A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma

Zha, Xinyi; Liu, Yuanqing; Ping, Xiaoxia; Bao, Jiayi; Wu, Qian; Hu, Su; Hu, Chunhong

doi:10.3389/fonc.2022.876264

Cited by 12 publications

(9 citation statements)

References 45 publications

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“…Eventually, 10 optimal quantitative radiomic features were extracted. This study covered first-to high-order texture features which is partially consistent with a previous study by Wei et al, 28 suggesting some similarities between the two studies regarding texture features, but not in agreement with the study by Zha et al 15 According to our study, the model combination with radiomic and clinical features is more effective. Due to the difficulty in delineating peritumoral ROI for the reason of the proximity of the tumor to the pleura, we only annotated the interior VOI.…”

Section: Discussionsupporting

confidence: 90%

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Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

Wang,

Pan,

Zhang

et al. 2023

Thoracic Cancer

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BackgroundTo develop and validate a preoperative nomogram model combining the radiomics signature and clinical features for preoperative prediction of visceral pleural invasion (VPI) in lung nodules presenting as part‐solid density.MethodsWe retrospectively reviewed 156 patients with pathologically confirmed invasive lung adenocarcinomas after surgery from January 2016 to August 2019. The patients were split into training and validation sets by a ratio of 7:3. The radiomic features were extracted with the aid of FeAture Explorer Pro (FAE). A CT‐based radiomics model was constructed to predict the presence of VPI and internally validated. Multivariable regression analysis was conducted to construct a nomogram model, and the performance of the models were evaluated with the area under the receiver operating characteristic curve (AUC) and compared with each other.ResultsThe enrolled patients were split into training (n = 109) and validation sets (n = 47). A total of 806 features were extracted and the selected 10 optimal features were used in the construction of the radiomics model among the 707 stable features. The AUC of the nomogram model was 0.888 (95% CI: 0.762–0.961), which was superior to the clinical model (0.787, 95% CI: 0.643–0.893; p = 0.049) and comparable to the radiomics model (0.879, 95% CI: 0.751–0.965; p > 0.05). The nomogram model achieved a sensitivity of 90.5% and a specificity of 76.9% in the validation dataset.ConclusionsThe nomogram model could be considered as a noninvasive method to predict VPI with either highly sensitive or highly specific diagnoses depending on clinical needs.

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Section: Discussionsupporting

confidence: 90%

“…The studies by Yuan et al 14 and Zha et al 15 attempted to derive automated quantitative imaging features to reach a noninvasive preoperative diagnosis for lung cancer with VPI hybridizing pure‐solid and solid lung lesions. Few studies have investigated radiomic signatures in only part‐solid lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

Wang,

Pan,

Zhang

et al. 2023

Thoracic Cancer

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“…In comparison with histogram and texture analysis, radiomics provide more high-dimensional and otherwise unrecognizable in-depth information and can comprehensively capture the characteristics of tumor heterogeneity in images. At present, there are only two researches on the use of radiomics to predict VPI ( 36 , 37 ). Yuan et al ( 36 ) analyzed the CT imaging characteristics of 327 cases of early lung adenocarcinoma with a maximum diameter ≤3 cm and found that several imaging characteristics (e.g., percentile 10%, WavEnLLS_2, and S-0_1SumAverage) reflected tumor heterogeneity and that differed significantly between the VPI positive and negative groups.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the radiomics model was constructed based on only the tumor and not the combined CT morphological features. Zha et al ( 37 ) excluded the pure GGN type and included a total of 659 cases of IA lung adenocarcinoma. Based on the tumor imaging features and CT signs, a comprehensive prediction model of VPI was constructed, with an AUC value of 0.89 in the training set and 0.88 in the internal verification set.…”

Section: Discussionmentioning

confidence: 99%

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review

Wang,

Lyu,

Fan

et al. 2024

Transl Cancer Res

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Background and Objective In lung cancer, visceral pleural invasion (VPI) affects the selection of surgical methods, the scope of lymph node dissection and the need for adjuvant chemotherapy. Preoperative or intraoperative prediction and diagnosis of VPI of lung cancer is helpful for choosing the best treatment plan and improving the prognosis of patients. This review aims to summarize the research progress of the clinical significance of VPI assessment, the intraoperative diagnosis technology of VPI, and various imaging methods for preoperative prediction of VPI. The diagnostic efficacy, advantages and disadvantages of various methods were summarized. The challenges and prospects for future research will also be discussed. Methods A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting VPI. PubMed database was being examined and the last run was on 4 August 2022. Key Content and Findings The pathological diagnosis and clinical significance of VPI of lung cancer were discussed in this review. The research progress of prediction and diagnosis of VPI in recent years was summarized. The results showed that preoperative imaging examination and intraoperative freezing pathology were of great value. Conclusions VPI is one of the adverse prognostic factors in patients with lung cancer. Accurate prediction of VPI status before surgery can provide guidance and help for the selection of clinical operation and postoperative treatment. There are some advantages and limitations in predicting VPI based on traditional computed tomography (CT) signs, 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET)/CT and magnetic resonance imaging (MRI) techniques. As an emerging technology, radiomics and deep learning show great potential and represent the future research direction.

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“…The 10-fold cross-testing was used to identify the optimal regularization parameter λ. The radiomics score (R-score) was calculated based on the fitting formula of the radiomics signature for all patients ( 17 ). The predictive accuracy of the radiomics signature was evaluated in both sets.…”

Section: Methodsmentioning

confidence: 99%

A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer

Ma¹,

Cui²,

Wang³

et al. 2022

Transl Cancer Res

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Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is very important for appropriate management of advanced gastric cancer (AGC) patients. In this study, we aimed to develop and validate a computed tomography (CT)-based radiomics signature for preoperative prediction of HER2 overexpression and treatment efficacy of trastuzumab in AGC. Methods:We retrospectively enrolled 536 consecutive AGC patients (median age, 59 years; interquartile range, 52-65 years; 377 male, 159 female) and separated them into a training set (n=357) and a testing set (n=179). Radiomic features were extracted from 3 different phase images of contrast-enhanced CT scans, and a radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. The predictive performance of the radiomics signature was assessed in the training and testing sets. Univariable and multivariable logistical regression analyses were used to identify independent risk factors of HER2 overexpression. Univariable and multivariable Cox regression analyses were used to identify the risk factors of overall survival (OS) and progression-free survival (PFS).The predictive value of the radiomics signature for treatment efficacy of trastuzumab was also evaluated. Results:The radiomics signature comprised eight robust features that demonstrated good discrimination ability for HER2 overexpression in the training set [area under the curve (AUC) =0.85] and the testing set (AUC =0.81). Multivariable Cox regression analysis revealed that the radiomics signature was an independent risk factor for OS [hazard ratio (HR) =2.01, P=0.001] and PFS (HR =1.32, P=0.01). The radiomics score of patients who achieved disease control was significantly lower than that of patients with progressive disease (P=0.023). Conclusions:The proposed radiomics signature showed favorable accuracy for prediction of HER2 overexpression and prognosis in AGC. It has promising potential as a noninvasive approach for selecting patients for target therapy.

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A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma

Cited by 12 publications

References 45 publications

Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review

A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer

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