Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?

Wang, Xiang; Zhao, Xingyu; Li, Qiong; Xia, Wei; Peng, Zhongmin; Zhang, Rui; Li, Qingchu; Jian, Junming; Wang, Wei; Tang, Yuguo; Liu, Shiyuan; Gao, Xin

doi:10.1007/s00330-019-06084-0

Cited by 121 publications

(106 citation statements)

References 52 publications

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“…Likewise, Weng et al (32) used two CT features (lesion shape and solid component size) to yield an AUC of 0.76. diagnosis of IA, which can be managed with follow-up CT rather than radical surgical intervention. Previous studies have reported that radiomic features extracted from the peritumoral region could provide additional information when predicting lymph node metastasis and abnormality type (17,18). Our results, however, indicate that the perinodular features do not contribute to radiomics model performance.…”

Section: Calibration and Radiomics Quality Scorecontrasting

confidence: 72%

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Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Wu¹,

Woodruff

Shen

et al. 2020

Radiology

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T he reduction of lung cancer mortality by almost 20% in the National Lung Screening Trial can be partially attributed to the extensive use of low-dose CT for lung cancer screening in high-risk populations, which led to the improved detection of pulmonary nodules and early stage lung cancers (1,2). Pulmonary nodules are classified as solid, pure ground-glass, and part-solid nodules (PSNs) based on CT phenotyping, with PSNs being an important cancer predictor in the Brock model that is widely used to assess the malignant risk of pulmonary nodules (3). Moreover, adenocarcinomas manifesting as PSNs have been suggested to be a distinct subtype, most of which are confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA) by abnormality, requiring a different management strategy due to different clinical-pathologic characteristics (4). Furthermore, evidence from histological specimens suggests that the solid components of lung nodules have a close-knit association with the invasive component of adenocarcinomas (5-7). Among the different subtypes of lung adenocarcinoma, IA has the worst prognosis, with the others having an almost 100% survival probability (8). Therefore, lobectomy is often recommended for patients with IA, whereas limited resections are suggested for patients with AIS or MIA (9).

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Section: Calibration and Radiomics Quality Scorecontrasting

confidence: 72%

“…Recent studies (15,16) show the potential of radiomics to distinguish AIS or MIA from IA in subsolid pulmonary nodules. Additionally, radiomic features extracted from the perinodular region have been examined for their diagnostic power (17,18).…”

Section: Key Resultsmentioning

confidence: 99%

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Wu¹,

Woodruff

Shen

et al. 2020

Radiology

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“…However, the tumor microenvironment has not been relatively explored and the radiomic signature extracted from peripheral lung parenchyma maybe enable enhanced tumor invasiveness prediction. Radiomic features extracted from the tumor and peritumor can provide information on both the tumor and its microenvironment, which play an important role on the prediction of lymph node metastasis, post-surgical recurrence risk, discrimination of adenocarcinomas from granulomas (26)(27)(28). Our study suggested that the GLRLM features of the surrounding area of tumor in IA was also higher than that in MIA/AIS, which indicated that the periphery of IA was also more heterogeneous.…”

Section: Discussionmentioning

confidence: 67%

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features

Gao

Xiang

et al. 2020

Front. Oncol.

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Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra-and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or perinodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Wu et al. Peri-Nodular Radiomics of Invasive GGNs Conclusions: The gross radiomic signature of intra-and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.

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“…Jiang et al (26) developed a nomogram for predicting occult N2 LNM in squamous cell lung cancer, but the number of cases was very limited and only involved squamous carcinomas. Some previous studies have developed nomogram models using radiomics for predicting LNM (27,28). However, identifying radiomics features requires special techniques, and the radiomics models are hard to be clinically applied.…”

Section: Discussionmentioning

confidence: 99%

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

Han

Wang

et al. 2020

Front. Oncol.

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Background: Lymph node metastasis (LNM) status is of key importance for the decision-making on treatment and survival prediction. There is no reliable method to precisely evaluate the risk of LNM in NSCLC patients. This study aims to develop and validate a dynamic nomogram to evaluate the risk of LNM in small-size NSCLC. Methods: The NSCLC ≤ 2 cm patients who underwent initial pulmonary surgery were retrospectively reviewed and randomly divided into a training cohort and a validation cohort as a ratio of 7:3. The training cohort was used for the least absolute shrinkage and selection operator (LASSO) regression to select optimal variables. Based on variables selected, the logistic regression models were developed, and were compared by areas under the receiver operating characteristic curve (AUCs) and decision curve analysis (DCA). The optimal model was used to plot a dynamic nomogram for calculating the risk of LNM and was internally and externally well-validated by calibration curves. Results: LNM was observed in 12.0% (83/774) of the training cohort and 10.1% (33/328) of the validation cohort (P = 0.743). The optimal model was used to plot a nomogram with six variables incorporated, including tumor size, carcinoembryonic antigen, imaging density, pathological type (adenocarcinoma or non-adenocarcinoma), lymphovascular invasion, and pleural invasion. The nomogram model showed excellent discrimination (AUC = 0.895 vs. 0.931) and great calibration in both the training and validation cohorts. At the threshold probability of 0-0.8, our nomogram adds more net benefits than the treat-none and treat-all lines in the decision curve. Conclusions: This study firstly developed a cost-efficient dynamic nomogram to precisely and expediently evaluate the risk of LNM in small-size NSCLC and would be helpful for clinicians in decision-making.

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Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?

Cited by 121 publications

References 52 publications

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

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