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Using endobronchial ultrasound features to predict lymph node metastasis in patients with lung cancer.

ABSTRACT: Reliable staging of the mediastinum determines TNM classification and directs therapy for non-small cell lung cancer (NSCLC). Our aim was to evaluate predictors of mediastinal lymph node metastasis in patients undergoing endobronchial ultrasound (EBUS).Patients with known or suspected lung cancer undergoing EBUS for staging were included. Lymph node radiographic characteristics on chest CT/PET scan and ultrasound characteristics of size, shape, border, echogenicity, and number were correlated with rapid on-site evaluation (ROSE) and final pathology. Logistic regression (estimated with generalized estimating equations to account for correlation across nodes within patients) was used with cancer (vs normal pathology) as the outcome. ORs compare risks across groups, and testing was performed with two-sided α of 0.05.Two hundred twenty-seven distinct lymph nodes (22.5% positive for malignancy) were evaluated in 100 patients. Lymph node size, by CT scan and EBUS measurements, and round and oval shape were predictive of mediastinal metastasis. Increasing size of lymph nodes on EBUS was associated with increasing malignancy risk (P = .0002). When adjusted for CT scan size, hypermetabolic lymph nodes on PET scan did not predict malignancy. Echogenicity and border contour on EBUS and site of biopsy were not significantly associated with cancer. In 94.8% of lymph nodes with a clear diagnosis, the ROSE of the first pass correlated with subsequent passes.Lymph node size on CT scan and EBUS and round or oval shape by EBUS are predictors of malignancy, but no single characteristic can exclude a visualized lymph node from biopsy. Further, increasing the number of samples taken is unlikely to significantly improve sensitivity.

SUBMITTER: Wang Memoli JS

PROVIDER: S-EPMC3231960 | biostudies-other | 2011 Dec

REPOSITORIES: biostudies-other

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Project description:Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60-0.88), 0.72 (95% CI 0.57-0.87) and 0.64 (95% CI 0.48-0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61-0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61-0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66-0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

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Project description:BackgroundComputed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer.MethodsWiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network.ResultsThe area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively.ConclusionsThe image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

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Using endobronchial ultrasound features to predict lymph node metastasis in patients with lung cancer.

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