PMID- 35326634
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221215
IS  - 2072-6694 (Print)
IS  - 2072-6694 (Electronic)
IS  - 2072-6694 (Linking)
VI  - 14
IP  - 6
DP  - 2022 Mar 14
TI  - Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using 
      Machine Learning.
LID - 10.3390/cancers14061481 [doi]
LID - 1481
AB  - The high-level relationships that form complex networks within tumors and between 
      surrounding tissue is challenging and not fully understood. To better understand 
      these tumoral networks, we developed a tumor connectomics framework (TCF) based 
      on graph theory with machine learning to model the complex interactions within 
      and around the tumor microenvironment that are detectable on imaging. The TCF 
      characterization model was tested with independent datasets of breast, brain, and 
      prostate lesions with corresponding validation datasets in breast and brain 
      cancer. The TCF network connections were modeled using graph metrics of 
      centrality, average path length (APL), and clustering from multiparametric MRI 
      with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the 
      Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical 
      analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM 
      AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast 
      lesions had a significantly higher clustering coefficient and degree centrality 
      than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity 
      compared to Grade 4 tumors with increased degree centrality and clustering 
      coefficients. Gleason 7 prostate lesions had increased betweenness centrality and 
      APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 
      and 0.73 to 0.87, respectively. These TCF findings were similar in the validation 
      breast and brain datasets. In conclusion, we present a new method for tumor 
      characterization and visualization that results in a better understanding of the 
      global and regional connections within the lesion and surrounding tissue.
FAU - Parekh, Vishwa S
AU  - Parekh VS
AUID- ORCID: 0000-0002-6306-0305
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, School of 
      Medicine, The Johns Hopkins University, Baltimore, MD 21205, USA.
AD  - Department of Computer Science, The Johns Hopkins University, Baltimore, MD 
      21210, USA.
FAU - Pillai, Jay J
AU  - Pillai JJ
AUID- ORCID: 0000-0002-8306-7192
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, School of 
      Medicine, The Johns Hopkins University, Baltimore, MD 21205, USA.
AD  - Department of Neurosurgery, School of Medicine, The Johns Hopkins University, 
      Baltimore, MD 21287, USA.
FAU - Macura, Katarzyna J
AU  - Macura KJ
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, School of 
      Medicine, The Johns Hopkins University, Baltimore, MD 21205, USA.
AD  - Sidney Kimmel Comprehensive Cancer Center, School of Medicine, The Johns Hopkins 
      University, Baltimore, MD 21205, USA.
FAU - LaViolette, Peter S
AU  - LaViolette PS
AUID- ORCID: 0000-0002-9602-6891
AD  - Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
FAU - Jacobs, Michael A
AU  - Jacobs MA
AUID- ORCID: 0000-0002-1125-1644
AD  - The Russell H. Morgan Department of Radiology and Radiological Science, School of 
      Medicine, The Johns Hopkins University, Baltimore, MD 21205, USA.
AD  - Sidney Kimmel Comprehensive Cancer Center, School of Medicine, The Johns Hopkins 
      University, Baltimore, MD 21205, USA.
LA  - eng
GR  - R01 CA249882/CA/NCI NIH HHS/United States
GR  - R01CA18144/NH/NIH HHS/United States
GR  - Tax Check-off Program for Prostate Cancer Research/The State of Wisconsin/
GR  - R01 CA218144/CA/NCI NIH HHS/United States
GR  - 1R01CA190299/NH/NIH HHS/United States
GR  - 5P30CA006973 (Imaging Response Assessment Team-IRAT)/NH/NIH HHS/United States
GR  - R01CA249882/NH/NIH HHS/United States
GR  - U01CA140204/NH/NIH HHS/United States
PT  - Journal Article
DEP - 20220314
PL  - Switzerland
TA  - Cancers (Basel)
JT  - Cancers
JID - 101526829
PMC - PMC8946165
OTO - NOTNLM
OT  - brain
OT  - breast
OT  - cancer
OT  - complex networks
OT  - graph theory
OT  - multiparametric MRI
OT  - prostate
OT  - tumor connectomics
COIS- The authors declare no conflict of interest.
EDAT- 2022/03/26 06:00
MHDA- 2022/03/26 06:01
PMCR- 2022/03/14
CRDT- 2022/03/25 01:02
PHST- 2021/12/13 00:00 [received]
PHST- 2022/03/01 00:00 [revised]
PHST- 2022/03/09 00:00 [accepted]
PHST- 2022/03/25 01:02 [entrez]
PHST- 2022/03/26 06:00 [pubmed]
PHST- 2022/03/26 06:01 [medline]
PHST- 2022/03/14 00:00 [pmc-release]
AID - cancers14061481 [pii]
AID - cancers-14-01481 [pii]
AID - 10.3390/cancers14061481 [doi]
PST - epublish
SO  - Cancers (Basel). 2022 Mar 14;14(6):1481. doi: 10.3390/cancers14061481.