Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients.
Purpose: To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). Methods: This retrospective study included 50 cervi...
| Publicado en: | Abdominal Radiology Vol. 47; no. 2; pp. 838 - 848 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
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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=154994618&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154994618 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2022 vid: 47 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154994618 153775624 154994618 154994618 10.1007/s00261-021-03350-y 154994618 ppf: 838 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients. aug: au: Nakajo, Masatoyo Jinguji, Megumi Tani, Atsushi Yano, Erina Hoo, Chin Khang Hirahara, Daisuke Togami, Shinichi Kobayashi, Hiroaki Yoshiura, Takashi affil: Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, 890-8544, Kagoshima, Japan sug: subj: Machine Learning Positron-Emission Tomography Tomography, X-Ray Computed Cervix Neoplasms Prognosis Fludeoxyglucose F 18 Diagnostic Use Cervix Neoplasms Radiography Human Retrospective Design Cancer Patients Disease Progression Risk Factors Algorithms ROC Curve Progression-Free Survival Cox Proportional Hazards Model Regression Neoplasm Staging Tumor Burden Descriptive Statistics Multivariate Analysis Confidence Intervals Radiographic Image Enhancement ab: Purpose: To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). Methods: This retrospective study included 50 cervical cancer patients who underwent 18F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 18F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. Results: The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p < 0.001) and was only the independent factor for PFS in multivariate analysis (HR, 6.89; 95% CI, 1.92–24.69; p = 0.003). Conclusion: A machine learning approach based on clinical and pretreatment 18F-FDG PET-based radiomic features may be useful for predicting tumor progression in cervical cancer patients. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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