Quantitative analysis of lung lesions using unenhanced chest computed tomography images - Original Article - Abstract, Introduction, Methods, Results, Conclusion

Sep 21, 2026

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Quantitative analysis of lung lesions on unenhanced chest computed tomography (CT) addresses the difficulty of distinguishing malignant from benign pulmonary nodules using visual imaging features alone. 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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Introduction

Diagnostic challenge of pulmonary nodules

Role of CT texture and CAD

Hounsfield unit and quantitative approach

Methods

Results

Conclusion

DOI: 10.1111/crj.13759 ORIGINAL ARTICL E Quantitative analysis of lung lesions using unenhanced chest computed tomography images Fariba Zarei 1,2 | Payam Jannatdoust 3 | Siamak Malekpour 2 | Mahshad Razaghi 4 | Sabyasachi Chatterjee 5 | Vani Varadhan Chatterjee 6 | Amirbahador Abbasi 4 | Rezvan Ravanfar Haghighi 1 1Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran 2Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran 3School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 4Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran 5Ongil (or Retired Scientist From Indian Institue of Astrophysics, Bengluru), Salem, India 6Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru, India Correspondence Rezvan Ravanfar Haghighi, Medical Imaging Research Center, 8th Floor, Research Tower, Khalili Street, Shiraz 7193635899, Iran. Email: [sravanfarr@gmail.com](sravanfarr@gmail.com) Funding information The Shiraz University of Medical Sciences supported the financials of this study. 1 | INTRODUCTION Abstract Introduction: Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. 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. Methods: 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. Results: 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. KEYWORDS chest CT, lung, pulmonary nodules, radiology mortality in both genders in many countries, including in Iran. Although several known risk factors for lung can- Pulmonary neoplasms constitute a significant health problem that causes the highest number of cancer-related cers can be used for risk stratification and choosing the population at most risk, unlike colorectal and breast This 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. © 2024 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd. Clin Respir J. 2024;18:e13759 . [https://doi.org/10.1111/crj.13759](https://doi.org/10.1111/crj.13759) [wileyonlinelibrary.com/journal/crj](wileyonlinelibrary.com/journal/crj) 1 of 8 cancers, 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 Diagnosis of pulmonary cancers relies primarily on computed tomography (CT) scans.4 Furthermore, pulmonary nodules can often be found i

FAQs

Why is it difficult to differentiate malignant from benign lung nodules on unenhanced CT?

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.

What quantitative approach is used in the study?

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.

What HU ranges were associated with malignant lesions in the results?

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.

Sep 21, 2026

Uploaded by Aurora