Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study
Objectives Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. Methods We retrospectively recruited 454 patients with p...
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| Veröffentlicht in: | European radiology 2023-09, Vol.33 (9), p.6308-6317 |
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| creator | Li, Ye Xu, Zexuan Lv, Xinna Li, Chenghai He, Wei Lv, Yan Hou, Dailun |
| description | Objectives
Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.
Methods
We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (
n
= 295, 102), nodules (
n
= 302, 97), and their combination (
n
= 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.
Results
Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877,
p
> 0.05) and testing cohort (0.820 versus 0.786,
p
0.05) and testing cohort (0.820 versus 0.855,
p
> 0.05).
Conclusions
The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.
Clinical relevance statement
Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.
Key Points
• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB.
• The radiomics model showed a favorable performance for the identification of MDR-TB.
• The combined model holds potential to be used as a diagnostic tool in routine clinical practice. |
| doi_str_mv | 10.1007/s00330-023-09589-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10067016</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2848589466</sourcerecordid><originalsourceid>FETCH-LOGICAL-c475t-5f5fd90ac00e74376b4ebbf532a5b521dc203ae52b1d4fa62aef3d54becfb24e3</originalsourceid><addsrcrecordid>eNp9kUtv1TAQhSNERR_wB1ggS2zYhI5fccIGoSsKSJWQUFlbjjO-uEriix1Xvf8etyml7YKVR5rvnBnPqarXFN5TAHWaADiHGhivoZNtV18_q46o4Kym0IrnD-rD6jilSwDoqFAvqkOuAIRU9KiafpjBh8nbRMxsxn3yiQRHxjxvyeaCuBDJlMfFDzFvScTSXsxskewiDt4uPszEz8SU6grJknuMNo-hYB-IWZUW5yUiSUse9i-rA2fGhK_u3pPq59nni83X-vz7l2-bT-e1FUoutXTSDR0YC4BKcNX0AvveSc6M7CWjg2XADUrW00E40zCDjg9S9GhdzwTyk-rj6rvL_YTD7Qpm1LvoJxP3OhivH3dm_0tvw5Uud20U0KY4vLtziOF3xrToySeL42hmDDlppjrRtB1nbUHfPkEvQ47lmIVqRVuCEc2NIVspG0NKEd39NhRuxiq9xqlLnPo2Tn1dRG8e_uNe8je_AvAVSKU1bzH-m_0f2z8QZq8m</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2848589466</pqid></control><display><type>article</type><title>Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Li, Ye ; Xu, Zexuan ; Lv, Xinna ; Li, Chenghai ; He, Wei ; Lv, Yan ; Hou, Dailun</creator><creatorcontrib>Li, Ye ; Xu, Zexuan ; Lv, Xinna ; Li, Chenghai ; He, Wei ; Lv, Yan ; Hou, Dailun</creatorcontrib><description>Objectives
Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.
Methods
We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (
n
= 295, 102), nodules (
n
= 302, 97), and their combination (
n
= 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.
Results
Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877,
p
> 0.05) and testing cohort (0.820 versus 0.786,
p
< 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933,
p
> 0.05) and testing cohort (0.820 versus 0.855,
p
> 0.05).
Conclusions
The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.
Clinical relevance statement
Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.
Key Points
• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB.
• The radiomics model showed a favorable performance for the identification of MDR-TB.
• The combined model holds potential to be used as a diagnostic tool in routine clinical practice.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-023-09589-x</identifier><identifier>PMID: 37004571</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Chest ; Diagnostic Radiology ; Drug Resistance, Multiple ; Health risks ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Lung ; Medicine ; Medicine & Public Health ; Multidrug resistance ; Multidrug resistant organisms ; Neuroradiology ; Nodules ; Patients ; Performance prediction ; Predictions ; Public health ; Radiology ; Radiomics ; Retrospective Studies ; Tomography, X-Ray Computed ; Training ; Tuberculosis ; Tuberculosis, Multidrug-Resistant - diagnostic imaging ; Ultrasound</subject><ispartof>European radiology, 2023-09, Vol.33 (9), p.6308-6317</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to European Society of Radiology.</rights><rights>The Author(s), under exclusive licence to European Society of Radiology 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-5f5fd90ac00e74376b4ebbf532a5b521dc203ae52b1d4fa62aef3d54becfb24e3</citedby><cites>FETCH-LOGICAL-c475t-5f5fd90ac00e74376b4ebbf532a5b521dc203ae52b1d4fa62aef3d54becfb24e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-023-09589-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-023-09589-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37004571$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Ye</creatorcontrib><creatorcontrib>Xu, Zexuan</creatorcontrib><creatorcontrib>Lv, Xinna</creatorcontrib><creatorcontrib>Li, Chenghai</creatorcontrib><creatorcontrib>He, Wei</creatorcontrib><creatorcontrib>Lv, Yan</creatorcontrib><creatorcontrib>Hou, Dailun</creatorcontrib><title>Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.
Methods
We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (
n
= 295, 102), nodules (
n
= 302, 97), and their combination (
n
= 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.
Results
Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877,
p
> 0.05) and testing cohort (0.820 versus 0.786,
p
< 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933,
p
> 0.05) and testing cohort (0.820 versus 0.855,
p
> 0.05).
