PMID- 34760700
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220428
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 11
DP  - 2021
TI  - Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the 
      Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors.
PG  - 752672
LID - 10.3389/fonc.2021.752672 [doi]
LID - 752672
AB  - BACKGROUND: Patients with pelvic and sacral tumors are prone to massive blood 
      loss (MBL) during surgery, which may endanger their lives. PURPOSES: This study 
      aimed to determine the feasibility of using deep neural network (DNN) and 
      radiomics nomogram (RN) based on 3D computed tomography (CT) features and 
      clinical characteristics to predict the intraoperative MBL of pelvic and sacral 
      tumors. MATERIALS AND METHODS: This single-center retrospective analysis included 
      810 patients with pelvic and sacral tumors. 1316 CT and CT enhanced radiomics 
      features were extracted. RN1 and RN2 were constructed by random grouping and time 
      node grouping, respectively. The DNN models were constructed for comparison with 
      RN. Clinical factors associated with the MBL were also evaluated. The area under 
      the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to 
      evaluate different models. RESULTS: Radscore, tumor type, tumor location, and sex 
      were significant predictors of the MBL of pelvic and sacral tumors (P < 0.05), of 
      which radscore (OR, ranging from 2.109 to 4.706, P < 0.001) was the most 
      important. The clinical-DNN and clinical-RN performed better than DNN and RN. The 
      best-performing clinical-DNN model based on CT features exhibited an AUC of 0.92 
      and an ACC of 0.97 in the training set, and an AUC of 0.92 and an ACC of 0.75 in 
      the validation set. CONCLUSIONS: The clinical-DNN and clinical-RN had good 
      performance in predicting the MBL of pelvic and sacral tumors, which could be 
      used for clinical decision-making.
CI  - Copyright (c) 2021 Yin, Sun, Wang, Chen and Hong.
FAU - Yin, Ping
AU  - Yin P
AD  - Department of Radiology, Peking University People's Hospital, Beijing, China.
FAU - Sun, Chao
AU  - Sun C
AD  - Department of Radiology, Peking University People's Hospital, Beijing, China.
FAU - Wang, Sicong
AU  - Wang S
AD  - Department of Pharmaceuticals Diagnosis, GE Healthcare (China), Shanghai, China.
FAU - Chen, Lei
AU  - Chen L
AD  - Department of Radiology, Peking University People's Hospital, Beijing, China.
FAU - Hong, Nan
AU  - Hong N
AD  - Department of Radiology, Peking University People's Hospital, Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20211025
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC8574215
OTO - NOTNLM
OT  - blood loss
OT  - computed tomography
OT  - deep neural network
OT  - pelvic tumors
OT  - radiomics
COIS- Author SW was employed by company GE healthcare. The remaining authors declare 
      that the research was conducted in the absence of any commercial or financial 
      relationships that could be construed as a potential conflict of interest.
EDAT- 2021/11/12 06:00
MHDA- 2021/11/12 06:01
PMCR- 2021/01/01
CRDT- 2021/11/11 07:00
PHST- 2021/08/03 00:00 [received]
PHST- 2021/10/06 00:00 [accepted]
PHST- 2021/11/11 07:00 [entrez]
PHST- 2021/11/12 06:00 [pubmed]
PHST- 2021/11/12 06:01 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2021.752672 [doi]
PST - epublish
SO  - Front Oncol. 2021 Oct 25;11:752672. doi: 10.3389/fonc.2021.752672. eCollection 
      2021.