Radiomics

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts...

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package...

AIM: To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases. MATERIALS AND METHODS: 117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1...

OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. METHODS: This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase...

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials...

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18 F-FDG PET images...

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted...

[This corrects the article DOI: 10.3389/fonc.2022.934291.].

OBJECTIVES: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. MATERIALS AND METHODS: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign)...

Introduction: Immune checkpoint inhibitors (ICIs) represent the standard therapy for metastatic melanoma. However, a few patients do not respond to ICIs and reliable predictive biomarkers are needed. Methods: This pilot study investigates the association between mRNA levels of programmed cell death-1 (PD-1) ligand 1 (PD-L1), interferon-gamma (IFN-γ), and transforming growth factor-β (TGF-β) in circulating extracellular vesicles (EVs) and survival in 30 patients with metastatic melanoma treated with first line anti-PD-1 antibodies...

OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls...

OBJECTIVES: To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. MATERIALS AND METHODS: A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2...

Cardiac magnetic resonance cine images are primarily used to evaluate functional consequences, whereas limited information is extracted from the noncontrast pixel-wise myocardial signal intensity pattern. In this study we want to assess whether characterizing this inherent contrast pattern of noncontrast-enhanced short axis (SAX) cine images via radiomics is sufficient to distinguish subjects with acute myocardial infarction (AMI) from controls. Cine balanced steady-state free-precession images acquired at 1...

OBJECTIVE: This study aims to evaluate the diagnostic accuracy of ultrasound imaging (US)-based radiomics for the early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS: We comprehensively searched PubMed, Cochrane Library, Embase, and Web of Science databases up to 1 January 2023 for eligible studies. We assessed the methodological quality of the enrolled studies with Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 tools...

OBJECTIVES: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data-driven approach for bone quality classification. MATERIALS AND METHODS: Twenty-six expert clinicians were asked to classify 110 CBCT cross-sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1)...

BACKGROUND: Gliomas, as Central Nervous System (CNS) tumors, are greatly common with 80% of malignancy. Treatment methods for gliomas, such as surgery, radiation therapy, and chemotherapy depend on the grade, size, location, and the patient's age. OBJECTIVE: This study aimed to quantify glioma based on the radiomics analysis and classify its grade into High-grade Glioma (HGG) or Low-grade Glioma (LGG) by various machine-learning methods using contrast-enhanced brain Computerized Tomography (CT) scans...

BACKGROUND: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal...

BACKGROUND: Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection...

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes...

BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS: Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57)...