Purpose: Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1.
Methods: A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis.
Results: Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively.
Conclusions: The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.
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http://dx.doi.org/10.1016/j.ejrad.2021.110093 | DOI Listing |
J Inflamm Res
January 2025
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, People's Republic of China.
Background: Accurately assessing the activity of Crohn's disease (CD) is crucial for determining prognosis and guiding treatment strategies for CD patients.
Objective: This study aimed to develop and validate a nomogram for assessing CD activity.
Methods: The semi-automatic segmentation method and PyRadiomics software were employed to segment and extract radiomics features from the spectral CT enterography images of lesions in 107 CD patients.
J Immunother Cancer
January 2025
Vall d'Hebron Institute of Oncology, Barcelona, Spain.
Background: The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Purpose: HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Vesical Imaging-Reporting and Data System (VI-RADS) score for noninvasive identification of HER2 status in bladder urothelial carcinoma (BUC).
Methods: A total of 197 patients were retrospectively enrolled and randomly divided into a training cohort (n = 145) and a testing cohort (n = 52).
Acad Radiol
January 2025
Imaging Center, Harbin Medical University Cancer Hospital, Haping Road No.150, Nangang District, Harbin 150081, China (Q-X.C., L-Q.Z., X-Y.W., H-X.Z., J-J.L., M-C.X., H-Y.S., Z-X.K.). Electronic address:
Rationale And Objectives: To propose a novel MRI-based hyper-fused radiomic approach to predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC).
Materials And Methods: Pretreatment dynamic contrast-enhanced (DCE) MRI and ultra-multi-b-value (UMB) diffusion-weighted imaging (DWI) data were acquired in BC patients who received NAT followed by surgery at two centers. Hyper-fused radiomic features (RFs) and conventional RFs were extracted from DCE-MRI or UMB-DWI.
Med Phys
January 2025
Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P. R. China.
Background: This study aims to explore the value of habitat-based magnetic resonance imaging (MRI) radiomics for predicting the origin of brain metastasis (BM).
Purpose: To investigate whether habitat-based radiomics can identify the metastatic tumor type of BM and whether an imaging-based model that integrates the volume of peritumoral edema (VPE) can enhance predictive performance.
Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions.
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