{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang Y"],"funding":["Natural Science Foundation of Shandong Province","Key Technology Research and Development Program of Shandong Province"],"pagination":["460"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11015586"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Background</h4>To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model.<h4>Methods</h4>We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomo"],"journal":["BMC cancer"],"pubmed_title":["A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma."],"pmcid":["PMC11015586"],"funding_grant_id":["ZR2022QH121","2021LCZX04","ZR2022ZD31"],"pubmed_authors":["Yang Y","Yi Y","Li S","Zhang L","Li B","Wang Z","Zhang B","Sang Z","Cao Q"],"additional_accession":[]},"is_claimable":false,"name":"A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.","description":"<h4>Background</h4>To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model.<h4>Methods</h4>We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomo","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2025-04-04T09:34:36.084Z","creation":"2025-04-04T09:34:36.084Z"},"accession":"S-EPMC11015586","cross_references":{"pubmed":["38609892"],"doi":["10.1186/s12885-024-12239-0"]}}