Long-term follow-up ultrasonography surveillance in a large cohort of patients with papillary thyroid carcinoma.

OBJECTIVE: This study aimed to investigate the predictive factors as well as the time and age course of recurrence/persistence in a large cohort of postoperative patients with papillary thyroid carcinoma (PTC) based on the long-term ultrasonography (US) follow-up data. METHODS: Between January 2007 and December 2016, 3106 patients underwent surgery for PTC and at least two postoperative US follow-up examination over more than three years. Tumor recurrence/persistence was confirmed based on the follow-up US data and histopathological results. Univariate and multivariate analyses were performed to evaluate the predictive factors of tumor recurrence/persistence. Kaplan-Meier survival analysis was used to evaluate the recurrence-/persistence-free survival curve based on the US results. RESULTS: A total of 321(10.3%) patients developed tumor recurrence/persistence during 54.3 months of mean follow-up (range 36-135 months), including 268(83.5%) cases of lymph node recurrence/persistence, 37 (11.5%) cases of non-lymph node recurrence/persistence, and 16(5%) cases of both types. Recurrence/persistence was observed using US examination at a mean interval of 23.6 ± 21.6 months (range 1-135 months) after surgery and peak incidence was observed 1-2 years after initial treatment. Younger (20-30 years old) and older (70-80 years old) patients had a higher proportion of tumor recurrence/persistence. Multifocality, advanced T and advanced N stages were independent risk factors of tumor recurrence/persistence. CONCLUSION: Tumor recurrence/persistence of PTC usually occurs during the early postoperative period. For patients with multifocal cancer, advanced T and N stage, the US surveillance examination should be cautiously performed, especially in younger and older patients.

Online Denoising Based on the Second-Order Adaptive Statistics Model.

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.