ObjectiveTo synthesize recent advances in surgical management of breast cancer, focusing on five key issues: axillary surgery de-escalation, margin control in breast-conserving surgery (BCS), prophylactic surgery for BRCA1/2 gene mutation carriers, local therapy for oligometastasis, and intraoperative radiotherapy (IORT), aiming to guide individualized clinical decisions. MethodsA comprehensive analysis of high-quality evidence (RCTs, prospective cohorts and multicenter studies) was conducted, comparing efficacy and safety across strategies. ResultsFor patients with positive sentinel lymph nodes (SLN) undergoing BCS, axillary lymph node dissection (ALND) can be safely omitted if they present with clinical stage cT1–2, cN0 disease, have not received preoperative chemotherapy, exhibit 1–2 positive SLNs, and are planned for whole-breast radiotherapy. The Memorial Sloan Kettering Cancer Center nomogram quantitatively predicts non-SLN metastasis risk by integrating features like tumor size and SLN metastatic burden. Omission of ALND is particularly safe for SLN micrometastasis (≤2 mm), demonstrating a 5-year overall survival rate of approximately 97.5%. In patients achieving clinically node-negative (ycN0) status post-neoadjuvant therapy, techniques such as dual-tracer mapping or pre-treatment marking of suspicious nodes reduce the false negative rate of SLN biopsy. Treatment decisions for elderly patients require multidisciplinary assessment of surgical risks versus benefits. The integration of multiparametric MRI, artificial intelligence with intraoperative ultrasound significantly reduces positive margin rates in BCS from 25% to 8%–15%, markedly decreasing reoperation rates. For BRCA1/2 mutation carriers, prophylactic mastectomy reduces breast cancer risk by 90%–95%, while prophylactic bilateral salpingo-oophorectomy (PBSO) reduces ovarian cancer risk by 80%–90%; the timing of PBSO is stratified by genotype (BRCA1: 35–40 years; BRCA2: 40–45 years) and integrated with fertility plans and psychological assessment. Local therapy provides clear survival benefits for oligometastatic breast cancer patients with hormone receptor positive disease and bone/soft tissue metastases, with stereotactic body radiotherapy being preferred for low-burden metastases. IORT for early breast cancer is strictly limited to low-risk patients, achieving long-term survival rates equivalent to conventional radiotherapy but necessitating stringent patient selection. ConclusionsPrecision surgery is evolving through axillary de-escalation, real-time margin assessment, risk-adapted prophylactic surgery, selected local therapy for oligometastasis, and strict patient selection for IORT. Multidisciplinary integration is essential for future optimization.
ObjectiveProlonged mechanical ventilation (PMV) is a prognostic marker for short-term adverse outcomes in patients after lung transplantation.The risk of prolonged mechanical ventilation after lung transplantation is still not clear. The study to identify the risk factors of prolonged mechanical ventilation (PMV) after lung transplantation.Methods This retrospective observational study recruited patients who underwent lung transplantation in Wuxi People’s Hospital from January 2020 to December 2022. Relevant information was collected from patients and donors, including recipient data (gender, age, BMI, blood type, comorbidities), donor data (age, BMI, time of endotracheal intubation, oxygenation index, history of smoking, and any comorbidity with multidrug-resistant bacterial infections), and surgical data (surgical mode, incision type, operation time, cold ischemia time of the donor lung, intraoperative bleeding, and ECMO support), and postoperative data (multi-resistant bacterial lung infection, multi-resistant bacterial bloodstream infection, and mean arterial pressure on postoperative admission to the monitoring unit). Patients with a duration of mechanical ventilation ≤72 hours were allocated to the non-prolonged mechanical ventilation group, and patients with a duration of mechanical ventilation>72 hours were allocated to the prolonged mechanical ventilation group. LASSO regression analysis was applied to screen risk factors., and a clinical prediction model for the risk of prolonged mechanical ventilation after lung.ResultsPatients who met the inclusion criteria were divided into the training set and the validation set. There were 307 cases in the training set group and 138 cases in the validation set group. The basic characteristics of the training set and the validation set were compared. There were statistically significant differences in the recipient’s BMI, donor’s gender, CRKP of the donor lung swab, whether the recipient had pulmonary infection before the operation, the type of transplantation, the cold ischemia time of the donor lung, whether ECMO was used during the operation, the duration of ECMO assistance, CRKP of sputum, and the CRE index of the recipient's anal test (P<0.05). 2. The results of the multivariate logistic regression model showed that female recipients, preoperative mechanical ventilation in recipients, preoperative pulmonary infection in recipients, intraoperative application of ECMO, and the detection of multi-drug resistant Acinetobacter baumannii, multi-drug resistant Klebsiella pneumoniae and maltoclomonas aeruginosa in postoperative sputum were independent risk factors for prolonged mechanical ventilation after lung transplantation. The AUC of the clinical prediction model in the training set and the validation set was 0.838 and 0.828 respectively, suggesting that the prediction model has good discrimination. In the decision curves of the training set and the validation set, the threshold probabilities of the curves in the range of 0.05-0.98 and 0.02-0.85 were higher than the two extreme lines, indicating that the model has certain clinical validity.ConclusionsFemale patients, Preoperative pulmonary infection, preoperative mechanical ventilation,blood type B, blood type O, application of ECMO assistance, multi-resistant Acinetobacter baumannii infection, multi-resistant Klebsiella pneumoniae infection, and multi-resistant Stenotrophomonas maltophilia infection are independent risk factors for PMV (prolonged mechanical ventilation) after lung transplantation.
