Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Erdoğdu, Eren; Özkan, Berker; Duman, Salih; Ağkoç, Melek; Ertürk, Şükrü Mehmet; Kara, Murat; Toker, Alper

doi:10.1111/crj.13489

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…He et al [20] proposed an ISHAP-based interpretation model-guided classification method to predict tumor signs. Erdogdu et al [21] used morphology-isolated pulmonary nodules to predict malignant tumors.…”

Section: Introductionmentioning

confidence: 99%

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

Zhang

Qiu

Cui

et al. 2022

BioMed Research International

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Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.

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Section: Introductionmentioning

confidence: 99%

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

Zhang

Qiu

Cui

et al. 2022

BioMed Research International

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Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Erdoğdu

Özkan

Duman

et al. 2022

Clinical Respiratory J

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Background The management of a solitary pulmonary nodule is a challenging issue in pulmonary disease. Although many factors have been defined as predictors for malignancy in solitary pulmonary nodules, the accurate diagnosis can only be established with the permanent histological diagnosis. Objective We tried to clarify the possible predictors of malignancy in solitary pulmonary nodules in patients who had definitive histological diagnosis. Methods We made a retrospective study to collect the data of patients with solitary pulmonary nodules who had histological diagnosis either before or after surgery. We made a statistical analysis of both the clinic and radiological features of these nodules with respect to malignancy both in contingency tables and with logistic regression analysis. Results We had a total of 223 patients with a radiological diagnosis of solitary pulmonary nodule. Age, smoking status and pack years of smoking, maximum standardized uptake value (SUVmax), and radiological features such as solid component, spiculation, pleural tag, lobulation, calcification, and higher density were significant predictors of malignancy in contingency tables. Age, smoking status and smoking (pack/year), SUVmax, and radiological features including spiculation, pleural tag, lobulation, calcification, and higher density were the significant predictors in univariate analysis. However, multivariate analysis revealed only SUVmax greater than 2.5 (p < 0.0001), spiculation (p = 0.009), and age older than 61 years (p = 0.015) as the significant predictors for malignancy. Conclusion Age, SUVmax, and spiculation are the independent predictors of malignancy in patients with solitary pulmonary nodules.

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Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule

Corica

Feo

Stazza

et al. 2023

Cancers

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This study aims to evaluate the reliability of qualitative and semiquantitative parameters of 18F-FDG PET-CT, and eventually a correlation between them, in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. A total of 146 patients were retrospectively studied according to their pre-test probability of malignancy (all patients were intermediate risk), based on radiological features and risk factors, and qualitative and semiquantitative parameters, such as SUVmax, SUVmean, TLG, and MTV, which were obtained from the FDG PET-CT scan of such patients before diagnosis. It has been observed that visual analysis correlates well with the risk of malignancy in patients with SPN; indeed, only 20% of SPNs in which FDG uptake was low or absent were found to be malignant at the cytopathological examination, while 45.45% of SPNs in which FDG uptake was moderate and 90.24% in which FDG uptake was intense were found to be malignant. The same trend was observed evaluating semiquantitative parameters, since increasing values of SUVmax, SUVmean, TLG, and MTV were observed in patients whose cytopathological examination of SPN showed the presence of lung cancer. In particular, in patients whose SPN was neoplastic, we observed a median (MAD) SUVmax of 7.89 (±2.24), median (MAD) SUVmean of 3.76 (±2.59), median (MAD) TLG of 16.36 (±15.87), and a median (MAD) MTV of 3.39 (±2.86). In contrast, in patients whose SPN was non-neoplastic, the SUVmax was 2.24 (±1.73), SUVmean 1.67 (±1.15), TLG 1.63 (±2.33), and MTV 1.20 (±1.20). Optimal cut-offs were drawn for semiquantitative parameters considered predictors of malignancy. Nodule size correlated significantly with FDG uptake intensity and with SUVmax. Finally, age and nodule size proved significant predictors of malignancy. In conclusion, considering the pre-test probability of malignancy, qualitative and semiquantitative parameters can be considered reliable tools in patients with SPN, since cut-offs for SUVmax, SUVmean, TLG, and MTV showed good sensitivity and specificity in predicting malignancy.

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Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Cited by 3 publications

References 25 publications

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule

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