{"doi":"10.1148/radiol.222830","title":"MRI-based Quantification of Intratumoral Heterogeneity for Predicting                     Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer","abstract":null,"journal":"Radiology","year":2023,"id":610926,"datarank":0.8195747707538417,"base_score":5.4638318050256105,"endowment":5.4638318050256105,"self_citation_contribution":0.8195747707538417,"citation_network_contribution":0.0,"self_endowment_contribution":0.8195747707538417,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":235,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":2,"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":1570953,"name":"Xiaomei Huang","orcid":"0000-0002-8567-2708","position":1,"is_corresponding":false},{"id":1570954,"name":"Ziliang Cheng","orcid":"0000-0002-8854-1500","position":2,"is_corresponding":false},{"id":1570955,"name":"Zeyan Xu","orcid":"0000-0003-1384-3536","position":3,"is_corresponding":false},{"id":236035,"name":"Huan Lin","orcid":"0000-0003-0134-5361","position":4,"is_corresponding":false},{"id":590517,"name":"Chen Liu","orcid":"0000-0003-1558-6836","position":5,"is_corresponding":false},{"id":190890,"name":"Xiaobo Chen","orcid":"0000-0001-8755-6199","position":6,"is_corresponding":false},{"id":290752,"name":"Chunling Liu","orcid":"0000-0002-5692-2229","position":7,"is_corresponding":false},{"id":1216625,"name":"Changhong Liang","orcid":"0000-0002-6017-7129","position":8,"is_corresponding":false},{"id":297606,"name":"Cheng Lu","orcid":"0000-0002-7651-3924","position":9,"is_corresponding":false},{"id":1513707,"name":"Yanfen Cui","orcid":"0000-0001-6631-5687","position":10,"is_corresponding":false},{"id":1570956,"name":"Chu Han","orcid":"0000-0001-7557-9131","position":11,"is_corresponding":false},{"id":1570957,"name":"Jinrong Qu","orcid":"0000-0001-8751-9988","position":12,"is_corresponding":false},{"id":336486,"name":"Jun Shen","orcid":"0000-0001-7746-5285","position":13,"is_corresponding":false},{"id":1215969,"name":"Zaiyi Liu","orcid":"0000-0002-9307-8522","position":14,"is_corresponding":false},{"id":1570952,"name":"Zhenwei Shi","orcid":"0000-0003-1305-5935","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"MRI-based Quantification of Intratumoral Heterogeneity for Predicting                     Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer","abstract":"Background Breast cancer is highly heterogeneous, resulting in different treatment responses to neoadjuvant chemotherapy (NAC) among patients. A noninvasive quantitative measure of intratumoral heterogeneity (ITH) may be valuable for predicting treatment response. Purpose To develop a quantitative measure of ITH on pretreatment MRI scans and test its performance for predicting pathologic complete response (pCR) after NAC in patients with breast cancer. Materials and Methods Pretreatment MRI scans were retrospectively acquired in patients with breast cancer who received NAC followed by surgery at multiple centers from January 2000 to September 2020. Conventional radiomics (hereafter, C-radiomics) and intratumoral ecological diversity features were extracted from the MRI scans, and output probabilities of imaging-based decision tree models were used to generate a C-radiomics score and ITH index. Multivariable logistic regression analysis was used to identify variables associated with pCR, and significant variables, including clinicopathologic variables, C-radiomics score, and ITH index, were combined into a predictive model for which performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The training data set was comprised of 335 patients (median age, 48 years [IQR, 42-54 years]) from centers A and B, and 590, 280, and 384 patients (median age, 48 years [IQR, 41-55 years]) were included in the three external test data sets. Molecular subtype (odds ratio [OR] range, 4.76-8.39 [95% CI: 1.79, 24.21]; all <i>P</i> < .01), ITH index (OR, 30.05 [95% CI: 8.43, 122.64]; <i>P</i> < .001), and C-radiomics score (OR, 29.90 [95% CI: 12.04, 81.70]; <i>P</i> < .001) were independently associated with the odds of achieving pCR. The combined model showed good performance for predicting pCR to NAC in the training data set (AUC, 0.90) and external test data sets (AUC range, 0.83-0.87). Conclusion A model that combined an index created from pretreatment MRI-based imaging features quantitating ITH, C-radiomics score, and clinicopathologic variables showed good performance for predicting pCR to NAC in patients with breast cancer. © RSNA, 2023 <i>Supplemental material is available for this article.</i> See also the editorial by Rauch in this issue.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37432083","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFF1201003","title":null},{"funder_name":"National Science Fund for Distinguished Young Scholars","grant_id":"81925023","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82102034","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62102103","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82001789","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82171920","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12126610","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82171996","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"U1801681","title":null},{"funder_name":"China Postdoctoral Science Foundation","grant_id":"2022M710843","title":null},{"funder_name":"China Postdoctoral Science Foundation","grant_id":"2021M700897","title":null},{"funder_name":"High-level Hospital Construction Project","grant_id":"DFJH201805","title":null}],"total_grants":12,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://pubs.rsna.org/doi/pdf/10.1148/radiol.222830","host_type":"publisher"},{"url":"http://pubs.rsna.org/doi/pdf/10.1148/radiol.222830","host_type":"publisher"}],"fields_of_study":[],"mesh_terms":["Humans","Breast Neoplasms","Magnetic Resonance Imaging","Neoadjuvant Therapy","Odds Ratio","Retrospective Studies","Middle Aged","Female"],"keywords":[],"sdg_mappings":[],"linked_datasets":[{"doi":"10.7937/tcia.d8z0-9t85","title":"I-SPY 2 Breast Dynamic Contrast Enhanced MRI (I-SPY2 TRIAL)","publisher":"The Cancer Imaging Archive","resource_type":"Dataset"},{"doi":"10.7937/tcia.e3sv-re93","title":"Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations","publisher":"The Cancer Imaging Archive","resource_type":"Dataset"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-01T12:59:15.848947Z","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":[]}