Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

Li, Yi; Peng, Zhiwei; Chen, Zhiyong; Tao, Yahong; Lin, Ze; He, Anjing; Jin, Mengni; Peng, Yun; Zhong, Yanjun; Yan, Huimin; Zuo, Minjing

doi:10.3389/fonc.2022.924055

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…Implementing CBCT in clinical practice represented one of the last decade’s most critical advances in interventional pulmonology. This type of technology uses a rotating C-arm that generates intraprocedural images with a quality almost similar to a chest CT [ 108 , 109 , 110 ]. CBCT provides 3D reconstructions by combining coronal, sagittal, and axial views, which are invaluable during navigation.…”

Section: Endoscopic Identification Of a Pplmentioning

confidence: 99%

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Fantin¹,

Manera

Patruno³

et al. 2023

Life

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Peripheral pulmonary lesions (PPLs) are frequent incidental findings in subjects when performing chest radiographs or chest computed tomography (CT) scans. When a PPL is identified, it is necessary to proceed with a risk stratification based on the patient profile and the characteristics found on chest CT. In order to proceed with a diagnostic procedure, the first-line examination is often a bronchoscopy with tissue sampling. Many guidance technologies have recently been developed to facilitate PPLs sampling. Through bronchoscopy, it is currently possible to ascertain the PPL’s benign or malignant nature, delaying the therapy’s second phase with radical, supportive, or palliative intent. In this review, we describe all the new tools available: from the innovation of bronchoscopic instrumentation (e.g., ultrathin bronchoscopy and robotic bronchoscopy) to the advances in navigation technology (e.g., radial-probe endobronchial ultrasound, virtual navigation, electromagnetic navigation, shape-sensing navigation, cone-beam computed tomography). In addition, we summarize all the PPLs ablation techniques currently under experimentation. Interventional pulmonology may be a discipline aiming at adopting increasingly innovative and disruptive technologies.

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Section: Endoscopic Identification Of a Pplmentioning

confidence: 99%

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Fantin¹,

Manera

Patruno³

et al. 2023

Life

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“…Radiomics can extract a large number of high-dimensional imaging features and convert this imaging information into quantitative parameters for analysis and modeling [ 14 ], which may serve as a noninvasive method to support personalized clinical decision-making. Previous studies have revealed that radiomics has great potential to support radiologists in identifying benign and malignant solid pulmonary nodules [ 11 , 15 – 18 ], but few studies have investigated the performance of enhanced CT radiomics in differentiating malignant from benign SSPNs. We aimed to explore the value of enhanced CT-based radiomics in discriminating malignant from benign SSPNs, to develop a combined model based on clinical and radiomics features for the differential diagnosis of SSPNs in the clinic.…”

Section: Introductionmentioning

confidence: 99%

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

Liu,

Qi,

Wang

et al. 2024

Eur Radiol Exp

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Background We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs). Methods A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC). Results Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918–0.966) in the training group and an AUC of 0.930 (0.902–0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value. Conclusions The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. Relevance statement The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. Key points • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules. Graphical Abstract

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“…Recently, radiomics has already been applied for the identification of malignancy [ 15 ] and histological subtypes [ 16 ], prediction of gene expression [ 17 ], and assessment of treatment response in lung cancer [ 18 ]. Radiomic features can be extracted from different regions of interest (ROIs) such as the intratumoral and/or peritumoral areas [ 19 – 22 ]. For example, Das SK et al improved the performance of predicting cT1N0M0 lung adenocarcinoma by combining features of the intratumor region, the peritumoral region and lymph node [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

Wu,

Gao,

Lou

et al. 2024

BMC Pulm Med

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Background The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. Methods Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. Results Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73–0.91]), 0.88 (95% CI [0.81–0.93]), and 0.93(95% CI [0.90–0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82–0.94]), 0.86 (95% CI [0.74–0.93]) and 0.94 (95% CI [0.91–0.96]), respectively. Conclusions Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. Trial registration CRD42022375712.

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Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

Cited by 6 publications

References 42 publications

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

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