{"doi":"10.3390/cancers18020306","title":"Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms","abstract":"<jats:p>The clinical management of Pancreatic Neuroendocrine Neoplasms (Pan-NENs) is complicated by the disease’s intrinsic variability, which creates significant hurdles for accurate risk profiling and the standardization of treatment protocols. Recently, Artificial Intelligence (AI) has offered a promising avenue to address these challenges. By integrating and processing high-dimensional multimodal datasets (encompassing clinical history, radiomics, and pathology), these computational tools can refine survival forecasts and support the development of personalized medicine. However, the transition from experimental success to routine clinical use is currently obstructed by reliance on limited, retrospective cohorts that lack external validation, alongside unresolved concerns regarding algorithmic transparency and ethical governance. This review evaluates the current landscape of AI-driven prognostic modeling for Pan-NENs and critically examines the pathway towards their reliable integration into clinical practice.</jats:p>","journal":"Cancers","year":2026,"id":596283,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1527099,"name":"Emanuela Pirino","orcid":null,"position":1,"is_corresponding":false},{"id":1527100,"name":"Stefano Marcucci","orcid":"0000-0001-9144-9038","position":2,"is_corresponding":false},{"id":1527101,"name":"Franca Chierichetti","orcid":"0000-0002-1898-4736","position":3,"is_corresponding":false},{"id":1527102,"name":"Andrea Michielan","orcid":"0000-0003-1353-0935","position":4,"is_corresponding":false},{"id":1527103,"name":"Laura Bernardoni","orcid":"0000-0002-1712-4582","position":5,"is_corresponding":false},{"id":1527104,"name":"Armando Gabbrielli","orcid":"0000-0001-5875-7952","position":6,"is_corresponding":false},{"id":1261395,"name":"Maria Pina Dore","orcid":"0000-0001-7305-3531","position":7,"is_corresponding":false},{"id":1527105,"name":"Giuseppe Fanciulli","orcid":"0000-0002-8367-5649","position":8,"is_corresponding":false},{"id":1527106,"name":"Alberto Brolese","orcid":"0000-0002-6362-9055","position":9,"is_corresponding":false},{"id":1527098,"name":"Elettra Merola","orcid":"0000-0001-9553-7684","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms","abstract":"<jats:p>The clinical management of Pancreatic Neuroendocrine Neoplasms (Pan-NENs) is complicated by the disease’s intrinsic variability, which creates significant hurdles for accurate risk profiling and the standardization of treatment protocols. Recently, Artificial Intelligence (AI) has offered a promising avenue to address these challenges. By integrating and processing high-dimensional multimodal datasets (encompassing clinical history, radiomics, and pathology), these computational tools can refine survival forecasts and support the development of personalized medicine. However, the transition from experimental success to routine clinical use is currently obstructed by reliance on limited, retrospective cohorts that lack external validation, alongside unresolved concerns regarding algorithmic transparency and ethical governance. This review evaluates the current landscape of AI-driven prognostic modeling for Pan-NENs and critically examines the pathway towards their reliable integration into clinical practice.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41595224","pmcid":"PMC12839224","openalex_id":"https://openalex.org/W7124964676","authors":[],"funders":[],"total_grants":0,"fwci":8.2082,"citation_percentile":0.93289197,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2072-6694/18/2/306/pdf?version=1768834964","host_type":"journal"},{"url":"https://www.mdpi.com/2072-6694/18/2/306/pdf?version=1768834964","host_type":"publisher"},{"url":"https://www.mdpi.com/2072-6694/18/2/306/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/cancers18020306","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41595224","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12839224/","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12839224","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12839224?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Neuroendocrine Tumor Research Advances","Pancreatic and Hepatic Oncology Research","Thyroid Cancer Diagnosis and Treatment"],"mesh_terms":[],"keywords":["Neuroendocrine tumors","Standardization","Profiling (computer programming)","Pancreatic neuroendocrine tumor","MEDLINE","Transparency (behavior)","Clinical Practice","Artificial intelligence","Survival","Clinical Outcomes","Prognostic Models","Pancreatic Neuroendocrine Neoplasms"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T09:38:03.636074Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}