Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images
This work addresses the problem of improving diagnostic accuracy for non-small cell lung cancer patients, but it is incremental as it shows CNN is comparable to existing methods without major gains.
The study compared machine learning methods for classifying mediastinal lymph node metastasis in non-small cell lung cancer from PET/CT images, finding that CNN performance was not significantly different from the best classical methods and human doctors, offering more convenience and objectivity without needing tumor segmentation or feature calculation.
The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.