Cargando…

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

PURPOSE: Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis. METHODS: This retrospective study reviewed the clinicopathological and imaging data of lung c...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Jingting, Zhong, Feiyang, Xiao, Feng, Dong, Xinyang, Long, Yun, Gan, Tian, Li, Ting, Liao, Meiyan
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Oncology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069670/ https://www.ncbi.nlm.nih.gov/pubmed/37020864 http://dx.doi.org/10.3389/fonc.2023.1157891

Ejemplares similares

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions
por: Zhong, Feiyang, et al.
Publicado: (2022)

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach
por: Chen, Bihong T., et al.
Publicado: (2020)

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
por: Xu, Fangyi, et al.
Publicado: (2020)

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma
por: Zhang, Baihua, et al.
Publicado: (2021)

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features
por: Yi, Li, et al.
Publicado: (2022)

Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features
por: Wang, Zhe, et al.
Publicado: (2023)

Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma
por: Xu, Yunyu, et al.
Publicado: (2021)

Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
por: Xu, Fangyi, et al.
Publicado: (2020)

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients
por: Song, Lan, et al.
Publicado: (2020)

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features
por: Wu, Linyu, et al.
Publicado: (2020)

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan
por: Xia, Xianwu, et al.
Publicado: (2020)

Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images
por: Qi, Lin, et al.
Publicado: (2021)

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma
por: Han, Xiaoyu, et al.
Publicado: (2022)

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules
por: Zheng, Yuting, et al.
Publicado: (2023)

Diagnosis of Serosal Invasion in Gastric Adenocarcinoma by Dual-Energy CT Radiomics: Focusing on Localized Gastric Wall and Peritumoral Radiomics Features
por: Yang, Li, et al.
Publicado: (2022)

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics
por: Tan, Mingyu, et al.
Publicado: (2021)

Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer
por: Li, Huanhuan, et al.
Publicado: (2021)

Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma
por: Huo, Ji-wen, et al.
Publicado: (2022)

A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma
por: Zha, Xinyi, et al.
Publicado: (2022)

Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan
por: Qiu, Lu, et al.
Publicado: (2022)

CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma
por: Zhang, Junjie, et al.
Publicado: (2023)

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma
por: Chang, Cheng, et al.
Publicado: (2021)

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients
por: Hong, Duo, et al.
Publicado: (2021)

Clinicopathological and CT features of tumor spread through air space in invasive lung adenocarcinoma
por: Qin, Lili, et al.
Publicado: (2022)

Combination of clinical, radiomic, and “delta” radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma
por: Krishna, Satheesh, et al.
Publicado: (2023)

Value of (18)F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations
por: Yang, Bin, et al.
Publicado: (2020)

Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma
por: Hong, Duo, et al.
Publicado: (2020)

Habitat Imaging-Based (18)F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases
por: Chen, Ling, et al.
Publicado: (2021)

Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma
por: Lv, Jieqin, et al.
Publicado: (2022)

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung
por: Chen, Zhiyong, et al.
Publicado: (2022)

Immunohistochemistry and Radiomic Features for Survival Prediction in Small Cell Lung Cancer
por: Gkika, Eleni, et al.
Publicado: (2020)

Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules
por: Shi, Wenjia, et al.
Publicado: (2022)

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules
por: Chen, Chengyu, et al.
Publicado: (2023)

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma
por: Li, Qi, et al.
Publicado: (2021)

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors
por: Shao, Meihua, et al.
Publicado: (2021)

Small peripheral lung adenocarcinoma: CT and histopathologic characteristics and prognostic implications
por: Chu, Zhi-gang, et al.
Publicado: (2011)

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions
por: Deng, Yan, et al.
Publicado: (2021)

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma
por: Liu, Guixue, et al.
Publicado: (2021)

CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study
por: Fu, Qing, et al.
Publicado: (2021)

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules
por: Huang, Wenjun, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_p87s1a73mqbjh1t19mq5svsnn5