Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma

Zhong, Yan; Yuan, Mei; Zhang, Teng; Zhang, Yudong; Li, Hai; Yu, Tong-Fu

doi:10.2214/ajr.17.19074

Cited by 75 publications

(81 citation statements)

References 22 publications

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“…Radiomics method was also applied in other imaging modalities like CT or MRI images of some primary cancer like bladder, colon cancer to predict regional lymph-node metastasis, demonstrating this method was a useful way to make a prediction of lymph-node metastasis 41,42 . Compared with the previous study, our study yielded a better diagnostic performance by concentrating on the clinical parameter combined DLR method, which can complement image features with more information and make the model more robust by restraining the features extracted from images 26 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

et al. 2020

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Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

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Section: Discussionmentioning

confidence: 99%

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

et al. 2020

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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%

“…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%

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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“…In recent years, predicting metastasis with machine learning algorithms, as a promising alternative for other invasive or noninvasive diagnostic method, has been proven to be feasible in lung adenocarcinoma and colorectal cancer [11,12]. These studies predicted on CT image and histologic evidence and obtained satisfying results.…”

Section: Discussionmentioning

confidence: 99%

“…Comparing to traditional statistical models, ML predictive analysis has several bene ts, including less outcomes required for each predictor, no requirement for speci c hypothesis and allowance of interaction between variables [9,10]. ML-based predictive analysis has been validly used in medical eld [11,12]. From the authors' perspective, there were very few studies that have reported the application of ML algorithms for evaluating the risk of LNM in lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

Chong

Liu

et al. 2020

Preprint

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BackgroundLymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.MethodsA total of 709 lung adenocarcinoma patients with tumor size ≤ 2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model’s predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors.ResultsLNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) > 0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of 5 variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves. The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%).ConclusionsIncorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.Trial registration: NA

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Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma

Abstract: The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.

Cited by 75 publications

References 22 publications

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

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

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

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