Objective:To construct a thyroid nodule segmentation model using ultrasound dynamic images and explore its potential for assisting in the screening of thyroid nodules.Methods:A total of 126 patients with thyroid nodules（comprising 150 nodules）who were diagnosed and treated at Xuzhou Cancer Hospital from April 2024 to December 2024 were prospectively enrolled. Two-dimensional ultrasound was performed to capture short-axis and long-axis video images of thyroid nodules，forming a dynamic ultrasound image dataset. The dataset was divided into training，validation，and test sets in a ratio of 6∶1∶3. After the training loss curve converged，the model that performed well on the validation set was selected for testing. Three-fold cross-validation was employed for training and testing. All 300 ultrasound videos were divided into three subsets. In each experiment，two subsets were used as the training set，and one subset was used as the test set to evaluate the model's generalization ability. A collaborative spatiotemporal diffusion model was established based on the dynamic trends and tissue texture details of thyroid nodules. Six widely used segmentation metrics were employed to evaluate the model's application capabilities.Results:The study included 126 patients with 150 thyroid nodules，300 dynamic ultrasound images，and video lengths of 3-4 seconds per nodule，resulting in 12 312 segmented images. The size of the thyroid nodules was（10.7 ± 10.6）mm（transverse diameter）×（8.4 ± 6.3）mm（anteroposterior diameter）. Among the nodules，62（41.3%）had clear boundaries，while 88（58.7%）had indistinct boundaries；61（40.7%）exhibited regular shapes，while 89（59.3%）were irregular；66（44.0%）had a taller-than-wide aspect ratio；and 70（46.7%）showed microcalcifications. The collaborative diffusion model based on dynamic ultrasound image segmentation achieved the following scores：a Jaccard score of（69.22 ± 0.03）%，a Dice score of（79.16 ± 0.18）%，a Precision score of（86.70 ± 0.17）%，a Recall score of（77.82 ± 0.04）%，an Sα score of（85.26 ± 0.01）%，and an Eθmn score of（90.58 ± 0.17）%. Compared to other models，this model demonstrated significant improvements across all evaluation metrics，achieving the highest values in each metric with increments of over 8% and 1%，respectively. Conclusions:The collaborative diffusion model with a dynamic controller，constructed based on dynamic ultrasound images of thyroid nodules，demonstrates excellent performance in ultrasound image segmentation. It improves the accuracy of thyroid nodule screening，thereby providing a valuable auxiliary diagnostic tool for clinical practice.

Objective:To construct a thyroid nodule segmentation model using ultrasound dynamic images and explore its potential for assisting in the screening of thyroid nodules.Methods:A total of 126 patients with thyroid nodules（comprising 150 nodules）who were diagnosed and treated at Xuzhou Cancer Hospital from April 2024 to December 2024 were prospectively enrolled. Two-dimensional ultrasound was performed to capture short-axis and long-axis video images of thyroid nodules，forming a dynamic ultrasound image dataset. The dataset was divided into training，validation，and test sets in a ratio of 6∶1∶3. After the training loss curve converged，the model that performed well on the validation set was selected for testing. Three-fold cross-validation was employed for training and testing. All 300 ultrasound videos were divided into three subsets. In each experiment，two subsets were used as the training set，and one subset was used as the test set to evaluate the model's generalization ability. A collaborative spatiotemporal diffusion model was established based on the dynamic trends and tissue texture details of thyroid nodules. Six widely used segmentation metrics were employed to evaluate the model's application capabilities.Results:The study included 126 patients with 150 thyroid nodules，300 dynamic ultrasound images，and video lengths of 3-4 seconds per nodule，resulting in 12 312 segmented images. The size of the thyroid nodules was（10.7 ± 10.6）mm（transverse diameter）×（8.4 ± 6.3）mm（anteroposterior diameter）. Among the nodules，62（41.3%）had clear boundaries，while 88（58.7%）had indistinct boundaries；61（40.7%）exhibited regular shapes，while 89（59.3%）were irregular；66（44.0%）had a taller-than-wide aspect ratio；and 70（46.7%）showed microcalcifications. The collaborative diffusion model based on dynamic ultrasound image segmentation achieved the following scores：a Jaccard score of（69.22 ± 0.03）%，a Dice score of（79.16 ± 0.18）%，a Precision score of（86.70 ± 0.17）%，a Recall score of（77.82 ± 0.04）%，an Sα score of（85.26 ± 0.01）%，and an Eθmn score of（90.58 ± 0.17）%. Compared to other models，this model demonstrated significant improvements across all evaluation metrics，achieving the highest values in each metric with increments of over 8% and 1%，respectively. Conclusions:The collaborative diffusion model with a dynamic controller，constructed based on dynamic ultrasound images of thyroid nodules，demonstrates excellent performance in ultrasound image segmentation. It improves the accuracy of thyroid nodule screening，thereby providing a valuable auxiliary diagnostic tool for clinical practice.

