Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma
© 2023. BioMed Central Ltd., part of Springer Nature..
OBJECTIVES: This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis.
MATERIALS AND METHODS: A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models.
RESULTS: Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models.
CONCLUSIONS: In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.
| Errataetall: |
ErratumIn: BMC Med Imaging. 2023 Oct 13;23(1):157. - PMID 37833624 |
|---|---|
| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
|---|---|
| Enthalten in: |
BMC medical imaging - 23(2023), 1 vom: 02. Okt., Seite 147 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Liu, Ying [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Computed tomography |
|---|
| Anmerkungen: |
Date Completed 04.10.2023 Date Revised 19.11.2023 published: Electronic ErratumIn: BMC Med Imaging. 2023 Oct 13;23(1):157. - PMID 37833624 Citation Status MEDLINE |
|---|
| doi: |
10.1186/s12880-023-01077-4 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM362798516 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM362798516 | ||
| 003 | DE-627 | ||
| 005 | 20231226092007.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s12880-023-01077-4 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1209.xml |
| 035 | |a (DE-627)NLM362798516 | ||
| 035 | |a (NLM)37784073 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Liu, Ying |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 04.10.2023 | ||
| 500 | |a Date Revised 19.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a ErratumIn: BMC Med Imaging. 2023 Oct 13;23(1):157. - PMID 37833624 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. BioMed Central Ltd., part of Springer Nature. | ||
| 520 | |a OBJECTIVES: This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis | ||
| 520 | |a MATERIALS AND METHODS: A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models | ||
| 520 | |a RESULTS: Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models | ||
| 520 | |a CONCLUSIONS: In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Ewing sarcoma | |
| 650 | 4 | |a Pulmonary metastases | |
| 650 | 4 | |a Radiomics | |
| 700 | 1 | |a Yin, Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Jingjing |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Nan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhentao |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t BMC medical imaging |d 2001 |g 23(2023), 1 vom: 02. Okt., Seite 147 |w (DE-627)NLM116251670 |x 1471-2342 |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2023 |g number:1 |g day:02 |g month:10 |g pages:147 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1186/s12880-023-01077-4 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 23 |j 2023 |e 1 |b 02 |c 10 |h 147 | ||