Purpose: Aim of this study is to assess the ability of contrast-enhanced CT image-based radiomic analysis to predict local response (LR) in a retrospective cohort of patients affected by pancreatic cancer and treated with stereotactic body radiation therapy (SBRT). Secondary aim is to evaluate progression free survival (PFS) and overall survival (OS) at long-term follow-up.
Methods: Contrast-enhanced-CT images of 37 patients who underwent SBRT were analyzed. Two clinical variables (BED, CTV volume), 27 radiomic features were included. LR was used as the outcome variable to build the predictive model. The Kaplan-Meier method was used to evaluate PFS and OS.
Results: Three variables were statistically correlated with the LR in the univariate analysis: Intensity Histogram (StdValue feature), Gray Level Cooccurrence Matrix (GLCM25_Correlation feature) and Neighbor Intensity Difference (NID25_Busyness feature). Multivariate model showed GLCM25_Correlation (P = 0.007) and NID25_Busyness (P = 0.03) as 2 independent predictive variables for LR. The odds ratio values of GLCM25_Correlation and NID25_Busyness were 0.07 (95%CI 0.01-0.49) and 8.10 (95%CI 1.20-54.40), respectively. The area under the curve for the multivariate logistic regressive model was 0.851 (95%CI 0.724-0.978). At a median follow-up of 30 months, median PFS was 7 months (95%CI 6-NA); median OS was 11 months (95%CI 10-22 months).
Conclusions: This analysis identified a radiomic signature that correlates with LR. To confirm these results, prospective studies could identify patient sub-groups with different rates of radiation dose-response to define a more personalized SBRT approach.
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http://dx.doi.org/10.1007/s11547-021-01422-z | DOI Listing |
Sci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFTo establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected.
View Article and Find Full Text PDFInt J Med Inform
January 2025
School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, PR China. Electronic address:
Background: In the context of routine breast cancer diagnosis, the precise discrimination between benign and malignant breast masses holds utmost significance. Notably, few prior investigations have concurrently explored the integration of imaging histology features, deep learning characteristics, and clinical parameters. The primary objective of this retrospective study was to pioneer a multimodal feature fusion model tailored for the prediction of breast tumor malignancy, harnessing the potential of ultrasound images.
View Article and Find Full Text PDFObjective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Abdom Radiol (NY)
January 2025
Department of Radiology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
Objectives: To develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).
Methods: A retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50).
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