Objective To investigate the factors influencing the occurrence of postoperative pulmonary complications (PPCs) in liver transplant recipients and to construct Nomogram model to identify high-risk patients. Methods The clinical data of 189 recipients who underwent liver transplantation at the General Hospital of Eastern Theater Command from November 1, 2019 to November 1, 2022 were retrospective collected, and divided into PPCs group (n=61) and non-PPCs group (n=128) based on the occurrence of PPCs. Univariate and multivariate logistic regression analyses were used to determine the risk factors for PPCs, and the predictive effect of the Nomogram model was evaluated by receiver operator characteristic curve (ROC) and calibration curve. Results Sixty-one of 189 liver transplant patients developed PPCs, with an incidence of 32.28%. Univariate analysis results showed that PPCs were significantly associated with age, smoking, Child-Pugh score, combined chronic obstructive pulmonary disease (COPD), combined diabetes mellitus, prognostic nutritional index (PNI), time to surgery, amount of bleeding during surgery, and whether or not to diuretic intraoperatively (P<0.05). Multivariate logistic regression analysis showed that age [OR=1.092, 95%CI (1.034, 1.153), P=0.002], Child-Pugh score [OR=1.575, 95%CI (1.215, 2.041), P=0.001], combined COPD [OR=4.578, 95%CI (1.832, 11.442), P=0.001], combined diabetes mellitus [OR=2.548, 95%CI (1.024, 6.342), P=0.044], preoperative platelet count (PLT) [OR=1.076, 95%CI (1.017, 1.138), P=0.011], and operative time [OR=1.061, 95%CI (1.012, 1.113), P=0.014] were independent risk factors for PPCs. The prediction model for PPCs which constructed by using the above six independent risk factors in Nomogram had an area under the ROC curve of 0.806. Hosmer and Lemeshow goodness of fit test (P=0.129), calibration curve, and decision curve analysis showed good agreement with Nomogram model. Conclusion The Nomogram model constructed based on age, Child-Pugh score, combined COPD, combined diabetes mellitus, preoperative PLT, and time of surgery can better identify patients at high risk of developing PPCs after liver transplantation.
The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
ObjectiveCombined with long non-coding RNA (lncRNA) to find a regression model that can be used to predict the survival rate of patients with colon cancer before operation.MethodsThe clinical information and gene expression information of patients with colon cancer were downloaded by using TCGA database. The differentially expressed lncRNAs in tumor and paracancerous tissues were screened out, and then combined with the clinical information of patients to construct Cox proportional hazard regression model.ResultsA total of 26 kinds of lncRNAs with statistical difference in gene expression between paracancerous tissues and tumor tissues were selected (P<0.05). Through repeated screening and comparison of prediction efficiency, the prediction model was finally selected, which was constructed by patients’ age, M stage, N stage, and three kinds of lncRNAs (ZFAS1, SNHG25, and SNHG7) gene expression level: age [HR=4.00, 95%CI: (1.48, 10.84), P=0.006], M stage [HR=3.96, 95%CI: (2.23, 7.04), P<0.001], N stage [HR=1.87, 95%CI: (1.24, 2.84), P=0.003], ZFAS1 gene expression level [HR=0.60, 95%CI: (0.41, 0.86), P=0.006], SNHG25 gene expression level [HR=0.85, 95%CI: (0.73, 1.00), P=0.045], and SNHG7 gene expression level [HR=2.32, 95%CI: (1.53, 3.52), P<0.001] were all independent risk factors for postoperative survival of patients with colon cancer. The area under the ROC curves for predicting 1, 3, and 5-year overall survival were 0.802, 0.828, and 0.771, respectiely, which had a good prediction ability.ConclusionThe predictive model constructed by the combination of ZFAS1, SNHG25, SNHG7 genes expression level with M stage, N stage, and age can better predict the overall survival rate of patients before operation, which can effectively guide clinical decision-making and choose the most suitable treatment method for patients.
