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Adrenal indeterminate nodules: CT-based radiomics analysis of different machine learning models for predicting adrenal metastases in lung cancer patients.
Front Oncol
November 2024
Department of Oncology Surgery, Tangshan People's Hospital, Tangshan, Hebei, China.
Article Synopsis
- The study aimed to compare various machine learning algorithms to differentiate between benign tumors and adrenal metastases in lung cancer patients using CT imaging data.
- It involved a total of 292 lung cancer patients, where radiomics features were extracted from CT scans and analyzed using techniques like SVM, random forest, decision tree, and logistic regression.
- Results revealed that the combined radiomics model had the highest predictive accuracy (AUC=0.938) for identifying adrenal metastases, outperforming other models and demonstrating better clinical utility.
Radiomics Nomogram Based on Optimal Volume of Interest Derived from High-Resolution CT for Preoperative Prediction of IASLC Grading in Clinical IA Lung Adenocarcinomas: A Multi-Center, Large-Population Study.
Technol Cancer Res Treat
November 2024
College of Mathematical Medicine, Zhejiang Normal University, Jinhua, P. R. China.
The novel grading system developed by the International Association for the Study of Lung Cancer (IASLC) for clinical stage IA lung adenocarcinomas has demonstrated remarkable prognostic capabilities. Notably, tumors classified as grade 3 have been associated with poor prognostic outcomes, thereby playing a crucial role in the formulation of personalized surgical strategies. The objective of this study is to develop a radiomics nomogram that utilizes the optimal volume of interest (VOI) derived from high-resolution CT (HRCT) scans to accurately predict the presence of grade 3 tumors in patients with clinical IA lung adenocarcinomas.
View Article and Find Full Text PDFA Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma.
J Hepatocell Carcinoma
October 2024
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People's Republic of China.
Article Synopsis
- Hepatocellular carcinoma (HCC) is a common and aggressive liver cancer, and this study investigates the relationship between gluconeogenesis-related genes (GRGs) and HCC to assess their influence on tumor progression and treatment response.
- Researchers used data from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) to create a predictive model based on the expression of GRGs, applying advanced statistical methods to validate their findings.
- The study successfully developed a four-gene prognostic model that categorizes HCC patients into risk groups and identifies differences in immune responses and drug sensitivities, ultimately aiding in personalized treatment approaches for HCC patients.
Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma.
Int J Gen Med
September 2024
Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People's Republic of China.
Purpose: The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model.
Methods: In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA).
T2-FLAIR imaging-based radiomic features for predicting early postoperative recurrence of grade II gliomas.
Future Oncol
November 2024
Department of Oncology, Qiandongnan Hospital affiliated to Guizhou Medical University (People's Hospital of Qiandongnan Miao & Dong Autonomous Prefecture), No. 31, Shaoshan South Road, Kaili, Guizhou Province, China.
To develop and validate a T2-weighted-fluid attenuated inversion recovery (T2-FLAIR) images-based radiomics model for predicting early postoperative recurrence (within 1 year) in patients with low-grade gliomas (LGGs). A retrospective analysis was performed by collecting clinical, pathological and magnetic resonance imaging (MRI) data from patients with LGG between 2017 and 2022. Regions of interest were delineated and radiomic features were extracted from T2-FLAIR images using 3D-Slicer software.
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