis of voxel size, bin size and lesion volume changes, feature and imaging system ranks were established. A 1-way analysis of variance (ANOVA) was performed over voxel size, bin size and lesion volume subgroups to identify the dependency and the trend change of feature variations across these parameters. Results. Feature ranking revealed that the gray-level co-occurrence matrix (GLCM) and shape features are the least sensitive to PET imaging system variations. Imaging system ranking illustrated that the use of point-spread function (PSF), small voxel sizes and narrow Gaussian post-filtering helped minimize feature variations. ANOVA subgroup analysis indicated that variations of each of the 37 features and for a given voxel size and bin size parameter can be minimized. Conclusions. Our study provides guidance to selecting optimized features from 18F-FDG-PET/CT studies. We were able to demonstrate that feature variations can be minimized for selected image parameters and imaging systems. These results can help imaging specialists and feature engineers in increasing the quality of future radiomic studies involving PET/CT.
关键词： radiomics,18F-FDG PET/CT,feature extraction
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE Workshop on Complexity in Engineering (COMPENG) - Florence, Italy (2018.10.10-2018.10.12)] 2018 IEEE Workshop on Complexity in Engineering (COMPENG) - Fractal-Radiomics as Complexity Analysis of CT and MRI Cancer Images
摘要： Cancer is the second leading cause of death globally. Early diagnosis can allow intervention to reduce mortality but due to cancer complex structure and spatial heterogeneity among different tumors and within each lesion, it is difficult to differentiate it from healthy tissue using conventional imaging techniques. Quantification of its complexity can be a prognostic tool for fighting this disease. In recent years, clinical imaging allows this quantification thanks to Radiomics, which extracts features from images. In this study, Fractal Dimension (FD) and Lacunarity (L) in computed tomography (CT) and magnetic resonance (MR) images for different kinds of cancer were examined using box counting method. Our aim is to highlight the potentiality of features based on fractal analysis, in order to obtain new indicators able to detect tumor spatial complexity and heterogeneity. The results indicated that both FD and L show problems linked to the lack of connection between complexity estimated with Radiomics and the underlying biological model.
关键词： cancer heterogeneity,lacunarity,complexity,fractal analysis,cancer,Radiomics
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) - St. Petersburg, Russia (2019.10.17-2019.10.18)] 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech) - Radiomics: Extracting more Features using Endoscopic Imaging
摘要： Cancer is the leading cause of death in the world and delayed detection being the cause of the most significant factor for its high mortality rate. Computers can help radiologists in analyzing medical images and detection of cancer. Radiomics refers to the computerized extraction of information from medical images and provides the potential for making cancer screening with high rapid and more accurate using machine learning algorithms. Endoscopic imaging and X-ray imaging (Computed tomography) are two common methods used in medical imaging. In this paper, the advantages and limitations of endoscopic and CT scan images discussed. Then the features that can be extracted from endoscopic and CT scans are discussed and finally these two imaging methods are considered and compared to use for computer-aided detection systems.
关键词： feature extraction,Computed tomography (CT) scan,Endoscopic image,Radiomics,texture features
更新于2025-09-12 10:27:22
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Hybrid 18F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study
摘要： Purpose: Detection of breast cancer (BC) metastasis at the early stage is important for the assessment of BC progression status. Image analysis represents a valuable tool for the management of oncological patients. Our preliminary study combined imaging parameters from hybrid 18F-FDG-PET/MRI and the expression level of the transcriptional factor Yin Yang 1 (YY1) for the detection of early metastases. Methods: The study enrolled suspected n = 217 BC patients that underwent 18F-FDG-PET/MRI scans. The analysis retrospectively included n = 55 subjects. n = 40 were BC patients and n = 15 imaging-negative female individuals were healthy subjects (HS). Standard radiomics parameters were extracted from PET/MRI image. RNA was obtained from peripheral blood mononuclear cells and YY1 expression level was evaluated by real time reverse transcription polymerase chain reactions (qRT-PCR). An enzyme-linked immuosorbent assay (ELISA) was used to determine the amount of YY1 serum protein. Statistical comparison between subgroups was evaluated by Mann-Whitney U and Spearman’s tests. Results: Radiomics showed a signi?cant positive correlation between Greg-level co-occurrence matrix (GLCM) and standardized uptake value maximum (SUVmax) (r = 0.8 and r = 0.8 respectively) in BC patients. YY1 level was signi?cant overexpressed in estrogen receptor (ER)-positive/progesteron receptor-positive/human epidermal growth factor receptor2-negative (ER+/PR+/HER2-) subtype of BC patients with synchronous metastasis (SM) at primary diagnosis compared to metachronous metastasis (MM) and HS (p < 0.001) and correlating signi?cantly with 18F-FDG-uptake parameter (SUVmax) (r = 0.48). Conclusions: The combination of functional 18F-FDG-PET/MRI parameters and molecular determination of YY1 could represent a novel integrated approach to predict synchronous metastatic disease with more accuracy than 18F-FDG-PET/MRI alone.
关键词： marker,Yin Yang 1,imaging,breast,radiomics
更新于2025-09-12 10:27:22
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External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy
摘要： Purpose The aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC). Methods Patients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts. Results The DFS model reached an accuracy of 90% (95% CI [79–98%]) (sensitivity 92–93%, specificity 87–89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90–99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80–99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82–85% and 71–86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56–60% only. Conclusions The previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.
关键词： External validation,Prediction,Cervical cancer,Chemoradiotherapy,Radiomics
更新于2025-09-04 15:30:14
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Deep learning for patient‐specific quality assurance: identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks
摘要： Purpose: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy quality assurance. In this work we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific QA. Methods: Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to 1) the error-free case 2) a random multileaf collimator (MLC) error case and 3) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID) and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k-nearest-neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two-class experiment classified images as error-free or containing any MLC error, and the three-class experiment classified images as error-free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold-based passing criteria were calculated for comparison. Results: 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two-class experiment and 64.3% in the three-class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two-class experiment and 53.7% in the three-class experiment. Variability between the results of the 4 machine learning classifiers was lower for the deep learning approach versus the texture feature approach. Both radiomic approaches were superior to threshold-based passing criteria. Conclusions: Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold-based passing criteria. The results suggest that radiomic quality assurance is a promising direction for clinical radiotherapy.
关键词： radiomics,IMRT QA,deep learning,quality assurance,texture features
更新于2025-09-04 15:30:14