Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach

Abstract

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning approach based on conventional magnetic resonance imaging (MRI) for non-invasive classification of SCLC vs. NSCLC brain metastases. Sixty-nine patients with brain metastasis of lung cancer origin were included. Of them, 44 patients had NSCLC and 25 patients had SCLC. Classification was performed with EfficientNet architecture on crop images of lesion areas and based on post-contrast T1-weighted, T2-weighted and FLAIR imaging input data. Evaluation of the model was carried out in a 5-fold cross-validation manner, and based on accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve. The best classification results were obtained with multiparametric MRI input data (T1WI+c+FLAIR+T2WI), with a mean overall accuracy of 0.90 ± 0.04, and F1 score of 0.92 ± 0.05 for NSCLC and 0.87 ± 0.08 for SCLC for the validation data and an accuracy of 0.87 ± 0.05, with an F1 score of 0.88 ± 0.05 for NSCLC and 0.85 ± 0.05 for SCLC for the test dataset. The proposed method provides an automatic noninvasive method for the classification of brain metastasis with high sensitivity and specificity for differentiation between NSCLC vs. SCLC brain metastases. It may be used as a diagnostic tool for improving decision-making in the treatment of patients with these metastases. Further studies on larger patient samples are required to validate the current results.

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

“…Han et al [71] used machine learning techniques to distinguish the SCLC types, and achieved an accuracy of 84.10%. Grossman et al [72] applied EfficientNet to deep learning, and obtained a highest accuracy of 90%. Hussain et al [13] computed different entropic-based features and computed the nonlinear dynamics to distinguish the SCLC from NSCLC with the highest significant results (p-value < 0.000000).…”

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images

“…Some studies have focused on classifying different types of BM using various approaches. [20][21][22] By using MRI characteristics and clinical information, Kniep et al employed forest machine learning method for multiclassification task and a two-classification was performed with DL models by Grossman et al 20,21 In contrast to this previous study, we divided this multiclassification task into binary classifications by DL models. Compared with traditional machine learning such as radiomics, DL models can complete data classification better by the deeper network layers, nonlinear activation, and feature transformation.…”

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

“…Moreover, alternative methods of dealing with the same question involving deep learning, neural networks, or automated methods can differentiate between tumor entities and may be used successfully instead of radiomic approaches-for example, to distinguish between small-cell and non-small-cell lung cancer [136]. Current research is increasingly focusing on incorporating artificial intelligence in radiomic studies.…”