BACKGROUND: Textbook outcome (TO) can guide decision-making among patients and clinicians during preoperative patient selection and postoperative quality improvement. We explored the factors associated with achieving a TO for gallbladder carcinoma (GBC) after curative-intent resection and analyzed the effect of adjuvant chemotherapy (ACT) on TO and non-TO patients. METHODS: A total of 540 patients who underwent curative-intent resection for GBC at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi'an Jiaotong University from January 2011 to December 2020 were retrospectively analyzed. Multivariable logistic regression was used to investigate the factors associated with TO. RESULTS: Among 540 patients with GBC who underwent curative-intent resection, 223 patients (41.3%) achieved a TO. The incidence of TO ranged from 19.0% to 51.0% across the study period, with a slightly increasing trend over the study period. The multivariate analysis showed that non-TO was an independent risk factor for prognosis among GBC patients after resection ( P = 0.003). Age ≤60 years ( P = 0.016), total bilirubin (TBIL) level ≤34.1 μmol/L ( P <0.001), well-differentiated tumor ( P = 0.008), no liver involvement ( P <0.001), and T1-2 stage disease ( P = 0.006) were independently associated with achieving a TO for GBC after resection. Before and after propensity score matching (PSM), the overall survival outcomes of non-TO GBC patients who received ACT and those who did not were statistically significant; ACT improved the prognosis of patients in the non-TO group ( P <0.05). CONCLUSION Achieving a TO is associated with a better long-term prognosis among GBC patients after curative-intent resection, and ACT can improve the prognosis of those with non-TO.
Humans ; Middle Aged ; Gallbladder Neoplasms/pathology* ; Retrospective Studies ; Prognosis ; Hepatectomy ; Cholecystectomy

Objective: To explore the prognostic factors of patients with intrahepatic cholangiocarcinoma (ICC) after surgical resection and establish a nomogram for survival prediction. Methods: A total of 160 patients with ICC who underwent surgical resection in the First Affiliated Hospital of Xi′an Jiaotong University from January 2010 to December 2018 were retrospectively analyzed. Among them, 89 patients were males and 71 were females, aged from 29 to 81 years with a age of (57.41±10.35) years. Observation indicators included: (1) The result of follow-up: postoperative survival. (2) The univariate analysis and multivariate analysis affecting postoperative patients′ prognosis. (3) The establishment and validation of nomogram model. The follow-up using outpatient and telephone was performed once every 3 months within 1 year postoperatively and once every 3-6 months after 1 year postoperatively up to August 1, 2019. The follow-up included liver function, CA19-9, upper abdominal ultrasound, CT or MRI. The overall postoperative survival time, end point of observation, was the date from the operation date to the follow-up date, or the date of death due to tumor recurrence and metastasis. The patients′ clinicopathological data was included in the prognostic factor analysis, the Kaplan-meier method and Log-rank test were conducted for the univariate analysis, the Cox proportional risk regression model was used for the multivariate analysis. The independent risk factors based on Cox regression model were screened to establish a nomogram for postoperative survival prediction. The patients were divided into the model group (n=112) for the establishment of survival nomogram and the validation group (n=48) for the assessment of predictive ability at a ratio of 7∶3, and the accuracy of nomogram in postoperative survival prediction was assessed by c-index. Measurement data with normal distribution were expressed with (Mean±SD). Measurement data with skewed distribution were described as M (range). Count data were expressed as cases and percentage. Results: During the follow-up period, all patients with ICC after surgical resection were followed up for a survival time of 20 months (2-111 months). 100 patients died of multiple organ failure caused by tumor recurrence and metastasis and 60 patients survived. The 1-, 3- and 5-year overall survival rates of patients were 63.3%, 30.0% and 19.6%, respectively. Univariate analysis showed that CA19-9, hepatolithiasis, number of tumor, range of liver resection, tumor differentiation, pathology type, tumor diameter, vascular invasion, TNM stage, lymphatic metastasis, satellite foci and surgical margin were the prognostic factors of ICC patients after surgical resection (HR=1.78, 1.97, 2.91, 1.89, 3.06, 2.86, 2.07, 1.94, 2.24, 1.95, 2.68, 2.00, 95%CI: 1.12-2.85, 1.22-3.16, 1.85-4.56, 1.26-2.85, 1.38-6.82, 1.31-6.25, 1.37-3.14, 1.07-3.51, 1.24-4.06, 1.26-3.01, 1.28-5.60, 1.11-3.59, P<0.05). Multivariate analysis showed that hepatolithiasis, number of tumor, range of liver resection, tumor differentiation (low differentiation) and pathology type were independent risk factors of ICC patients after surgical resection (HR=2.47, 2.37, 2.06, 5.52, 5.72, 95%CI: 1.39-4.38, 1.44-3.91, 1.25-3.40, 1.24-24.49, 2.31-14.17, P<0.05). The nomogram was established based on above five independent risk factors, the c-index value for postoperative survival of the model group and validation group were 0.71 (95%CI: 0.64-0.79) and 0.71(95%CI: 0.61-0.81), respectively. Conclusion A nomogram based on hepatolithiasis, number of tumor, range of liver resection, tumor differentiation and pathology type has better accuracy in postoperative survival prediction for patients with ICC.