Abstract
This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.
Highlights
At present, cancer has become the primary cause of human death, and the incidence and mortality of lung cancer rank first among all cancers [1]. e occurrence of lung cancer is not easy to find, and about 50% of patients are already at the advanced stage at the time of diagnosis [2]
The 1-year survival rate of patients only accounts for 40% to 50% [6, 7]. 50% of lung cancer patients with bone metastases occur in the spine, and the rest often occur in the femur, ribs, and sternum
Judgment on Classification Accuracy of AdaBoost Algorithm Preliminary. 87 lung cancer spinal bone metastasis MRI images were selected and firstly marked by two radiologists to be classified into 3 levels (Figure 2), and the results of the expert’s marking were deemed as the gold standard (Figures 3–5). e results of the expert’s annotation showed that level I contained 45 images: that is, there must be hot spots in 45 patients
Summary
Cancer has become the primary cause of human death, and the incidence and mortality of lung cancer rank first among all cancers [1]. e occurrence of lung cancer is not easy to find, and about 50% of patients are already at the advanced stage (stage IV) at the time of diagnosis [2]. With the survival benefit of patients, the probability of bone metastasis and skeletal related events increases [3, 4]. The probability of bone metastasis is about 10%∼15%. Bone metastasis of lung cancer is due to bone resorption caused by osteoclasts, usually manifested as osteolytic bone metastasis, which accounts for about 70% of malignant tumor bone metastases [8]. Only 50% of patients with bone metastases have clinical symptoms, usually accompanied by severe bone pain and skeletal related events (spinal cord compression, pathological fractures, hypercalcemia, etc.) [9]. Pathological fracture is the first symptom of patients with lung cancer bone metastasis, and hypercalcemia is a major cause of death of lung cancer bone metastasis
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