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The study applies a computer-assisted histogram analysis of Hounsfield unit (HU) values from manually defined regions of interest. In an exploratory retrospective control setting with 20 patients, summary HU statistics guided classification and highlighted malignant ranges, aiming to reduce reliance on invasive biopsy.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/quantitative-analysis-of-lung-lesions-using-unenhanced-chest-computed-tomography-images-original-article-abstract-introduction-methods-results-conclusion/330563/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/quantitative-analysis-of-lung-lesions-using-unenhanced-chest-computed-tomography-images-original-article-abstract-introduction-methods-results-conclusion/330563.png","ImageObject",300,407,{"name":92,"@type":93},"Aurora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is it difficult to differentiate malignant from benign lung nodules on unenhanced CT?","Question",{"text":112,"@type":113},"Malignant and benign nodules can look very similar on chest CT without specific visual imaging features. This limits the ability to judge malignancy reliably by visual inspection alone.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What quantitative approach is used in the study?",{"text":117,"@type":113},"The study uses histogram analysis of Hounsfield unit (HU) values. Mean, minimum, maximum, and standard deviations are computed from a manually placed region of interest across slices.",{"name":119,"@type":110,"acceptedAnswer":120},"What HU ranges were associated with malignant lesions in the results?",{"text":121,"@type":113},"Histogram analysis indicated that more malignant lesions often show a mean HU between 30 and 50, a maximum HU below 150, and a minimum HU between -30 and 20. 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Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy.  \nMethods: In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar inchest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA.  \nResults: HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between 􀀁30 and 20 . Lesions outside these ranges were mostly benign. Conclusion: Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.  \nKEYWORDS  \nchest CT, lung, pulmonary nodules, radiology  \nmortality in both genders in many countries, including in Iran. Although several known risk factors for lung can-  \nPulmonary neoplasms constitute a significant health problem that causes the highest number of cancer-related  \ncers can be used for risk stratification and choosing the population at most risk, unlike colorectal and breast  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd.  \nClin Respir J. 2024;18:e13759 .  \n[https://doi.org/10.1111/crj.13759](https://doi.org/10.1111/crj.13759)  \n[wileyonlinelibrary.com/journal/crj](wileyonlinelibrary.com/journal/crj)  \n1 of 8  \ncancers, there is no efficient method to screen for lung cancers. Most of these malignancies are discovered in moderate or late stages.1 However, some screening programs have been introduced that cover people with highrisk profiles, such as a history of smoking and population that is older than 55 years. These programs are to be beneficial as they help early diagnosis.2–4  \nDiagnosis of pulmonary cancers relies primarily on computed tomography (CT) scans.4 Furthermore, pulmonary nodules can often be found i","cbCaijTZKAaVQ2LV","https://ap.wps.com/l/cbCaijTZKAaVQ2LV","pdf",2104062,"English","# Abstract\n# Introduction\n## Diagnostic challenge of pulmonary nodules\n## Role of CT texture and CAD\n## Hounsfield unit and quantitative approach\n# Methods\n# Results\n# Conclusion","[{\"question\":\"Why is it difficult to differentiate malignant from benign lung nodules on unenhanced CT?\",\"answer\":\"Malignant and benign nodules can look very similar on chest CT without specific visual imaging features. This limits the ability to judge malignancy reliably by visual inspection alone.\"},{\"question\":\"What quantitative approach is used in the study?\",\"answer\":\"The study uses histogram analysis of Hounsfield unit (HU) values. Mean, minimum, maximum, and standard deviations are computed from a manually placed region of interest across slices.\"},{\"question\":\"What HU ranges were associated with malignant lesions in the results?\",\"answer\":\"Histogram analysis indicated that more malignant lesions often show a mean HU between 30 and 50, a maximum HU below 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign.\"}]","Quantitative analysis of lung lesions using unenhanced chest computed tomography images - Original Article - Abstract, Introduction, Methods, Results, Conclusion | PDF",1789989425]