CT-Based Quantitative Analysis for Early Detection of Pulmonary Nodules Associated With Lung Cancer

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Published: 2026-05-15
DOI: https://doi.org/10.61919/axsfms53
Keywords:
Computed Tomography, Lung Cancer, Pulmonary Nodule, Early Detection, Benign, Malignant

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Musfirah Khalid
Al Razi Institute, Lahore, Pakistan
Hafiza Maria Fawad
Head of Radiography & Imaging Technology, Al Razi Institute, Lahore, Pakistan
Tayyaba Iqbal
Lecturer of Radiography & Imaging Technology, Al Razi Institute, Lahore, Pakistan
Syeda Eman Shah
Al Razi Institute, Lahore, Pakistan
Atiqa Aziz
Al Razi Institute, Lahore, Pakistan
M. Noor ul Hassan
Al Razi Institute, Lahore, Pakistan
Hira Nadeem
Al Razi Institute, Lahore, Pakistan

Abstract

Background: Pulmonary nodules are commonly detected on chest computed tomography (CT), but differentiating benign from malignant nodules remains clinically challenging because many nodules are benign while some malignant lesions may be missed or require delayed confirmation. Objective: To evaluate CT-based quantitative and morphological characteristics of pulmonary nodules and examine their association with tumor status among patients with suspected lung cancer-related nodules. Methods: This cross-sectional observational study was conducted at Lahore General Hospital over 90 days after synopsis approval. Sixty adult patients with CT-detected pulmonary nodules and adequate image quality were included. CT-based variables included nodule size, volume, shape, location, margin characteristics, pleural effusion, lymph node involvement, nodule type, and tumor status. Continuous variables were summarized as mean ± standard deviation, categorical variables as frequencies and percentages, and associations between categorical CT features and tumor status were assessed using chi-square tests. Results: The mean age was 53.68 ± 15.62 years. Mean nodule size was 17.07 ± 7.51 mm, and mean nodule volume was 286.72 ± 158.91 mm³. Malignant nodules accounted for 51.7% of cases. Margin characteristics showed the strongest association signal with tumor status (χ² = 5.792, p = 0.055), while shape, location, pleural effusion, and lymph node involvement were not statistically significant. Conclusion: CT-based assessment provides useful morphological and quantitative information for pulmonary nodule evaluation, with margin characteristics showing the most clinically relevant trend. Larger studies with diagnostic accuracy analysis are required

Article Details

Issue

Vol. 4 No. 9 (2026): Volume 4, Issue 9 (May 2026)

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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

1.
Musfirah Khalid, Hafiza Maria Fawad, Tayyaba Iqbal, Syeda Eman Shah, Atiqa Aziz, M. Noor ul Hassan, et al. CT-Based Quantitative Analysis for Early Detection of Pulmonary Nodules Associated With Lung Cancer. JHWCR [Internet]. 2026 May 15 [cited 2026 Jun. 16];4(9):1-9. Available from: https://jhwcr.com/index.php/jhwcr/article/view/1792

References

1. Lanjewar MG, Panchbhai KG, Charanarur P. Lung cancer detection from CT scans using modified DenseNet with feature selection methods and ML classifiers. Expert Syst Appl. 2023;224:119961.

2. Makaju S, Prasad PWC, Alsadoon A, Singh AK, Elchouemi A. Lung cancer detection using CT scan images. Procedia Comput Sci. 2018;125:107-114.

3. Song Z, Song B, Abd Al Rahman M. LNA-Net: Enhancing detection and classification of benign and malignant pulmonary nodules in CT scans. Biomed Signal Process Control. 2026;117:109505.

4. Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017;6(1):86-91.

5. Liang X, Kong Y, Shang H, Yang M, Lu W, Zeng Q, et al. Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals. J Cancer Res Ther. 2022;18(7):2041-2048.

6. Shariff V, Paritala C, Ankala KM. Optimizing non-small cell lung cancer detection with convolutional neural networks and differential augmentation. Sci Rep. 2025;15(1):15640.

7. González Maldonado S, Delorme S, Hüsing A, Motsch E, Kauczor HU, Heussel CP, Kaaks R. Evaluation of prediction models for identifying malignancy in pulmonary nodules detected via low-dose computed tomography. JAMA Netw Open. 2020;3(2):e1921221.

8. Heuvelmans MA, Walter JE, Peters RB, de Bock GH, Yousaf-Khan U, van der Aalst CM, et al. Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: the NELSON study. Lung Cancer. 2017;113:45-50.

9. Duan L, Shan W, Bo G, Lu G, Guo L. Qualitative and quantitative values of the Lung-RADS and computed tomography in diagnosing solitary pulmonary nodules. Diagnostics. 2022;12(11):2699.

10. Dennie C, Thornhill R, Sethi-Virmani V, Souza CA, Bayanati H, Gupta A, Maziak D. Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg. 2016;6(1):6-15.

11. Peters AA, Weinheimer O, von Stackelberg O, Kroschke J, Piskorski L, Debic M, et al. Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules. Eur Radiol. 2023;33(6):3908-3917.

12. Shakeel PM, Burhanuddin MA, Desa MI. Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement. 2019;145:702-712.