Conclusions
The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.
Clinical relevance statement
Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.
Key Points
• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB.
• The radiomics model showed a favorable performance for the identification of MDR-TB.
• The combined model holds potential to be used as a diagnostic tool in routine clinical practice.</description><subject>Chest</subject><subject>Diagnostic Radiology</subject><subject>Drug Resistance, Multiple</subject><subject>Health risks</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Lung</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Multidrug resistance</subject><subject>Multidrug resistant organisms</subject><subject>Neuroradiology</subject><subject>Nodules</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Predictions</subject><subject>Public health</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Retrospective Studies</subject><subject>Tomography, X-Ray Computed</subject><subject>Training</subject><subject>Tuberculosis</subject><subject>Tuberculosis, Multidrug-Resistant - diagnostic imaging</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUtv1TAQhSNERR_wB1ggS2zYhI5fccIGoSsKSJWQUFlbjjO-uEriix1Xvf8etyml7YKVR5rvnBnPqarXFN5TAHWaADiHGhivoZNtV18_q46o4Kym0IrnD-rD6jilSwDoqFAvqkOuAIRU9KiafpjBh8nbRMxsxn3yiQRHxjxvyeaCuBDJlMfFDzFvScTSXsxskewiDt4uPszEz8SU6grJknuMNo-hYB-IWZUW5yUiSUse9i-rA2fGhK_u3pPq59nni83X-vz7l2-bT-e1FUoutXTSDR0YC4BKcNX0AvveSc6M7CWjg2XADUrW00E40zCDjg9S9GhdzwTyk-rj6rvL_YTD7Qpm1LvoJxP3OhivH3dm_0tvw5Uud20U0KY4vLtziOF3xrToySeL42hmDDlppjrRtB1nbUHfPkEvQ47lmIVqRVuCEc2NIVspG0NKEd39NhRuxiq9xqlLnPo2Tn1dRG8e_uNe8je_AvAVSKU1bzH-m_0f2z8QZq8m</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Li, Ye</creator><creator>Xu, Zexuan</creator><creator>Lv, Xinna</creator><creator>Li, Chenghai</creator><creator>He, Wei</creator><creator>Lv, Yan</creator><creator>Hou, Dailun</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230901</creationdate><title>Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study</title><author>Li, Ye ; Xu, Zexuan ; Lv, Xinna ; Li, Chenghai ; He, Wei ; Lv, Yan ; Hou, Dailun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c475t-5f5fd90ac00e74376b4ebbf532a5b521dc203ae52b1d4fa62aef3d54becfb24e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Chest</topic><topic>Diagnostic Radiology</topic><topic>Drug Resistance, Multiple</topic><topic>Health risks</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Lung</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Multidrug resistance</topic><topic>Multidrug resistant organisms</topic><topic>Neuroradiology</topic><topic>Nodules</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Predictions</topic><topic>Public health</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Retrospective Studies</topic><topic>Tomography, X-Ray Computed</topic><topic>Training</topic><topic>Tuberculosis</topic><topic>Tuberculosis, Multidrug-Resistant - diagnostic imaging</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Ye</creatorcontrib><creatorcontrib>Xu, Zexuan</creatorcontrib><creatorcontrib>Lv, Xinna</creatorcontrib><creatorcontrib>Li, Chenghai</creatorcontrib><creatorcontrib>He, Wei</creatorcontrib><creatorcontrib>Lv, Yan</creatorcontrib><creatorcontrib>Hou, Dailun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Ye</au><au>Xu, Zexuan</au><au>Lv, Xinna</au><au>Li, Chenghai</au><au>He, Wei</au><au>Lv, Yan</au><au>Hou, Dailun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2023-09-01</date><risdate>2023</risdate><volume>33</volume><issue>9</issue><spage>6308</spage><epage>6317</epage><pages>6308-6317</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.
Methods
We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (
n
= 295, 102), nodules (
n
= 302, 97), and their combination (
n
= 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.
Results
Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877,
p
> 0.05) and testing cohort (0.820 versus 0.786,
p
< 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933,
p
> 0.05) and testing cohort (0.820 versus 0.855,
p
> 0.05).
Conclusions
The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.
Clinical relevance statement
Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.
Key Points
• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB.
• The radiomics model showed a favorable performance for the identification of MDR-TB.
• The combined model holds potential to be used as a diagnostic tool in routine clinical practice.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37004571</pmid><doi>10.1007/s00330-023-09589-x</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-1084 |
| ispartof | European radiology, 2023-09, Vol.33 (9), p.6308-6317 |
| issn | 1432-1084 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10067016 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Chest Diagnostic Radiology Drug Resistance, Multiple Health risks Humans Imaging Internal Medicine Interventional Radiology Lung Medicine Medicine & Public Health Multidrug resistance Multidrug resistant organisms Neuroradiology Nodules Patients Performance prediction Predictions Public health Radiology Radiomics Retrospective Studies Tomography, X-Ray Computed Training Tuberculosis Tuberculosis, Multidrug-Resistant - diagnostic imaging Ultrasound |
| title | Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study |
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