{"doi":"10.21037/qims-21-290","title":"Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features","abstract":"BACKGROUND: Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB. METHODS: We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB. RESULTS: DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features. CONCLUSIONS: Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.","journal":"Quantitative Imaging in Medicine and Surgery","year":2021,"id":167486,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9557,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":694958,"name":"Hang Yu","orcid":"0000-0003-3444-9992","position":1,"is_corresponding":false},{"id":694959,"name":"Karthik Kantipudi","orcid":"0000-0002-6423-1647","position":2,"is_corresponding":false},{"id":694960,"name":"Manohar Karki","orcid":"0000-0002-0353-9728","position":3,"is_corresponding":false},{"id":277105,"name":"Yasmin M. Kassim","orcid":"0000-0001-5339-8081","position":4,"is_corresponding":false},{"id":89650,"name":"Alex Rosenthal","orcid":"0000-0003-4190-9045","position":5,"is_corresponding":false},{"id":271863,"name":"Darrell E. Hurt","orcid":"0000-0002-9829-8567","position":6,"is_corresponding":false},{"id":243361,"name":"Ziv Yaniv","orcid":"0000-0003-0315-7727","position":7,"is_corresponding":false},{"id":75948,"name":"Stefan Jaeger","orcid":"0000-0001-6877-4318","position":8,"is_corresponding":false},{"id":277106,"name":"Feng Yang","orcid":"0000-0002-8334-7450","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-18T23:45:58.359801Z","pmid":"34993110","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":[]}