Objective To study the risk factors of mediastinal lymph node metastasis in patients with ≤3 cm peripheral non small cell lung cancer. Methods From January 2000 to December 2010,a total of 281 patients with NSCLC [15...Objective To study the risk factors of mediastinal lymph node metastasis in patients with ≤3 cm peripheral non small cell lung cancer. Methods From January 2000 to December 2010,a total of 281 patients with NSCLC [152 men and 129 women,aged (60. 31 ± 12. 13) years; ≤ 3 cm in diameter]underwent lobectomy or partial resection with systematic mediastinal lymphadenectomy in hospital. Clinical data included age,gender,展开更多
Objective To identify risk factors of lymph node metastasis in superficial esophageal squamous cell carcinoma(ESCC),and to provide evidence for treatment choice under endoscope.Methods From January 2007 to December 20...Objective To identify risk factors of lymph node metastasis in superficial esophageal squamous cell carcinoma(ESCC),and to provide evidence for treatment choice under endoscope.Methods From January 2007 to December 2011,285 patients with pathologically diagnosed ESCC who received surgery and had clear record of lymph nodes resection were enrolled.The clinical pathological data of these patients were analyzed。展开更多
Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian n...Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.展开更多
文摘Objective To study the risk factors of mediastinal lymph node metastasis in patients with ≤3 cm peripheral non small cell lung cancer. Methods From January 2000 to December 2010,a total of 281 patients with NSCLC [152 men and 129 women,aged (60. 31 ± 12. 13) years; ≤ 3 cm in diameter]underwent lobectomy or partial resection with systematic mediastinal lymphadenectomy in hospital. Clinical data included age,gender,
文摘Objective To identify risk factors of lymph node metastasis in superficial esophageal squamous cell carcinoma(ESCC),and to provide evidence for treatment choice under endoscope.Methods From January 2007 to December 2011,285 patients with pathologically diagnosed ESCC who received surgery and had clear record of lymph nodes resection were enrolled.The clinical pathological data of these patients were analyzed。
文摘Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.
