Search Our Scientific Publications & Authors

Publications
find publications by category +
Translate page:

Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography.

Authors:
Takehiro Shiinoki Koya Fujimoto Yusuke Kawazoe Yuki Yuasa Miki Kajima Yuki Manabe Taiki Ono Tsunahiko Hirano Kazuto Matsunaga Hidekazu Tanaka

Biomed Phys Eng Express 2022 02 1;8(2). Epub 2022 Feb 1.

Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, 1-1-1 Minamikogushi, Ube, Yamaguchi 755-8505, Japan.

In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomic features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels≥50% or≥1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-L1 expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.

Download full-text PDF

 Source
http://dx.doi.org/10.1088/2057-1976/ac4d43DOI Listing
February 2022

Publication Analysis

Top Keywords

radiomic features
32
intratumoral peritumoral
20
features intratumoral
20
expression level
16
peritumoral regions
12
pd-l1 expression
12
features
10
intratumoral
8
selected features
8
lung cancer
8
cell lung
8
non-small cell
8
intratumoral radiomic
8
prediction models
8
computed tomography
8
training test
8
test cohorts
8
calculated training
8
programmed death-ligand
8
yielded auc
8

Keyword Occurance

Similar Publications

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

Authors:
Chaya S Moskowitz Mattea L Welch Michael A Jacobs Brenda F Kurland Amber L Simpson

Radiology 2022 May 17:211597. Epub 2022 May 17.

From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.).

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Read More

View Article and Full-Text PDF
May 2022
Similar Publications

Can Radiomics Provide Additional Diagnostic Value for Identifying Adrenal Lipid-Poor Adenomas From Non-Adenomas on Unenhanced CT?

Authors:
Binhao Zhang Huangqi Zhang Xin Li Shengze Jin Jiawen Yang Wenting Pan Xue Dong Jin Chen Wenbin Ji

Front Oncol 2022 29;12:888778. Epub 2022 Apr 29.

Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, China.

Background: It is difficult for radiologists to differentiate adrenal lipid-poor adenomas from non-adenomas; nevertheless, this differentiation is important as the clinical interventions required are different for adrenal lipid-poor adenomas and non-adenomas.

Purpose: To develop an unenhanced computed tomography (CT)-based radiomics model for identifying adrenal lipid-poor adenomas to assist in clinical decision-making.

Materials And Methods: Patients with adrenal lesions who underwent CT between January 2015 and August 2021 were retrospectively recruited from two independent institutions. Read More

View Article and Full-Text PDF
April 2022
Similar Publications

Radiomics Study for Differentiating Focal Hepatic Lesions Based on Unenhanced CT Images.

Authors:
Xitong Zhao Pan Liang Liuliang Yong Yan Jia Jianbo Gao

Front Oncol 2022 27;12:650797. Epub 2022 Apr 27.

Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Objectives: To investigate the feasibility of computer-aided discriminative diagnosis among hepatocellular carcinoma (HCC), hepatic metastasis, hepatic hemangioma, hepatic cysts, hepatic adenoma, and hepatic focal nodular hyperplasia, based on radiomics analysis of unenhanced CT images.

Methods: 452 patients with 77 with HCC, 104 with hepatic metastases, 126 with hepatic hemangioma, 99 with hepatic cysts, 24 with FNH, 22 with HA, who underwent CT examination from 2016 to 2018, were included. Radcloud Platform was used to extract radiomics features from manual delineation on unenhanced CT images. Read More

View Article and Full-Text PDF
April 2022
Similar Publications

Association between cine CMR-based radiomics signature and microvascular obstruction in patients with ST-segment elevation myocardial infarction.

Authors:
Yongjia Peng Kongyang Wu Yi Xiang J Wang Jingshan Gong

J Thorac Dis 2022 Apr;14(4):969-978

Department of Radiology, Shenzhen People's Hospital (The Second Clinical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China.

Background: In the process of percutaneous coronary intervention (PCI), patients with ST-segment elevation myocardial infarction (STEMI) may receive large doses of the iodine contrast agent. Some adverse events may be aroused if the patients receive the gadolinium agents. We investigate the association between cine cardiac magnetic resonance (CMR)-based radiomics signature and microvascular obstruction (MVO) in patients with STEMI. Read More

View Article and Full-Text PDF
April 2022
Similar Publications

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study.

Authors:
Haipeng Tong Jinju Sun Jingqin Fang Mi Zhang Huan Liu Renxiang Xia Weicheng Zhou Kaijun Liu Xiao Chen

Front Immunol 2022 29;13:859323. Epub 2022 Apr 29.

Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.

Background: The tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual's TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using F-FDG PET/CT radiomics and clinical characteristics. Read More

View Article and Full-Text PDF
April 2022
Similar Publications
}