{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:11:54Z","timestamp":1777633914163,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>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 &gt;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 &gt;95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned &gt;98.42% and &gt;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.<\/jats:p>","DOI":"10.3390\/curroncol30100668","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"9244-9261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors"],"prefix":"10.3390","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4715-1108","authenticated-orcid":false,"given":"Mahesh Kumar","family":"Padwal","sequence":"first","affiliation":[{"name":"Molecular Biology Division, Bhabha Atomic Research Centre, Mumbai 400085, India"},{"name":"Homi Bhabha National Institute, Mumbai 400094, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6607-2558","authenticated-orcid":false,"given":"Sandip","family":"Basu","sequence":"additional","affiliation":[{"name":"Homi Bhabha National Institute, Mumbai 400094, India"},{"name":"Radiation Medicine Centre, Bhabha Atomic Research Centre, Tata Memorial Hospital Annexe, Mumbai 400012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7381-6671","authenticated-orcid":false,"given":"Bhakti","family":"Basu","sequence":"additional","affiliation":[{"name":"Molecular Biology Division, Bhabha Atomic Research Centre, Mumbai 400085, India"},{"name":"Homi Bhabha National Institute, Mumbai 400094, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/nrendo.2011.120","article-title":"Neuroendocrine neoplasms of the gut and pancreas: New insights","volume":"8","author":"Rindi","year":"2012","journal-title":"Nat. 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