Background Increased attention has been focused on cancer immunity gene signature. However, the threshold of immune scores to predict disease‐free survival (DFS) and overall survival (OS) in breast cancer has not yet been defined. This study aimed to explore the association of immune scores with prognosis and build a clinical nomogram to predict the survival of patients with breast cancer. Methods A total of 986 subjects were analyzed, and clinicopathological characteristics and immune scores were obtained from the TCGA database. Cox proportional hazards regression model was used to estimate the adjusted hazard ratios ( HR s). Based on results of multivariate analysis, nomograms were built. The models were subjected to bootstrap internal validation. The predictive accuracy and discriminative ability were measured by concordance index (C‐index) and the calibration curve. Results The patients were divided into three subgroups according to their immune scores. We found that compared with patients with low immune scores, those with intermediate and high immune scores had significantly improved DFS ( HR and 95% confidence interval [ CI ]: 0.439 [0.242‐0.799], 0.541 [0.343‐0.855], respectively), whereas only intermediate immune scores significantly indicated better OS ( HR and 95% CI : 0.385 [0.163‐0.910]). The C‐index for DFS and OS prediction was 0.723 (95% CI , 0.661‐0.785) and 0.800 (95% CI , 0.724‐0.877), respectively. The calibration curves for probability of 3‐ and 5‐year DFS showed significant agreement between nomogram predictions and the actual observations. Conclusions High and/or intermediate immune scores are significantly correlated with better DFS and OS in patients with breast cancer. Moreover, the nomograms for predicting prognosis may help to estimate the survival of patients.
Objective:This randomized study aimed to compare the clinical efficacy between the novel dual tracer composed of indocyanine green (ICG) and blue dye (BD) and the conventional dual tracer composed of radioisotope and BD for sentinel lymph node (SLN) mapping in patients with breast cancer.Methods:This study enrolled 471 clinically lymph node-negative patients with primary breast cancer. All patients underwent mastectomy, and those undergoing sentinel lymph node biopsy (SLNB) were randomized to receive blue dye plus radioisotope (RB group) or BD plus ICG (IB group). The detection performances on SLN identification rate, positive SLN counts, detection sensitivity, and false-negative rate were compared between the two groups.Results:In the IB group, 97% (194/200) of the patients who underwent the ICG and BD dual tracer injection showed fluorescent-positive lymphatic vessels within 2–5 min. The identification rate of SLNs was comparable between the IB group (99.0%, 198/200) and the RB group (99.6%, 270/271) (P = 0.79). No significant differences were observed in the identification rate of metastatic SLNs (22.5% vs. 22.9%, P > 0.05, RB group vs. IB group, the same below), positive SLN counts (3.72 ± 2.28 vs. 3.91 ± 2.13, P > 0.05), positive metastatic SLN counts (0.38 ± 0.84 vs. 0.34 ± 0.78, P > 0.05), SLNB detection sensitivity (94.4% vs. 92.5%, P > 0.05), or false-negative rate (5.6% vs. 7.5%, P > 0.05) between the two groups. Conclusions:ICG can be used as a promising alternative tracer for radioisotope in SLN mapping, and when it is combined with BD in lymphangiography, it offers comparable detection sensitivity compared to the conventional lymphatic mapping strategies that are widely used in clinical practice.
Objective: To investigate the efficacy and safety of da Vinci robot-assisted thyroidectomy via an unilateral axilla-bilateral areola (UABA) approach. Methods: The clinical data of 500 patients undergoing robotic thyroidectomy via an UABA approach from July 2014 to April 2018 were retrospectively analyzed. All 500 patients were operated on by the same surgeon and divided into two groups by the time sequence. The efficacy and complications were compared between the two groups. Results: Robotic thyroidectomy via an UABA approach was performed successfully in 500 cases, including 196 cases of benign thyroid diseases with a lesion diameter of 3.1 ± 1.3 cm (0.4-8.2 cm) and 304 cases of thyroid cancer with a tumor diameter of 1.2 ± 0.7 cm (0.4-4.4 cm). Surgical procedures included unilateral lobectomy and total thyroidectomy with or without central lymph node dissection. Among the 500 patients, 9 (1.8%) had transient recurrent laryngeal nerve injury, 1 (0.2%) had permanent unilateral recurrent laryngeal nerve injury, 12 (2.4%) had subcutaneous hemorrhage of the trajectory area, and 6 (1.2%) had subcutaneous infection of the trajectory area after surgery. Among 239 thyroid cancer patients undergoing total thyroidectomy, 45 (18.8%) had transient hypoparathyroidism and 5 (2.1%) had permanent hypoparathyroidism. The incidence of permanent hypoparathyroidism was 1.9% (4/212) among the patients undergoing total thyroidectomy plus unilateral central lymph node dissection, and 3.7% (1/27) among the patients undergoing total thyroidectomy plus bilateral central lymph node dissection. During the follow-up of median 17 months, all patients were satisfied with postoperative appearance of the neck and no structural recurrence or metastases occurred. There was no significant difference in efficacy between the two groups (P > 0.05), while the complication rate in phase 2 was significantly lower than that in phase 1 (P < 0.05) as the surgeon became more proficient in the UABA approach. Conclusion: Robotic thyroidectomy via an UABA approach is simple, safe, and minimally invasive, suitable for radical resection of large benign tumors and early thyroid cancer and central lymph node dissection.
Background: Pregnancy and childbirth are the main causes of pelvic floor dysfunction (PFD). Although pelvic floor muscle tension is typically measured at 42 days postpartum to assess the severity of PFD and provide timely rehabilitation, it is still impossible to predict PFD and take targeted preventive measures in clinical practice. A PFD prediction model based on big data obtained in prenatal check-ups was established in this study to allow the formulation of personalized preventive strategies to reduce the incidence of PFD.Methods: A total of 1,500 women who underwent regular prenatal checkups and examinations for PFD at 42 days postpartum at the Zhuji Maternal and Child Health Hospital between May 2015 and May 2020 were selected. The data from 1,000 of them were selected as the training cohort, and the data from 500 of them were used as the validation cohort. The women were divided into a PFD group and a non-PFD group according to whether PFD was diagnosed at 42 days postpartum. A nomogram prediction model was created using the influencing factors that lead to PFD, and the discrimination and calibration of the nomogram were evaluated through internal and external validation. Results: A total of 389 cases (38.9%) of PFD were included in the training cohort. Multivariate analysis showed that age (odds ratio (OR) =1.896, P<0.001), history of childbirth (OR =4.531, P<0.001), history of constipation (OR =2.475, P<0.001), urinary incontinence during pregnancy (OR =4.416, P<0.001), and biparietal diameter at 32 weeks of gestation (OR =51.672, P=0.012) were independent influencing factors of PFD at 42 days postpartum. These factors were used to establish a nomogram prediction model. This prediction model maintained good discrimination between the training cohort and the external validation cohort (the area under the curve was 0.893 and 0.842 for the training and validation cohorts, respectively). Conclusions:The study validated that the nomogram prediction model based on the factors influencing PFD can be used to predict PFD at 32 weeks of gestation for timely intervention and prevention of PFD.
A 31-year-old male patient had been diagnosed with a left papillary micro-thyroid carcinoma (Figure 1) with node metastases and received a total thyroidectomy with bilateral central neck dissection and left lateral neck dissection in January 2014 in our hospital. After the operation, he received radioiodine treatment and thyroid-stimulating hormone (TSH) suppression treatment with levothyroxine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.