{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T14:18:12Z","timestamp":1787926692031,"version":"build-2784847793"},"reference-count":43,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Lung cancer is a common cancer with a high mortality rate worldwide. Significant differences in treatment, prognosis, and recurrence rates exist depending on lung cancer subtype. The goal of our study is to use machine learning models based on chest computed tomography (CT) scans to predict tumor subtypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 99 lung cancer cases with histopathological tissue diagnosis were included in this retrospective, single-center study. Tumor segmentation was performed in three-dimension (3D) using mediastinal and parenchymal windows on a chest CT, and 180 features were extracted, of which 90 were retained after recursive feature elimination (RFE). Due to class imbalance, model performance was tested using a stratified folding method. Machine learning (ML) algorithms CatBoost, XGBoost, and Ensemble models were trained separately on parenchymal and mediastinal window feature sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The most common type of lung cancer among patients was adenocarcinoma (AC) in the non-small cell lung cancer (NSCLC) group (n=57, 57.58%). When compared to XGBoost and Ensemble models, CatBoost demonstrated superior performance in differentiating between small cell lung cancer (SCLC) and NSCLC, achieving 90% accuracy and an 80.39% area under the curve (AUC). The Ensemble model, also trained with the parenchymal window, achieved the highest accuracy in distinguishing adenocarcinoma from other lung cancers (non-adenocarcinoma), with 75% accuracy and 72.92% AUC, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In summary, our research has demonstrated that machine learning (ML) algorithms can accurately determine lung cancer tumor subtypes, eliminating the need for additional invasive procedures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338261483507","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:58:55Z","timestamp":1787698735000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Using Machine Learning Algorithms in Non-contrast CT Based Images in Determining Pathological Subtypes of Lung Cancer"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5001-3649","authenticated-orcid":false,"given":"Beyza","family":"Nur KUZAN","sequence":"first","affiliation":[{"name":"Kartal Dr. L\u00fctfi K\u0131rdar City Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"ILGIN","sequence":"additional","affiliation":[{"name":"Istanbul University"},{"name":"School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"EME\u00c7","sequence":"additional","affiliation":[{"name":"Istanbul Nisantasi University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hacer","family":"\u015eahika YILDIZ","sequence":"additional","affiliation":[{"name":"Kartal Dr. L\u00fctfi K\u0131rdar City Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonca","family":"G\u00fcl GE\u00c7MEN","sequence":"additional","affiliation":[{"name":"Kartal Dr. L\u00fctfi K\u0131rdar City Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamer","family":"BAYSAL","sequence":"additional","affiliation":[{"name":"Kartal Dr. L\u00fctfi K\u0131rdar City Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21820"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/ANNONC\/MDT205"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/S41401-020-0389-3"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/AAD648"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11748-015-0564-5\/METRICS"},{"issue":"1","key":"e_1_3_6_7_2","first-page":"56","article-title":"Lung Cancer: Diagnosis and Management","volume":"75","author":"Collins LG","year":"2007","unstructured":"Collins LG, Haines C, Perkel R, Enck RE. Lung Cancer: Diagnosis and Management. Am Fam Physician. 2007;75(1):56-63. https:\/\/www.aafp.org\/pubs\/afp\/issues\/2007\/0101\/p56.html. Accessed August 26, 2025.","journal-title":"Am Fam Physician"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/IJMS22168661"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/IJMS21134569"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/FONC.2022.875761\/FULL"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1101\/CSHPERSPECT.A039537"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.EJCA.2011.11.036"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/S00330-011-2319-8\/METRICS"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1148\/RADIOL.13130110"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.CMPB.2025.109137"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.21037\/QIMS-24-734\/COIF"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/S12194-017-0433-2\/METRICS"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.3389\/FONC.2016.00071\/BIBTEX"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1371\/JOURNAL.PDIG.0001246"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.1136\/BMJ-2023-078378"},{"issue":"1","key":"e_1_3_6_21_2","first-page":"39","article-title":"The 