PMID- 36001926
OWN - NLM
STAT- MEDLINE
DCOM- 20220923
LR  - 20220930
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 149
DP  - 2022 Oct
TI  - Pathological prognosis classification of patients with neuroblastoma using 
      computational pathology analysis.
PG  - 105980
LID - S0010-4825(22)00707-7 [pii]
LID - 10.1016/j.compbiomed.2022.105980 [doi]
AB  - Neuroblastoma is the most common extracranial solid tumor in early childhood. 
      International Neuroblastoma Pathology Classification (INPC) is a commonly used 
      classification system that provides clinicians with a reference for treatment 
      stratification. However, given the complex and subjective assessment of the INPC, 
      there will be inconsistencies in the analysis of the same patient by multiple 
      pathologists. An automated, comprehensive and objective classification method is 
      needed to identify different prognostic groups in patients with neuroblastoma. In 
      this study, we collected 563 hematoxylin and eosin-stained histopathology 
      whole-slide images from 107 patients with neuroblastoma who underwent surgical 
      resection. We proposed a novel processing pipeline for nuclear segmentation, 
      cell-level image feature extraction, and patient-level feature aggregation. 
      Logistic regression model was built to classify patients with favorable histology 
      (FH) and patients with unfavorable histology (UH). On the training/test dataset, 
      patient-level of nucleus morphological/intensity features and age could correctly 
      classify patients with a mean area under the receiver operating characteristic 
      curve (AUC) of 0.946, a mean accuracy of 0.856, and a mean Matthews Correlation 
      Coefficient (MCC) of 0.703,respectively. On the independent validation dataset, 
      the classification model achieved a mean AUC of 0.938, a mean accuracy of 0.865 
      and a mean MCC of 0.630, showing good generalizability. Our results suggested 
      that automatically derived image features could identify the differences in 
      nuclear morphological and intensity between different prognostic groups, which 
      could provide a reference to pathologists and facilitate the evaluation of the 
      pathological prognosis in patients with neuroblastoma.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Liu, Yanfei
AU  - Liu Y
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China.
FAU - Jia, Yuxia
AU  - Jia Y
AD  - Center for Brain Imaging, School of Life Science and Technology, Xidian 
      University & Engineering Research Center of Molecular and Neuro Imaging, Ministry 
      of Education, Xi'an, Shaanxi, 710126, China; International Joint Research Center 
      for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key 
      Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, 
      School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, 
      China.
FAU - Hou, Chongzhi
AU  - Hou C
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China.
FAU - Li, Nan
AU  - Li N
AD  - Center for Brain Imaging, School of Life Science and Technology, Xidian 
      University & Engineering Research Center of Molecular and Neuro Imaging, Ministry 
      of Education, Xi'an, Shaanxi, 710126, China; International Joint Research Center 
      for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key 
      Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, 
      School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, 
      China.
FAU - Zhang, Na
AU  - Zhang N
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China.
FAU - Yan, Xiaosong
AU  - Yan X
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China.
FAU - Yang, Li
AU  - Yang L
AD  - Department of Pathology, Xijing Hospital, The Fourth Military Medical University, 
      Xi'an, Shanxi, 710032, China.
FAU - Guo, Yong
AU  - Guo Y
AD  - Department of Pathology, Xijing Hospital, The Fourth Military Medical University, 
      Xi'an, Shanxi, 710032, China.
FAU - Chen, Huangtao
AU  - Chen H
AD  - Department of Neurosurgery, the Second Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, Shaanxi, 710032, China.
FAU - Li, Jun
AU  - Li J
AD  - Center for Brain Imaging, School of Life Science and Technology, Xidian 
      University & Engineering Research Center of Molecular and Neuro Imaging, Ministry 
      of Education, Xi'an, Shaanxi, 710126, China; International Joint Research Center 
      for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key 
      Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, 
      School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, 
      China. Electronic address: junli@xidian.edu.cn.
FAU - Hao, Yuewen
AU  - Hao Y
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China. Electronic address: 1982_edifier@163.com.
FAU - Liu, Jixin
AU  - Liu J
AD  - The Affiliated Children's Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 
      710003, China; Center for Brain Imaging, School of Life Science and Technology, 
      Xidian University & Engineering Research Center of Molecular and Neuro Imaging, 
      Ministry of Education, Xi'an, Shaanxi, 710126, China; International Joint 
      Research Center for Advanced Medical Imaging and Intelligent Diagnosis and 
      Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of 
      trans-Scale Life Information, School of Life Science and Technology, Xidian 
      University, Xi'an, Shaanxi, 710126, China. Electronic address: 
      liujixin@xidian.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220818
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
RN  - TDQ283MPCW (Eosine Yellowish-(YS))
RN  - YKM8PY2Z55 (Hematoxylin)
SB  - IM
MH  - Child, Preschool
MH  - Eosine Yellowish-(YS)
MH  - *Ganglioneuroblastoma/pathology
MH  - Hematoxylin
MH  - Humans
MH  - *Neuroblastoma/diagnostic imaging/pathology
MH  - ROC Curve
OTO - NOTNLM
OT  - Computational pathology
OT  - Machine learning
OT  - Neuroblastoma
OT  - Prognosis
OT  - Risk stratification
COIS- Declaration of competing interest None declared.
EDAT- 2022/08/25 06:00
MHDA- 2022/09/24 06:00
CRDT- 2022/08/24 18:16
PHST- 2022/05/10 00:00 [received]
PHST- 2022/08/08 00:00 [revised]
PHST- 2022/08/14 00:00 [accepted]
PHST- 2022/08/25 06:00 [pubmed]
PHST- 2022/09/24 06:00 [medline]
PHST- 2022/08/24 18:16 [entrez]
AID - S0010-4825(22)00707-7 [pii]
AID - 10.1016/j.compbiomed.2022.105980 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Oct;149:105980. doi: 10.1016/j.compbiomed.2022.105980. Epub 
      2022 Aug 18.