{"doi":"10.3390/bioengineering10111334","title":"Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy","abstract":"Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.","journal":"Bioengineering","year":2023,"id":364875,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8815,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":854224,"name":"Ke Zhang","orcid":"0000-0003-3194-2546","position":1,"is_corresponding":false},{"id":854226,"name":"Patrik Gilley","orcid":"0009-0003-1013-4483","position":2,"is_corresponding":false},{"id":854223,"name":"Xuxin Chen","orcid":"0000-0001-8872-8992","position":3,"is_corresponding":false},{"id":1120443,"name":"Youkabed Sadri","orcid":null,"position":4,"is_corresponding":false},{"id":1120444,"name":"Theresa Thai","orcid":null,"position":5,"is_corresponding":false},{"id":905610,"name":"Lauren Dockery","orcid":null,"position":6,"is_corresponding":false},{"id":70255,"name":"Kathleen N. Moore","orcid":"0000-0002-5803-0718","position":7,"is_corresponding":false},{"id":66134,"name":"Robert S. Mannel","orcid":null,"position":8,"is_corresponding":false},{"id":857978,"name":"Yuchen Qiu","orcid":"0009-0009-2826-9654","position":9,"is_corresponding":false},{"id":854225,"name":"Neman Abdoli","orcid":"0009-0002-4959-2652","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T01:14:41.218128Z","pmid":"38002458","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":[]}