Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm's robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail.
Objective: To analyze the correlation between lymph node metastasis of thoracic esophageal squamous cell carcinoma (ESCC) and clinical and pathological factors, and to provide a reference for the outline of clinical target volume. Methods: The pathological characteristics of 1034 thoracic ESCC patients after surgery were described, and the correlations between clinical and pathological factors and lymph node metastasis were studied by univariate and Logistic multivariate analyses. Results: Lymph node metastasis was significantly correlated with tumor length, invasion depth and differentiation degree (P<0.01), but not gender, age, tumor site or pathological type (P>0.05). Logistic multivariate analysis showed that tumor length, invasion depth and differentiation degree were independent risk factors for thoracic ESCC. The lymph node metastasis rates of mid-thoracic ESCC in the middle mediastinum, lower-thoracic ESCC in the lower mediastinum and abdominal cavity were 18.5%, 35.3% and 19.7% respectively in the T1-T2 stage. In the T3-T4 stage, the lymph node metastasis rates of mid-thoracic ESCC in the middle mediastinum and abdominal cavity were 39.6% and 17.4% respectively, and those of lower-thoracic ESCC in middle and lower mediastina and abdominal cavity were 21.1%, 43.4% and 29.8% respectively. Highly/moderately differentiated mid-thoracic ESCC in the middle mediastinum, lower-thoracic ESCC in middle and lower mediastina and abdominal cavity had the lymph node metastasis rates of 34.7%, 15.1%, 33.5% and 23.7% respectively. Lowly differentiated mid-thoracic ESCC in the middle mediastinum and abdominal cavity had the lymph node metastasis rates of 46.9% a 29.6% respectively, and those of lower-thoracic ESCC in middle and lower mediastina and abdominal cavity were 25.5%, 49.1% and 27.3% respectively. Conclusion: During the outline of radiotherapy target volume for thoracic ESCC, tumor length, invasion depth and differentiation degree should be comprehensively considered to selectively irradiate the regions prone to lymph node metastasis. How to cite this:Pan G, Pan H, Zhang Y, Shuai H. Effects of lymph node metastasis of thoracic esophageal squamous cell carcinoma on design of radiotherapy target volume. Pak J Med Sci. 2019;35(1):177-182. doi: https://doi.org/10.12669/pjms.35.1.431 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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