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Objective:To investigate the factors influencing the prognosis of anaplastic thyroid cancer (ATC) and to evaluate the application value of established random survival forest (RSF) model in the prognosis prediction of ATC.Methods:A total of 707 ATC patients diagnosed by histopathology in the Surveillance, Epidemiology and End Results (SEER) database of the National Cancer Institute from 2004 to 2015 were selected and randomly divided into the training set (495 cases) and the validation set (212 cases). Univariate Cox regression risk model was used to analyze the related factors affecting overall survival (OS) of patients in the training set. The multivariate Cox proportional risk model based on the minimum Akaike information criterion (AIC) was used to analyze the above variables and then the variables were screened out. The traditional Cox model for predicting OS was constructed based on the screened variables. The RSF algorithm was used to analyze the variables with P < 0.05 in the univariate Cox regression analysis, and 5 important features were selected. Multivariate Cox proportional risk model was selected based on the minimum AIC. Then the RSF-Cox model for predicting OS was constructed by using screened variables. The time-dependent receiver operating characteristic (tROC) curve and the area under the curve (AUC), calibration curve, decision curve and integrated Brier score (IBS) in the training set and the validation set were used to evaluate the prediction performance of the models. Results:Univariate Cox regression analysis showed that age, chemotherapy, lymph node metastasis, radiotherapy, surgical method, tumor infiltration degree, tumor number, tumor diameter and diagnosis time were factors affecting the prognosis of ATC (all P < 0.05). Multivariate Cox regression analysis based on minimal AIC (4 855.8) showed that younger age (61-70 years vs. > 80 years: HR = 0.732, 95% CI 0.56-0.957, P = 0.023; ≤ 50 years vs. > 80 years: HR = 0.561, 95% CI 0.362-0.87, P = 0.010), receiving chemotherapy (receiving or not: HR = 0.623, 95% CI 0.502-0.773, P < 0.001), receiving radiotherapy (receiving or not: HR = 0.695, 95% CI 0.559-0.866, P = 0.001), receiving surgery (lobectomy, no surgery or unknown: HR = 0.712, 95% CI 0.541-0.939, P = 0.016; total resection or subtotal resection vs. no surgery or unknown: HR = 0.535, 95% CI 0.436-0.701, P < 0.001), and tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.495, 95% CI 0.262-0.938, P = 0.031; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.714, 95% CI 0.520-0.980, P = 0.037; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.699, 95 % CI 0.545-0.897, P = 0.005) were independent protective factors for OS of ATC patients. Lymph node metastasis (N 1 unknown vs. N 0: HR = 1.664, 95% CI 1.158-2.390, P = 0.006; N 1b: HR = 1.312, 95% CI 1.029-1.673, P = 0.028), more aggressive tumor infiltration degree (group 3 vs. group 1: HR = 1.492, 95% CI 1.062-2.096, P = 0.021; group 4 vs. group 1: HR = 1.636, 95% CI 1.194 - 2.241, P = 0.002) were independent risk factors for OS of ATC patients. Although diagnosis time was not statistically significant (2010-2015 vs.2004-2009: HR = 1.166, 95% CI 0.962-1.413, P = 0.118), the inclusion of it could improve the efficacy of the traditional Cox model. RFS algorithm was used to select out 5 important variables: surgical method, tumor diameter, age group, chemotherapy, and tumor number. Multivariate Cox regression analysis based on minimum AIC (4 884.6) showed that chemotherapy (receiving or not: HR = 0.574, 95% CI 0.476-0.693, P < 0.001), surgical method (lobectomy, no surgery or unknown: HR = 0.730, 95% CI 0.567-0.940, P = 0.015; total resection or subtotal resection vs. no surgery or unknown: HR = 0.527, 95% CI 0.423-0.658, P < 0.001), tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.428, 95% CI 0.231-0.793, P = 0.007; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.701, 95% CI 0.513-0.958, P = 0.026; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.681, 95% CI 0.536-0.866, P = 0.002) were independent factors for OS of ATC patients. RSF-Cox model was constructed based on 3 variables. The tAUC curve analysis showed that RSF-Cox model for predicting the 6-month, 12-month, and 18-month OS rates were 93.56, 92.62, and 90.80, respectively in the training set, and 93.05, 92.47, and 90.20, respectively in the validation set; in the traditional Cox model, the corresponding OS rates were 89.00, 87.76, 85.24, respectively in the training set, and 86.22, 83.68, 82.86, respectively in the validation set. When predicting OS rate at 6-month, 12-month and 18-month, the calibration curve of RSF-Cox model was closer to 45° compared with that of traditional Cox model, and the clinical net benefit of decision curve in RSF-Cox model was higher than that in traditional Cox model. The IBS of RSF-Cox model (0.089) was lower than that of traditional Cox model (0.111). Conclusions:The RSF model based on chemotherapy, surgical method and tumor diameter can effectively predict the OS of ATC patients.
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STUDY DESIGN: Retrospective study. OBJECTIVES: To evaluate the factors related to the incidence of a new fracture in an adjacent vertebra after kyphoplasty for single vertebral body fracture due to osteoporosis and to assess the impact of such factors on patients' survival rate. SUMMARY OF LITERATURE REVIEW: It is controversial whether fracture of an adjacent vertebra after kyphoplasty is due to the natural course of osteoporosis or as a complication of kyphoplasty. MATERIALS AND METHODS: From December 2006 to December 2016, among 490 cases of kyphoplasty for single vertebral body fracture due to osteoporosis, 153 cases were analyzed retrospectively. The survival rate was analyzed based on age, gender, body mass index (BMI), fracture level, leakage of cement, amount of cement, compression rate, recovery rate, bone density, osteoporotic medication rate and compliance, existence of a compression fracture, hypertension, diabetes, and smoking habit. The average follow-up duration was 15.1 months (range, 1 month to 8 years and 8 months) and the mean age was 74.4 years (range, 54–93 years). RESULTS: A new fracture in an adjacent vertebral body occurred in 27 cases (17.3%). The 1-year survival rate was 82.6%, the 2-year survival rate was 72.5%, and the 6-year survival rate was 53.7%. The survival rate was significantly higher in patients younger than 75 years (p=0.0495). The survival rate was also significantly higher in patients with a preoperative vertebral bone density greater than −3.7 and hip bone density greater than −2.2 (p < 0.0001, p=0.0114). The survival rate was significantly higher in patients with a BMI greater than 18.1 kg/m2 at the time of surgery (p=0.0014). Furthermore, the survival rate was significantly higher in patients with a postoperative recovery of vertebral height of 14% or less (p=0.0031). In addition, the survival rate was higher in patients without a compression fracture before surgery (p=0.0297). In multiple factor analysis, vertebral bone density (p=0.0049) and age (p=0.0408) were identified as statistically significant factors. CONCLUSIONS: The survival rate was significantly lower at 1, 2, and 6 years in patients with an adjacent vertebral fracture. The most crucial factors affecting the survival rate were age and vertebral bone density.