A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram

Yang, Xia; Xu, Pan; Liu, Hui; Gao, David Yang; He, Jianxing; Liang, Wenhua; Guan, Yubao

doi:10.21037/jtd.2018.03.126

Cited by 63 publications

(66 citation statements)

References 33 publications

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“…(6) The ROI included bronchi, blood vessels, and vacuoles within the nodules, excluding normal lung tissue. Previous studies [16][17][18] had reported that the radiomic analyses were capable of predicting the LNM in the patients with lung adenocarcinoma. The predicative AUC values based on the radiomic analyses regarding the patients who had both pre-surgical node-positive and node-negative in the CT scans were 0.86 from 159 patients [16].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies [16][17][18] had reported that the radiomic analyses were capable of predicting the LNM in the patients with lung adenocarcinoma. The predicative AUC values based on the radiomic analyses regarding the patients who had both pre-surgical node-positive and node-negative in the CT scans were 0.86 from 159 patients [16]. For CT-reported N0 adenocarcinoma patients, the predictive AUC values were 0.91 from 492 patients [17] and 0.76 from 153 patients [18] based on the radiomic analyses, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…A few retrospective studies established that radiomic analyses could predict LNM using CT scans in both pre-surgically node-positive and node-negative lung adenocarcinoma patients [16][17][18]. We have used only the pre-surgical CT-based stage IA (imaging node-negative) NSCLC patients to predict LNM in this study.…”

Section: Introductionmentioning

confidence: 99%

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Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

Cong

Feng

Ren³

et al. 2020

Lung Cancer

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To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. Methods: This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. Results: The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both < 0.001). Conclusion: A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

Cong

Feng

Ren³

et al. 2020

Lung Cancer

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“…Many studies have shown that radiomics features have great potential to be the maker for tumor phenotype (8)(9)(10)(11)(12)(13)(14)(15)(16)(17), and found Adc can be differentiated from Sqc by radiomics (17)(18)(19)(20)(21)(22)(23). However, The data sets of those studies only included Adc and Sqc, that is to say, the accuracy of those models will be affected by other histological subtypes of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Yan

Wang

2020

Front. Oncol.

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Purpose: To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET. Methods: Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics. Results: The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR fourclass classification model showed 91% Acc and 0.89 Kappa. Conclusion: The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.

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“…12,13 Moreover, some studies have shown that radiomics can predict mediastinal involvement after extracting the data of the ROI from the primary tumour instead of the lymph nodes. 13,14 Magnetic resonance imaging…”

Section: Image-based Techniques Computed Tomographymentioning

confidence: 99%

Preoperative staging of the mediastinum is an essential and multidisciplinary task

Sanz-Santos

Call

2020

Respirology

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Mediastinal staging is a crucial step in the management of patients with NSCLC. With the recent development of novel techniques, mediastinal staging has evolved from an activity of interest mainly for thoracic surgeons to a joint effort carried out by many specialists. In this regard, the debate of cases in MDT sessions is crucial for optimal management of patients. Current evidencebased clinical guidelines for preoperative NSCLC staging recommend that mediastinal staging should be performed with increasing invasiveness. Image-based techniques are the first approach, although they have limited accuracy and findings must be confirmed by pathology in almost all cases. In this setting, the advent of radiomics is promising. Invasive staging depends on procedural factors rather than diagnostic performance. The choice between endoscopy-based or surgical procedures should depend on the local expertise of each centre. As the extension of mediastinal disease in terms of number of involved lymph nodes and nodal stations affects prognosis and the choice of treatment, systematic samplings are preferred over random targeted samplings. Following this approach, a diagnosis of single mediastinal nodal involvement can be unreliable if all reachable mediastinal nodal stations have not been assessed. The performance of confirmatory mediastinoscopy after a negative endoscopy-based procedure is controversial but currently recommended. Current indications of invasive staging in patients with radiologically normal mediastinum have to be re-evaluated, especially for central tumour location.

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A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram

Abstract: The radiomics nomogram, based on preoperative CT images, can be used as a noninvasive method to predict LNM in patients with solid lung adenocarcinoma.

Cited by 63 publications

References 33 publications

Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Preoperative staging of the mediastinum is an essential and multidisciplinary task

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