Prediction of malignant lymph nodes in NSCLC by machine-learning classifiers using EBUS-TBNA and PET/CT.

Autor: Guberina, Maja, Herrmann, Ken, Pöttgen, Christoph, Guberina, Nika, Hautzel, Hubertus, Gauler, Thomas, Ploenes, Till, Umutlu, Lale, Wetter, Axel, Theegarten, Dirk, Aigner, Clemens, Eberhardt, Wilfried E. E., Metzenmacher, Martin, Wiesweg, Marcel, Schuler, Martin, Karpf-Wissel, Rüdiger, Santiago Garcia, Alina, Darwiche, Kaid, Stuschke, Martin
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Zdroj: Scientific Reports; 10/20/2022, Vol. 12 Issue 1, p1-13, 13p
Abstrakt: Accurate determination of lymph-node (LN) metastases is a prerequisite for high precision radiotherapy. The primary aim is to characterise the performance of PET/CT-based machine-learning classifiers to predict LN-involvement by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in stage-III NSCLC. Prediction models for LN-positivity based on [18F]FDG-PET/CT features were built using logistic regression and machine-learning models random forest (RF) and multilayer perceptron neural network (MLP) for stage-III NSCLC before radiochemotherapy. A total of 675 LN-stations were sampled in 180 patients. The logistic and RF models identified SUVmax, the short-axis LN-diameter and the echelon of the considered LN among the most important parameters for EBUS-positivity. Adjusting the sensitivity of machine-learning classifiers to that of the expert-rater of 94.5%, MLP (P = 0.0061) and RF models (P = 0.038) showed lower misclassification rates (MCR) than the standard-report, weighting false positives and false negatives equally. Increasing the sensitivity of classifiers from 94.5 to 99.3% resulted in increase of MCR from 13.3/14.5 to 29.8/34.2% for MLP/RF, respectively. PET/CT-based machine-learning classifiers can achieve a high sensitivity (94.5%) to detect EBUS-positive LNs at a low misclassification rate. As the specificity decreases rapidly above that level, a combined test of a PET/CT-based MLP/RF classifier and EBUS-TBNA is recommended for radiation target volume definition. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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