{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:42:33Z","timestamp":1791499353773,"version":"4.3.4"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microscopy"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Background<\/jats:bold>\n                    : Accurate differentiation between non\u2010small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) is crucial for optimising treatment strategies and improving patient outcomes in lung cancer management. Early and precise classification supports tailored therapeutic decisions and enhances prognosis prediction.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Purpose<\/jats:bold>\n                    : This study develops a novel method to use machine learning models trained with physical science features extracted from pathological images to classify lung cancer into NSCLC and SCLC with high accuracy and robustness.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    : Physical science features were employed to acquire quantitative cellular microarchitecture of cancer cells from histopathological images. Random Forest algorithm was applied to identify the most informative 20 features. Next, the selected top features were used to train and evaluate four machine learning classifiers: Support Vector Machine (SVM), Gradient Boosting, Logistic Regression, and Decision Tree. The dataset included pathological images from 240 histologically confirmed lung cancer cases, divided randomly into training and validation sets (80% train, 20% test). Then, model performance was evaluated using accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), with robustness validation via fivefold cross\u2010validation.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    : Logistic Regression achieved the highest overall performance, with a median accuracy near 90% and an AUC consistently above 0.90 across fivefold cross\u2010validation. SVM and Gradient Boosting followed closely, each surpassing 0.90 in AUC, demonstrating reliable discrimination between NSCLC and SCLC. Decision Tree showed broader variability, though it maintained acceptable recall for SCLC. Random Forest feature selection revealed refractive index percentiles and polarisation histograms as top contributors to model performance.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions<\/jats:bold>\n                    : Machine learning models trained on physical science features have the potential to serve as a highly accurate and robust framework for differentiating NSCLC from SCLC.\n                  <\/jats:p>","DOI":"10.1111\/jmi.70090","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T08:49:01Z","timestamp":1775897341000},"page":"60-78","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate diagnosis of non\u2010small cell and small cell lung cancer by using machine learning models trained with physical science features extracted from pathological images"],"prefix":"10.1111","volume":"303","author":[{"given":"Wencheng","family":"Shao","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Bao","sequence":"additional","affiliation":[{"name":"Oncology Department the Second People's Hospital of Taizhou Affiliated to Medical College of Yangzhou University  Taizhou City Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xiang'an Hospital of Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-61101-7"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jncc.2024.01.006"},{"issue":"3","key":"e_1_2_9_5_1","first-page":"243","article-title":"China guideline for the screening and early detection of lung cancer (2021, Beijing)","volume":"43","author":"He J.","year":"2021","journal-title":"Zhonghua zhong liu za zhi [Chinese Journal of Oncology]"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"Liu J.(2024 August 13).AI\u2010enabled lung cancer diagnosis and treatment in China: Challenges opportunities and future prospects. In:Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science(pp.216\u2013222). 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He Y. &Ma H.(2009 February 12).Propagation of polarized light in anisotropic medium. In:Proceedings Volume 7175 Optical Interactions with Tissue and Cells XX(Vol.7175 pp.280\u2013285).SPIE.","DOI":"10.1117\/12.808390"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/14685248.2012.762455"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/0016-0032(53)90727-3"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.81.865"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1119\/5.0082472"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781138893603-7"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1021\/cr60220a002"},{"key":"e_1_2_9_23_1","doi-asserted-by":"crossref","unstructured":"Harris C. R. Millman K. J. van derWalt S. J. Gommers R. Virtanen P. Cournapeau D. Wieser E. Taylor J. Berg S. Smith N. J. Kern R. Picus M. Hoyer S. vanKerkwijk M. H. Brett M. Haldane A. Del R\u00edo J. F. Wiebe M. Peterson P. \u2026Oliphant T. E.(2020 September).Array programming with NumPy.Nature 585(7825) 357\u2013362.https:\/\/doi.org\/10.1038\/s41586\u2010020\u20102649\u20102. Epub 2020 Sep 16.","DOI":"10.1038\/s41586-020-2649-2"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/info11040193"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201009954\u20108"},{"issue":"13","key":"e_1_2_9_26_1","first-page":"1541","article-title":"Deep learning based on enhanced MRI T1 imaging to differentiate small\u2010cell and non\u2010small\u2010cell primary lung cancers in patients with brain metastases","volume":"19","author":"Sui L.","year":"2023","journal-title":"Current Medical Imaging Reviews"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"Coudray N. Ocampo P. S. Sakellaropoulos T. Narula N. Snuderl M. Feny\u00f6 D. Moreira A. L. Razavian N. &Tsirigos A.(2018 October).Classification and mutation prediction from non\u2010small cell lung cancer histopathology images using deep learning.Nature Medicine 24(10) 1559\u20131567.https:\/\/doi.org\/10.1038\/s41591\u2010018\u20100177\u20105. 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