PMID- 36330195
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221105
IS  - 2223-4292 (Print)
IS  - 2223-4306 (Electronic)
IS  - 2223-4306 (Linking)
VI  - 12
IP  - 11
DP  - 2022 Nov
TI  - Differentiation of predominantly osteolytic from osteoblastic spinal metastases 
      based on standard magnetic resonance imaging sequences: a comparison of radiomics 
      model versus semantic features logistic regression model findings.
PG  - 5004-5017
LID - 10.21037/qims-22-267 [doi]
AB  - BACKGROUND: The aim of this study was to compare the ability of a standard 
      magnetic resonance imaging (MRI)-based radiomics model and a semantic features 
      logistic regression model in differentiating between predominantly osteolytic and 
      osteoblastic spinal metastases. METHODS: We retrospectively analyzed standard 
      MRIs and computed tomography (CT) images of 78 lesions of spinal metastases, of 
      which 52 and 26 were predominantly osteolytic and osteoblastic, respectively. CT 
      images were used as references for determining the sensitivity and specificity of 
      standard MRI. Five standard MRI semantic features of each lesion were evaluated 
      and used for constructing a logistic regression model to differentiate between 
      predominantly osteolytic and osteoblastic metastases. For each lesion, 107 
      radiomics features were extracted. Six features were selected using a support 
      vector machine (SVM) and were used for constructing classi fi cation models. Model 
      performance was measured by means of the area under the curve (AUC) approach and 
      compared using receiver operating characteristics (ROC) curve analysis. RESULTS: 
      The signal intensity on T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed 
      T2-weighted (FS-T2W) MRI sequences were significantly different between 
      predominantly osteolytic and osteoblastic spinal metastases (P<0.001), as is the 
      case with the existence of soft-tissue masses. The overall prediction accuracy of 
      the models based on radiomics and semantic features was 78.2% and 75.6%, 
      respectively, with corresponding AUCs of 0.82 and 0.79, respectively. 
      CONCLUSIONS: The standard MRI-based radiomics model outperformed the semantic 
      features logistic regression model with regard to differentiating predominantly 
      osteolytic and osteoblastic spinal metastases.
CI  - 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
FAU - Liu, Ke
AU  - Liu K
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Zhang, Yang
AU  - Zhang Y
AD  - Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, 
      Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
AD  - Department of Radiological Sciences, University of California, Irvine, CA, USA.
FAU - Wang, Qizheng
AU  - Wang Q
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Chen, Yongye
AU  - Chen Y
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Qin, Siyuan
AU  - Qin S
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Xin, Peijin
AU  - Xin P
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Zhao, Weili
AU  - Zhao W
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
FAU - Zhang, Enlong
AU  - Zhang E
AD  - Department of Radiology, Peking University International Hospital, Beijing, 
      China.
FAU - Nie, Ke
AU  - Nie K
AD  - Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, 
      Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
FAU - Lang, Ning
AU  - Lang N
AD  - Department of Radiology, Peking University Third Hospital, Beijing, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Quant Imaging Med Surg
JT  - Quantitative imaging in medicine and surgery
JID - 101577942
PMC - PMC9622449
OTO - NOTNLM
OT  - Spinal metastases
OT  - differential
OT  - radiomics
OT  - semantics
OT  - standard magnetic resonance imaging (MRI)
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://qims.amegroups.com/article/view/10.21037/qims-22-267/coif). The authors 
      have no conflicts of interest to declare.
EDAT- 2022/11/05 06:00
MHDA- 2022/11/05 06:01
PMCR- 2022/11/01
CRDT- 2022/11/04 02:29
PHST- 2022/03/21 00:00 [received]
PHST- 2022/08/01 00:00 [accepted]
PHST- 2022/11/04 02:29 [entrez]
PHST- 2022/11/05 06:00 [pubmed]
PHST- 2022/11/05 06:01 [medline]
PHST- 2022/11/01 00:00 [pmc-release]
AID - qims-12-11-5004 [pii]
AID - 10.21037/qims-22-267 [doi]
PST - ppublish
SO  - Quant Imaging Med Surg. 2022 Nov;12(11):5004-5017. doi: 10.21037/qims-22-267.