Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans : a secondary analysis of lung nodule datasets
© 2024. The Author(s)..
BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images.
METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules.
RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%.
CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Cancer imaging : the official publication of the International Cancer Imaging Society - 24(2024), 1 vom: 20. März, Seite 40 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lin, Chia-Ying [VerfasserIn] |
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| Links: |
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| Themen: |
3D region proposal network |
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| Anmerkungen: |
Date Completed 22.03.2024 Date Revised 23.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s40644-024-00683-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369992180 |
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| 100 | 1 | |a Lin, Chia-Ying |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans |b a secondary analysis of lung nodule datasets |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 23.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images | ||
| 520 | |a METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules | ||
| 520 | |a RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5% | ||
| 520 | |a CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Computer-aided detection system | |
| 650 | 4 | |a Ground glass nodules | |
| 650 | 4 | |a LUNA16 dataset | |
| 700 | 1 | |a Guo, Shu-Mei |e verfasserin |4 aut | |
| 700 | 1 | |a Lien, Jenn-Jier James |e verfasserin |4 aut | |
| 700 | 1 | |a Tsai, Tzung-Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yi-Sheng |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Chao-Han |e verfasserin |4 aut | |
| 700 | 1 | |a Hsu, I-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Chao-Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Tseng, Yau-Lin |e verfasserin |4 aut | |
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