Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer

Erol, Mustafa; Önner, Hasan; Küçükosmanoğlu, İlknur

doi:10.1007/s13139-022-00774-2

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Another noteworthy feature in our study was ’busyness,’ which is associated with the spatial frequency of intensity changes. Erol M et al [ 30 ] reported that radiomic features, including busyness, were independently correlated with the staging of lung squamous cancer. Bashir et al [ 13 ] and Hyun et al [ 16 ] have previously explored the application of radiomics in the classification of NSCLC.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Zhang,

Liu,

Chang

et al. 2024

PLoS ONE

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Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

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Section: Discussionmentioning

confidence: 99%

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Zhang,

Liu,

Chang

et al. 2024

PLoS ONE

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“…First, adenocarcinoma is the most common histologic type in NSCLC [12]. In addition, metabolic pattern and characteristics represented on FDG uptake differ between adenocarcinoma and squamous cell carcinoma [13][14][15]. Differences in prognosis and treatment options, such as targeted therapy, increase the necessity of separate analysis for adenocarcinoma [16,17].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Significance of Volumetric Parameters Based on FDG PET/CT in Patients with Lung Adenocarcinoma Undergoing Curative Surgery

Lee,

Choi,

Kim

et al. 2023

Cancers

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Introduction: FDG PET/CT is a robust imaging modality to diagnose and stratify prognoses for non-small cell lung carcinoma. However, the role of FDG PET/CT in operable lung adenocarcinoma patients has not been previously investigated in a large cohort with varying pathological stages. The prognostic value of volumetric parameters based on FDG PET/CT was investigated in patients with stage I–III lung adenocarcinoma receiving curative surgery. Methods: This retrospective study included 432 patients with lung adenocarcinoma undergoing preoperative FDG PET/CT between January 2016 and December 2017. Clinicopathologic variables, conventional image parameters, such as the maximum standardized uptake value (SUVmax) and mean SUV (SUVmean) of the primary tumor, and volumetric parameters, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were included in Cox regression analysis. Subgroup analysis was conducted to compare hazard ratios (HRs) based on MTV in each pathological stage. A new staging system including volumetric parameters was suggested. Results: A total of 432 patients (median age: 62 years; interquartile range: 56–70 years; 225 males) were evaluated. Sex, age, presence of EGFR mutation, pathological stage, MTV, and TLG of the primary tumor were selected as statistically significant prognostic factors for overall survival irrespective of other variables (OS; p < 0.05 for all). Pathological stage, MTV, and TLG of the primary tumor were selected as statistically significant prognostic factors for disease-free survival irrespective of other variables (p < 0.05 for all). The suggested new staging system including MTV as an additional criterion showed better discrimination and prediction for OS than the conventional pathological staging system despite statistical insignificance (concordance index: 0.698 vs. 0.673). Conclusions: The volumetric parameters of the primary tumor based on preoperative FDG PET/CT were independent prognostic factors in addition to pathological stage in patients with operable lung adenocarcinoma. The suggested new staging system considering MTV predicted the prognoses better than the conventional pathological staging system.

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[18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings—a bicenter retrospective study

Moon,

Song,

Kim

et al. 2024

Ann Nucl Med

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Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer

Cited by 5 publications

References 30 publications

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Prognostic Significance of Volumetric Parameters Based on FDG PET/CT in Patients with Lung Adenocarcinoma Undergoing Curative Surgery

[18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings—a bicenter retrospective study

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