A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features
Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manua…
Radiomics Analyses of Schwannomas in the Head and Neck: A Preliminary Analysis
The purpose of this preliminary study was to evaluate the differences in Magnetic Resonance Imaging (MRI)-based radiomics analysis between cerebellopontine angle neurinomas and schwannomas originating from other locations in the neck spaces. Twenty-six patients with available MRI exams and head and neck schwannomas were included. Lesions were manually segmented on the precontrast and postcontrast T1 sequences. The radiomics features were extracted by using PyRadiomics software, and a total of 120 radiomics features were obtained from each segmented tumor volume. An operator-independent hybrid descriptive‐inferential method was adopted for the selection and reduction of the features, while d…
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-…
Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies
Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …
Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT.
Background: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. Methods: We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis…
An extended catalogue of ncRNAs in Streptomyces coelicolor reporting abundant tmRNA, RNase-P RNA and RNA fragments derived from pre-ribosomal RNA leader sequences
Streptomyces coelicolor is a model organism for studying streptomycetes. This genus possesses relevant medical and economical roles, because it produces many biologically active metabolites of pharmaceutical interest, including the majority of commercialized antibiotics. In this bioinformatic study, the transcriptome of S. coelicolor has been analyzed to identify novel RNA species and quantify the expression of both annotated and novel transcripts in solid and liquid growth medium cultures at different times. The major characteristics disclosed in this study are: (i) the diffuse antisense transcription; (ii) the great abundance of transfer-messenger RNAs (tmRNA); (iii) the abundance of rnpB…
A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard te…
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
Featured Application Based on results defined in this study, new investigations might propose morpho-functional-based radiomics algorithms for risk stratification with possible impact on treatment management in colorectal cancer. The aim of this study was to investigate the application of [F-18]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [F-18]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded f…
Deep learning approach for the segmentation of aneurysmal ascending aorta.
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimic…