The effects of volume of interest delineation on MRI-based radiomics analysis : evaluation with two disease groups
BACKGROUND: Manual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer.
METHODS: This study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC).
RESULTS: The larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p > 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725, p = 0.001; dilation: 0.800 vs. 0.725, p = 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations.
CONCLUSIONS: Differences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
Cancer imaging : the official publication of the International Cancer Imaging Society - 19(2019), 1 vom: 21. Dez., Seite 89 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Xiao [VerfasserIn] |
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| Links: |
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| Themen: |
Breast cancer |
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| Anmerkungen: |
Date Completed 12.03.2020 Date Revised 12.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s40644-019-0276-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM304648930 |
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| 100 | 1 | |a Zhang, Xiao |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The effects of volume of interest delineation on MRI-based radiomics analysis |b evaluation with two disease groups |
| 264 | 1 | |c 2019 | |
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| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Manual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer | ||
| 520 | |a METHODS: This study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC) | ||
| 520 | |a RESULTS: The larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p > 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725, p = 0.001; dilation: 0.800 vs. 0.725, p = 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations | ||
| 520 | |a CONCLUSIONS: Differences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard | ||
| 650 | 4 | |a Evaluation Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Nasopharyngeal carcinoma | |
| 650 | 4 | |a Preoperative prediction | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Segmentation | |
| 700 | 1 | |a Zhong, Liming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Haiyan |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Lijun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuixing |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Qianjin |e verfasserin |4 aut | |
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