2015 World Health Organization Classification of lung tumors: new entities since the 2004 Classification","volume":"110","author":"Mengoli MC","year":"2018","unstructured":"Mengoli MC, Longo FR, Fraggetta F, et al. The 2015 World Health Organization Classification of lung tumors: new entities since the 2004 Classification. Pathologica - Journal of the Italian Society of Anatomic Pathology and Diagnostic Cytopathology. 2018;110(1):39-67. https:\/\/old.pathologica.it\/article\/view\/39. Accessed September 12, 2025.","journal-title":"Pathologica - Journal of the Italian Society of Anatomic Pathology and Diagnostic Cytopathology"},{"key":"e_1_3_6_22_2","doi-asserted-by":"publisher","DOI":"10.21037\/TLCR-2025-187\/COIF"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.JLB.2024.100153"},{"key":"e_1_3_6_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/BIOM15010081"},{"key":"e_1_3_6_25_2","doi-asserted-by":"publisher","DOI":"10.1159\/000544892"},{"issue":"11","key":"e_1_3_6_26_2","article-title":"Comparison of the CatBoost Classifier with other Machine Learning Methods","volume":"11","author":"Ibrahim AA","year":"2020","unstructured":"Ibrahim AA, Ridwan RL, Muhammed MM, Abdulaziz RO, Saheed GA. Comparison of the CatBoost Classifier with other Machine Learning Methods. IJACSA) International Journal of Advanced Computer Science and Applications. 2020;11(11). www.ijacsa.thesai.org. Accessed September 12, 2025.","journal-title":"IJACSA) International Journal of Advanced Computer Science and Applications"},{"key":"e_1_3_6_27_2","doi-asserted-by":"publisher","DOI":"10.38094\/jastt20165"},{"key":"e_1_3_6_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIPCN63822.2024.00060"},{"key":"e_1_3_6_29_2","doi-asserted-by":"publisher","DOI":"10.1155\/BMRI\/6666688"},{"key":"e_1_3_6_30_2","doi-asserted-by":"publisher","DOI":"10.35882\/JEEEMI.V6I2.382"},{"key":"e_1_3_6_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.JTHO.2021.11.003"},{"key":"e_1_3_6_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10916-025-02156-5"},{"key":"e_1_3_6_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2021.100300"},{"key":"e_1_3_6_34_2","doi-asserted-by":"publisher","DOI":"10.17179\/EXCLI2020-3095"},{"key":"e_1_3_6_35_2","unstructured":"Computed Tomographic Screening for Lung Cancer The Relationship of Disease Stage to Tumor Size The International Early Lung Cancer Action Program Investigators*. Accessed September 12 2025.https:\/\/jamanetwork.com\/"},{"key":"e_1_3_6_36_2","doi-asserted-by":"publisher","DOI":"10.1097\/COC.0000000000001284"},{"key":"e_1_3_6_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccm.2019.11.001"},{"key":"e_1_3_6_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.CCM.2011.08.005\/ASSET\/7134EFA8-235D-4BD6-A7D7-EAB20B5DCE04\/MAIN.ASSETS\/GR11.JPG"},{"key":"e_1_3_6_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.10.013"},{"key":"e_1_3_6_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/APP12125829"},{"key":"e_1_3_6_41_2","doi-asserted-by":"publisher","DOI":"10.3389\/FONC.2021.658887\/TEXT"},{"key":"e_1_3_6_42_2","doi-asserted-by":"publisher","DOI":"10.3389\/FONC.2017.00193"},{"key":"e_1_3_6_43_2","doi-asserted-by":"publisher","DOI":"10.3978\/J.ISSN.2072-1439.2013.08.43"},{"key":"e_1_3_6_44_2","doi-asserted-by":"publisher","DOI":"10.21037\/ATM-22-2918"}],"container-title":["Technology in Cancer Research &amp; Treatment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15330338261483507","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15330338261483507","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15330338261483507","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:18:53Z","timestamp":1787923133000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15330338261483507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":43,"alternative-id":["10.1177\/15330338261483507"],"URL":"https:\/\/doi.org\/10.1177\/15330338261483507","relation":{},"ISSN":["1533-0346","1533-0338"],"issn-type":[{"value":"1533-0346","type":"print"},{"value":"1533-0338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]},"article-number":"15330338261483507"}}