{"doi":"10.3390/cancers17132133","title":"Using [18F]FDG PET/CT to Identify Optimal Responders to Neoadjuvant Therapy in Breast Cancer—Results from a Prospective Patient Cohort","abstract":"<jats:p>Background/objectives: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is a solid indicator of favourable prognosis, potentially also being useful for more conservative patient management. We aim to explore the potential of [18F]FDG PET/CT as a non-invasive method to predict response to NAC. Methods: In this prospective, observational cohort study, we enrolled BC patient candidates for NAC who underwent baseline and preoperative [18F]FDG PET/CT. NAC response was determined using final histopathology. PET images were assessed qualitatively and semi-quantitatively, and the findings correlated with NAC response. Results: In total, 133 BC patients were included. The visual analysis of preoperative PET/CT detected residual disease (RD) with high specificity (&gt;93%) and moderate sensitivity, based on pCR/RD classification and RCB index. Semiquantitative measures (SUVmax, TBR) were significantly higher in non-responders across the classification methods (p &lt; 0.001 for all). Conclusions: These findings highlight the potential of preoperative [18F]FDG PET/CT as a complementary tool for identifying excellent responders to NAC across BC subtypes or response criteria. This could inform personalised treatment and potentially allow for surgery to be omitted in selected patients.</jats:p>","journal":"Cancers","year":2025,"id":635802,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"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":1649692,"name":"Paola Tiberio","orcid":"0000-0001-9506-7010","position":1,"is_corresponding":false},{"id":1649694,"name":"Rosalba Torrisi","orcid":null,"position":2,"is_corresponding":false},{"id":1649696,"name":"Roberta Zanca","orcid":"0009-0006-6703-2463","position":3,"is_corresponding":false},{"id":1649698,"name":"Marcello Rodari","orcid":null,"position":4,"is_corresponding":false},{"id":554610,"name":"Alberto Zambelli","orcid":"0000-0002-1374-1831","position":5,"is_corresponding":false},{"id":258833,"name":"Armando Santoro","orcid":"0000-0003-1709-9492","position":6,"is_corresponding":false},{"id":1649700,"name":"Bethania Fernandes","orcid":"0000-0002-4327-1586","position":7,"is_corresponding":false},{"id":1649703,"name":"Andrea Sagona","orcid":null,"position":8,"is_corresponding":false},{"id":1649705,"name":"Valentina Errico","orcid":null,"position":9,"is_corresponding":false},{"id":199482,"name":"Alberto Testori","orcid":"0000-0002-3032-9494","position":10,"is_corresponding":false},{"id":199481,"name":"Corrado Tinterri","orcid":"0000-0002-7272-3924","position":11,"is_corresponding":false},{"id":1135650,"name":"Arturo Chiti","orcid":"0000-0002-5806-1856","position":12,"is_corresponding":false},{"id":1649710,"name":"Rita De Sanctis","orcid":"0000-0003-3202-1933","position":13,"is_corresponding":false},{"id":643212,"name":"Martina Sollini","orcid":"0000-0003-2214-6492","position":14,"is_corresponding":false},{"id":1649714,"name":"Lidija Antunovic","orcid":"0000-0003-1832-1083","position":15,"is_corresponding":false},{"id":1554734,"name":"Fabrizia Gelardi","orcid":"0000-0001-7120-333X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Using [18F]FDG PET/CT to Identify Optimal Responders to Neoadjuvant Therapy in Breast Cancer—Results from a Prospective Patient Cohort","abstract":"<jats:p>Background/objectives: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is a solid indicator of favourable prognosis, potentially also being useful for more conservative patient management. We aim to explore the potential of [18F]FDG PET/CT as a non-invasive method to predict response to NAC. Methods: In this prospective, observational cohort study, we enrolled BC patient candidates for NAC who underwent baseline and preoperative [18F]FDG PET/CT. NAC response was determined using final histopathology. PET images were assessed qualitatively and semi-quantitatively, and the findings correlated with NAC response. Results: In total, 133 BC patients were included. The visual analysis of preoperative PET/CT detected residual disease (RD) with high specificity (&gt;93%) and moderate sensitivity, based on pCR/RD classification and RCB index. Semiquantitative measures (SUVmax, TBR) were significantly higher in non-responders across the classification methods (p &lt; 0.001 for all). Conclusions: These findings highlight the potential of preoperative [18F]FDG PET/CT as a complementary tool for identifying excellent responders to NAC across BC subtypes or response criteria. This could inform personalised treatment and potentially allow for surgery to be omitted in selected patients.</jats:p>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40647433","pmcid":"PMC12248987","openalex_id":"https://openalex.org/W4411623529","authors":[],"funders":[{"funder_name":"Pink Union","grant_id":"Pink Union","title":null},{"funder_name":"Pink Union project of Fondazione Humanitas per la Ricerca","grant_id":"","title":null}],"total_grants":2,"fwci":1.4072,"citation_percentile":0.81358941,"influential_citations":0,"citation_trend":[{"year":2025,"count":1},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2072-6694/17/13/2133/pdf?version=1750846380","host_type":"journal"},{"url":"https://www.mdpi.com/2072-6694/17/13/2133/pdf?version=1750846380","host_type":"publisher"},{"url":"https://www.mdpi.com/2072-6694/17/13/2133/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/cancers17132133","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40647433","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12248987","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12248987","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12248987?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Breast Cancer Treatment Studies","Medical Imaging Techniques and Applications","Radiomics and Machine Learning in Medical Imaging"],"mesh_terms":[],"keywords":["Medicine","Breast cancer","Prospective cohort study","PET-CT","Cohort","Radiology","Neoadjuvant therapy","Cancer","Positron emission tomography","Complete response","Internal medicine","Chemotherapy","Oncology","Nuclear medicine","Surgery","Neoadjuvant chemotherapy","Pet/ct","Suvmax","Tbr"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"nct"},{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T15:35:04.068593Z","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":[]}