<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>25(1)</volume><submitter>Bai D</submitter><pubmed_abstract>&lt;h4>Objective&lt;/h4>This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (PMP), using Shapley Additive Explanations (SHAP) for model interpretation.&lt;h4>Materials and methods&lt;/h4>This retrospective study analyzed 158 pathologically confirmed PMP cases (85 low-grade, 73 high-grade) from January 4, 2015 to April 30, 2024. Comprehensive clinical data including demographic characteristics, serum tumor markers (CEA, CA19-9, CA125, D-dimer, CA-724, CA-242), and CT-peritoneal cancer index (CT-PCI) were collected. Radiomics features were extracted from preoperative contrast-enhanced CT scans using standardized protocols</pubmed_abstract><journal>BMC medical imaging</journal><pagination>300</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12306113</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.</pubmed_title><pmcid>PMC12306113</pmcid><pubmed_authors>Bai D</pubmed_authors><pubmed_authors>Zheng Z</pubmed_authors><pubmed_authors>Liang Y</pubmed_authors><pubmed_authors>Li F</pubmed_authors><pubmed_authors>Shi G</pubmed_authors><pubmed_authors>Wang Z</pubmed_authors></additional><is_claimable>false</is_claimable><name>A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.</name><description>&lt;h4>Objective&lt;/h4>This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (PMP), using Shapley Additive Explanations (SHAP) for model interpretation.&lt;h4>Materials and methods&lt;/h4>This retrospective study analyzed 158 pathologically confirmed PMP cases (85 low-grade, 73 high-grade) from January 4, 2015 to April 30, 2024. Comprehensive clinical data including demographic characteristics, serum tumor markers (CEA, CA19-9, CA125, D-dimer, CA-724, CA-242), and CT-peritoneal cancer index (CT-PCI) were collected. Radiomics features were extracted from preoperative contrast-enhanced CT scans using standardized protocols</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Jul</publication><modification>2026-03-18T14:05:28.479Z</modification><creation>2025-08-23T03:07:38.465Z</creation></dates><accession>S-EPMC12306113</accession><cross_references><pubmed>40722011</pubmed><doi>10.1186/s12880-025-01843-6</doi></cross_references></HashMap>