Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer.
The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall surviva...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 911 - 923 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473104&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473104 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473104 161582330 164473104 164473104 10.1007/s10278-023-00778-0 164473104 ppf: 911 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. aug: au: Le, Viet Huan Kha, Quang Hien Minh, Tran Nguyen Tuan Nguyen, Van Hiep Le, Van Long Le, Nguyen Quoc Khanh affil: International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, 110, Taipei, Taiwan sug: subj: Tomography, X-Ray Computed Neoplasms Prognosis Prediction Models Tumor Markers, Biological Neoplasm Metastasis Head and Neck Neoplasms Prognosis Kidney Neoplasms Prognosis Lung Neoplasms Prognosis Multiple Organ Dysfunction Syndrome Human Validation Studies Retrospective Design Algorithms Univariate Statistics Cox Proportional Hazards Model ROC Curve Kaplan-Meier Estimator Log-Rank Test Descriptive Statistics Confidence Intervals Funding Source ab: The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan–Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan–Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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