Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors
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作者:
Padwal, Mahesh Kumar
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Bhabha Atom Res Ctr, Mol Biol Div, Mumbai 400085, India
Homi Bhabha Natl Inst, Mumbai 400094, IndiaBhabha Atom Res Ctr, Mol Biol Div, Mumbai 400085, India
Padwal, Mahesh Kumar
[1
,2
]
Basu, Sandip
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机构:
Homi Bhabha Natl Inst, Mumbai 400094, India
Bhabha Atom Res Ctr, Tata Mem Hosp Annexe, Radiat Med Ctr, Mumbai 400012, IndiaBhabha Atom Res Ctr, Mol Biol Div, Mumbai 400085, India
Basu, Sandip
[2
,3
]
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机构:
Basu, Bhakti
[1
,2
]
机构:
[1] Bhabha Atom Res Ctr, Mol Biol Div, Mumbai 400085, India
[2] Homi Bhabha Natl Inst, Mumbai 400094, India
[3] Bhabha Atom Res Ctr, Tata Mem Hosp Annexe, Radiat Med Ctr, Mumbai 400012, India
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.
机构:
Rush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Tierney, John F.
Poirier, Jennifer
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机构:
Rush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Poirier, Jennifer
Chivukula, Sitaram
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机构:
Rush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Chivukula, Sitaram
Pappas, Sam G.
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Rush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Pappas, Sam G.
Hertl, Martin
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机构:
Rush Univ, Med Ctr, Dept Surg, Div Transplantat, Chicago, IL 60612 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Hertl, Martin
Schadde, Erik
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机构:
Rush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Rush Univ, Med Ctr, Dept Surg, Div Transplantat, Chicago, IL 60612 USA
Cantonal Hosp Winterthur, Dept Surg, Zurich, Switzerland
Univ Zurich, Inst Physiol, Zurich, SwitzerlandRush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
Schadde, Erik
Keutgen, Xavier
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机构:
Univ Chicago, Med Ctr, Dept Surg, Div Gen Surg, Chicago, IL 60637 USARush Univ, Med Ctr, Dept Surg, Div Surg Oncol, Chicago, IL 60612 USA
机构:
Univ Calif San Francisco, Div Hematol Oncol, Dept Med, San Francisco, CA 94143 USA
Univ Calif San Francisco, UCSF Helen Diller Family Comprehens Canc Ctr, San Francisco, CA 94143 USAUniv Calif San Francisco, Div Hematol Oncol, Dept Med, San Francisco, CA 94143 USA
Bergsland, Emily K.
Nakakura, Eric K.
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机构:
Univ Calif San Francisco, UCSF Helen Diller Family Comprehens Canc Ctr, San Francisco, CA 94143 USA
Univ Calif San Francisco, Div Surg Oncol, Dept Surg, San Francisco, CA 94143 USAUniv Calif San Francisco, Div Hematol Oncol, Dept Med, San Francisco, CA 94143 USA