Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

Abstract

Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.

Machine Learning and Texture Analysis of [18F]FDG PET/CT Images for the Prediction of Distant Metastases in Non-Small-Cell Lung Cancer Patients

“…In the prediction of treatment response and prognosis, AUC values up to 0.95 were achieved using machine learning algorithms [ 7 , 22 ]. For staging purposes, lymph node involvement was predicted by both machine learning and deep learning models, which often included radiomic features and clinical data with AUC values up to 0.94 [ 19 , 22 , 37 , 38 , 39 , 40 ]. Only a few studies used AI-based algorithms for the prediction of distant metastases in lung cancer patients [ 21 , 22 , 23 ].…”

“…In particular, previous studies performed texture analysis in lung cancer patients and showed that several features, including the coefficient of variation, dissimilarity, coarseness, and entropy, were able to predict both PFS and OS in patients [13][14][15][16][17][18]. Other studies successfully used texture analysis and machine learning or deep learning methods to discriminate involved lymph nodes from reactive lymph nodes in lung cancer patients by analyzing and processing 18 F-labeled 2-deoxy-d-glucose {[ 18 F]FDG} PET/CT or CT alone [19,20]. Only a few studies reported the use of texture analysis to predict distant metastases in NSCLC patients and even fewer combined texture analysis and machine learning methods to identify patients at high risk of developing distant metastases [21][22][23].…”

Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

“…Similarly, random forest is a model which combines many decision trees for prediction [ 33 ]. SVM is a classification algorithm that estimates the hyperplane equation that divides the input data into different output classes, maximizing the minimum distance between the classes and the hyperplane [ 34 , 35 ]. Logistic regression is an algorithm by which a logistic curve is fitted to a training data set by modeling the probability of belonging to one of the classes.…”

Investigation on the incidence and risk factors of lung cancer among Chinese hospital employees

“…According to previous research reports, it is known that repeated LDCT screening in high‐risk groups of lung cancer can effectively reduce the death of patients because of lung cancer to a certain extent. However, considering the epidemiological characteristics of lung cancer in different regions and the differences in the availability of medical resources, 19,22–26 whether high‐risk groups with different definitions can also benefit from one‐time LDCT screening is an urgent scientific question that needs to be answered 27–31 …”

“…However, considering the epidemiological characteristics of lung cancer in different regions and the differences in the availability of medical resources, 19,[22][23][24][25][26] whether high-risk groups with different definitions can also benefit from one-time LDCT screening is an urgent scientific question that needs to be answered. [27][28][29][30][31] The effect of popularization of LDCT on the detection rate of lung cancer Chest X-rays are usually performed in routine physical examinations. This examination has great limitations in the detection of pulmonary nodules, and it is easy to miss the sub-centimeter lesions of pulmonary nodules, approximately 22% to 85% of early lung cancers may be miss detection.…”