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DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm

The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning an...

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Autores principales: Beck, Kyongmin Sarah, Gil, Bomi, Na, Sae Jung, Hong, Ji Hyung, Chun, Sang Hoon, An, Ho Jung, Kim, Jae Jun, Hong, Soon Auck, Lee, Bora, Shim, Won Sang, Park, Sungsoo, Ko, Yoon Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8287408/
https://www.ncbi.nlm.nih.gov/pubmed/34290979
http://dx.doi.org/10.3389/fonc.2021.661244
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author Beck, Kyongmin Sarah
Gil, Bomi
Na, Sae Jung
Hong, Ji Hyung
Chun, Sang Hoon
An, Ho Jung
Kim, Jae Jun
Hong, Soon Auck
Lee, Bora
Shim, Won Sang
Park, Sungsoo
Ko, Yoon Ho
author_facet Beck, Kyongmin Sarah
Gil, Bomi
Na, Sae Jung
Hong, Ji Hyung
Chun, Sang Hoon
An, Ho Jung
Kim, Jae Jun
Hong, Soon Auck
Lee, Bora
Shim, Won Sang
Park, Sungsoo
Ko, Yoon Ho
author_sort Beck, Kyongmin Sarah
collection PubMed
description The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning and 3D Convolutional Neural Network (CNN) to predict LVI or pathological nodal involvement on chest CT images. A total of 695 preoperative CT images of resected NSCLC with tumor size of less than or equal to 3 cm from 2008 to 2015 were used to train and validate the DeepCUBIT model using five-fold cross-validation method. We also used tumor size and consolidation to tumor ratio (C/T ratio) to build a support vector machine (SVM) classifier. Two-hundred and fifty-four out of 695 samples (36.5%) had LVI or nodal involvement. An integrated model (3D CNN + Tumor size + C/T ratio) showed sensitivity of 31.8%, specificity of 89.8%, accuracy of 76.4%, and AUC of 0.759 on external validation cohort. Three single SVM models, using 3D CNN (DeepCUBIT), tumor size or C/T ratio, showed AUCs of 0.717, 0.630 and 0.683, respectively on external validation cohort. DeepCUBIT showed the best single model compared to the models using only C/T ratio or tumor size. In addition, the DeepCUBIT model could significantly identify the prognosis of resected NSCLC patients even in stage I. DeepCUBIT using transfer learning and 3D CNN can accurately predict LVI or nodal involvement in cT1 size NSCLC on CT images. Thus, it can provide a more accurate selection of candidates who will benefit from limited surgery without increasing the risk of recurrence.
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spelling pubmed-82874082021-07-20 DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm Beck, Kyongmin Sarah Gil, Bomi Na, Sae Jung Hong, Ji Hyung Chun, Sang Hoon An, Ho Jung Kim, Jae Jun Hong, Soon Auck Lee, Bora Shim, Won Sang Park, Sungsoo Ko, Yoon Ho Front Oncol Oncology The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning and 3D Convolutional Neural Network (CNN) to predict LVI or pathological nodal involvement on chest CT images. A total of 695 preoperative CT images of resected NSCLC with tumor size of less than or equal to 3 cm from 2008 to 2015 were used to train and validate the DeepCUBIT model using five-fold cross-validation method. We also used tumor size and consolidation to tumor ratio (C/T ratio) to build a support vector machine (SVM) classifier. Two-hundred and fifty-four out of 695 samples (36.5%) had LVI or nodal involvement. An integrated model (3D CNN + Tumor size + C/T ratio) showed sensitivity of 31.8%, specificity of 89.8%, accuracy of 76.4%, and AUC of 0.759 on external validation cohort. Three single SVM models, using 3D CNN (DeepCUBIT), tumor size or C/T ratio, showed AUCs of 0.717, 0.630 and 0.683, respectively on external validation cohort. DeepCUBIT showed the best single model compared to the models using only C/T ratio or tumor size. In addition, the DeepCUBIT model could significantly identify the prognosis of resected NSCLC patients even in stage I. DeepCUBIT using transfer learning and 3D CNN can accurately predict LVI or nodal involvement in cT1 size NSCLC on CT images. Thus, it can provide a more accurate selection of candidates who will benefit from limited surgery without increasing the risk of recurrence. Frontiers Media S.A. 2021-07-05 /pmc/articles/PMC8287408/ /pubmed/34290979 http://dx.doi.org/10.3389/fonc.2021.661244 Text en Copyright © 2021 Beck, Gil, Na, Hong, Chun, An, Kim, Hong, Lee, Shim, Park and Ko 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
Beck, Kyongmin Sarah
Gil, Bomi
Na, Sae Jung
Hong, Ji Hyung
Chun, Sang Hoon
An, Ho Jung
Kim, Jae Jun
Hong, Soon Auck
Lee, Bora
Shim, Won Sang
Park, Sungsoo
Ko, Yoon Ho
DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title_full DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title_fullStr DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title_full_unstemmed DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title_short DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm
title_sort deepcubit: predicting lymphovascular invasion or pathological lymph node involvement of clinical t1 stage non-small cell lung cancer on chest ct scan using deep cubical nodule transfer learning algorithm
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8287408/
https://www.ncbi.nlm.nih.gov/pubmed/34290979
http://dx.doi.org/10.3389/fonc.2021.661244
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