Purpose: The purpose was to assess the predictive ability of computed tomography (CT)-based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC).
Materials And Methods: Eighty-one patients (63.6 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase ( = 396) and portal venous phase ( = 396) for each case. Significantly different features were selected using Mann-Whitney test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method.
Results: Seven radiomics features from late arterial phase images and three from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC, with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using the LGOCV method, with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV.
Conclusion: CT-based radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.
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http://dx.doi.org/10.3389/fonc.2020.01618 | DOI Listing |
Acad Radiol
January 2025
Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China (X.W., C.C., W.C., Y.G., X.L., X.J.); Department of Pathology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou 325000, China (X.W., J.W., C.C., W.C., Y.G., K.G., Y.C., Y.S., J.C., X.L., X.J.). Electronic address:
Rationale And Objectives: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in patients with LARC.
Materials And Methods: We retrospectively enrolled 768 eligible participants from three independent hospitals who had received neoadjuvant chemoradiotherapy followed by radical surgery.
Eur J Radiol
January 2025
Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China. Electronic address:
Objective: To assess the efficacy of computed tomography (CT)-based radiomics nomogram in predicting perineural invasion (PNI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC).
Materials And Methods: Overall, 146 patients were retrospectively recruited and divided into training and test cohorts at a 7:3 ratio. Radiomics features were extracted and delta and absolute delta radiomics features were calculated.
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).
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