Cargando…

(18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer

OBJECTIVE: To explore a prediction model for lymphovascular invasion (LVI) on cT(1–2)N(0)M(0) radiologic solid non-small cell lung cancer (NSCLC) based on a 2-deoxy-2[(18)F]fluoro-D-glucose ([(18)F]F-FDG) positron emission tomography-computed tomography (PET-CT) radiomics analysis. METHODS: The pres...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Jie, Zheng, Zhonghang, Zhang, Yi, Tan, Weiyue, Li, Jing, Xing, Ligang, Sun, Xiaorong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401837/
https://www.ncbi.nlm.nih.gov/pubmed/37546415
http://dx.doi.org/10.3389/fonc.2023.1185808
_version_ 1785084752451076096
author Wang, Jie
Zheng, Zhonghang
Zhang, Yi
Tan, Weiyue
Li, Jing
Xing, Ligang
Sun, Xiaorong
author_facet Wang, Jie
Zheng, Zhonghang
Zhang, Yi
Tan, Weiyue
Li, Jing
Xing, Ligang
Sun, Xiaorong
author_sort Wang, Jie
collection PubMed
description OBJECTIVE: To explore a prediction model for lymphovascular invasion (LVI) on cT(1–2)N(0)M(0) radiologic solid non-small cell lung cancer (NSCLC) based on a 2-deoxy-2[(18)F]fluoro-D-glucose ([(18)F]F-FDG) positron emission tomography-computed tomography (PET-CT) radiomics analysis. METHODS: The present work retrospectively included 148 patients receiving surgical resection and verified pathologically with cT(1–2)N(0)M(0) radiologic solid NSCLC. The cases were randomized into training or validation sets in the ratio of 7:3. PET and CT images were used to select optimal radiomics features. Three radiomics predictive models incorporating CT, PET, as well as PET/CT images radiomics features (CT-RS, PET-RS, PET/CT-RS) were developed using logistic analyses. Furthermore, model performance was evaluated by ROC analysis for predicting LVI status. Model performance was evaluated in terms of discrimination, calibration along with clinical utility. Kaplan-Meier curves were employed to analyze the outcome of LVI. RESULTS: The ROC analysis demonstrated that PET/CT-RS (AUCs were 0.773 and 0.774 for training and validation sets) outperformed both CT-RS(AUCs, 0.727 and 0.752) and PET-RS(AUCs, 0.715 and 0.733). A PET/CT radiology nomogram (PET/CT-model) was developed to estimate LVI; the model demonstrated conspicuous prediction performance for training (C-index, 0.766; 95%CI, 0.728–0.805) and validation sets (C-index, 0.774; 95%CI, 0.702–0.846). Besides, decision curve analysis and calibration curve showed that PET/CT-model provided clinically beneficial effects. Disease-free survival and overall survival varied significantly between LVI and non-LVI cases (P<0.001). CONCLUSIONS: The PET/CT radiomics models could effectively predict LVI on early stage radiologic solid lung cancer and provide support for clinical treatment decisions.
format Online
Article
Text
id pubmed-10401837
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-104018372023-08-05 (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer Wang, Jie Zheng, Zhonghang Zhang, Yi Tan, Weiyue Li, Jing Xing, Ligang Sun, Xiaorong Front Oncol Oncology OBJECTIVE: To explore a prediction model for lymphovascular invasion (LVI) on cT(1–2)N(0)M(0) radiologic solid non-small cell lung cancer (NSCLC) based on a 2-deoxy-2[(18)F]fluoro-D-glucose ([(18)F]F-FDG) positron emission tomography-computed tomography (PET-CT) radiomics analysis. METHODS: The present work retrospectively included 148 patients receiving surgical resection and verified pathologically with cT(1–2)N(0)M(0) radiologic solid NSCLC. The cases were randomized into training or validation sets in the ratio of 7:3. PET and CT images were used to select optimal radiomics features. Three radiomics predictive models incorporating CT, PET, as well as PET/CT images radiomics features (CT-RS, PET-RS, PET/CT-RS) were developed using logistic analyses. Furthermore, model performance was evaluated by ROC analysis for predicting LVI status. Model performance was evaluated in terms of discrimination, calibration along with clinical utility. Kaplan-Meier curves were employed to analyze the outcome of LVI. RESULTS: The ROC analysis demonstrated that PET/CT-RS (AUCs were 0.773 and 0.774 for training and validation sets) outperformed both CT-RS(AUCs, 0.727 and 0.752) and PET-RS(AUCs, 0.715 and 0.733). A PET/CT radiology nomogram (PET/CT-model) was developed to estimate LVI; the model demonstrated conspicuous prediction performance for training (C-index, 0.766; 95%CI, 0.728–0.805) and validation sets (C-index, 0.774; 95%CI, 0.702–0.846). Besides, decision curve analysis and calibration curve showed that PET/CT-model provided clinically beneficial effects. Disease-free survival and overall survival varied significantly between LVI and non-LVI cases (P<0.001). CONCLUSIONS: The PET/CT radiomics models could effectively predict LVI on early stage radiologic solid lung cancer and provide support for clinical treatment decisions. Frontiers Media S.A. 2023-07-21 /pmc/articles/PMC10401837/ /pubmed/37546415 http://dx.doi.org/10.3389/fonc.2023.1185808 Text en Copyright © 2023 Wang, Zheng, Zhang, Tan, Li, Xing and Sun https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wang, Jie
Zheng, Zhonghang
Zhang, Yi
Tan, Weiyue
Li, Jing
Xing, Ligang
Sun, Xiaorong
(18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title_full (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title_fullStr (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title_full_unstemmed (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title_short (18)F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
title_sort (18)f-fdg pet/ct radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401837/
https://www.ncbi.nlm.nih.gov/pubmed/37546415
http://dx.doi.org/10.3389/fonc.2023.1185808
work_keys_str_mv AT wangjie 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT zhengzhonghang 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT zhangyi 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT tanweiyue 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT lijing 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT xingligang 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer
AT sunxiaorong 18ffdgpetctradiomicsforpredictionoflymphovascularinvasioninpatientswithearlystagenonsmallcelllungcancer