ObjectiveTo review the recent research progress on prediction models for pancreatic fistula after pancreaticoduodenectomy and explore the potential application of prediction models in personalized treatment, aiming to provide useful reference information for clinical doctors to improve patient’s treatment outcomes and quality of life. MethodWe systematically searched and reviewed the literature on various prediction models for pancreatic fistula after pancreaticoduodenectomy in recent years domestically and internationally. ResultsSpecifically, the fistula risk score (FRS) and the alternative FRS (a-FRS), as widely used tools, possessed a certain degree of subjectivity due to the lack of an objective evaluation standard for pancreatic texture. The updated a-FRS (ua-FRS) had demonstrated superior predictive efficacy in minimally invasive surgery compared to the original FRS and a-FRS. The NCCH (National Cancer Center Hospital) prediction system, based on preoperative indicators, showed high predictive accuracy. Prediction models based on CT imaging informatics had improved the accuracy and reliability of predictions. Prediction models based on elastography had provided new perspectives for the assessment of pancreatic texture and the prediction of clinically relevant postoperative pancreatic fistula. The Stacking ensemble machine learning model contributed to the individualization and localization of prediction models. The existing pancreatic fistula prediction models showed satisfactory predictive efficacy, but there were still limitations in identifying high-risk patients for pancreatic fistula.ConclusionsAfter pancreaticoduodenectomy, pancreatic fistula remains a major complication that is difficult to overcome. The prevention of pancreatic fistula is crucial for improving postoperative recovery and reducing mortality rates. Future research should focus on the development and validation of pancreatic fistula prediction models, thereby enhancing their predictive power and increasing their predictive efficacy in different regional patients, providing a scientific basis for medical decision-making.
ObjectiveTo explore the influencing factors affecting lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) metastasis in papillary thyroid carcinoma (PTC) and construct a clinical nomogram prediction model to provide a reference for LN-prRLN dissection decision-making. MethodsThe clinical data of PTC patients admitted to the General Surgery Department of Baoding No.1 Central Hospital from January 2021 to December 2023 were retrospectively analyzed. Among them, 325 patients underwent LN-prRLN dissection, and they were divided into non-metastatic group (269 cases) and metastasis group (56 cases) according to the presence or absence of LN-prRLN metastasis. By comparing the differences of clinical and pathological characteristics between the two groups, the risk factors of LN-prRLN metastasis were analyzed and discussed, and then the nomogram prediction model of LN-prRLN metastasis was constructed with the risk factors, and the effectiveness of the model was verified and evaluated. ResultsIn 325 patients, 56 cases (17.23%) occurred LN-prRLN metastasis. The results of univariate analysis showed that gender, extrathyroidal extension, lymph nodes anterior to right recurrent laryngeal nerve (LN-arRLN) metastasis, location of cancer focus, and lateral lymph node metastasis (LLNM) were related to LN-prRLN metastasis of PTC (P<0.05). Multivariate binary logistic regression analysis showed that male [OR=3.878, 95%CI (1.192, 12.615)], with extrathyroidal extension [OR=2.836, 95%CI (1.036, 7.759)], with LN-arRLN metastasis [OR=10.406, 95%CI (3.225, 33.926)], right cancer focus [OR= 5.632, 95%CI (1.812, 17.504)] and with LLNM [OR=3.426, 95%CI (1.147, 10.231)] were the risk factors of LN-prRLN metastasis. Receiver operating characteristic curves of nomogram prediction model based on the above risk factors showed that the area under the curve was 0.865, 95%CI was (0.795, 0.934), Jordan index was 0.729, sensitivity was 0.873, and specificity was 0.856, which had higher prediction value. The C-index of Bootstrap test was 0.840 [95%CI (0.755, 0.954) ]. Calibration curves showed that predictive value close to the ideal curve, had good consistency. The clinical decision curve analysis showed that the model had good clinical prediction effect on LN-prRLN metastasis of PTC. ConclusionsMale, extrathyroidal extension, LN-arRLN metastasis, right cancer focus and LLNM are independent risk factors for LN-prRLN metastasis of PTC. The nomogram prediction model based on the above independent risk factors has high discrimination and calibration, which is helpful for surgeons to make clinical decisions.
Objective
To identify risk factors that affect the verification of malignancy in patients with solitary pulmonary nodule (SPN) and verify different prediction models for malignant probability of SPN.
Methods
We retrospectively analyzed the clinical data of 117 SPN patients with definite postoperative pathological diagnosis who underwent surgical procedure in China-Japan Friendship Hospital from March to September 2017. There were 59 males and 58 females aged 59.10±11.31 years ranging from 24 to 83 years. Imaging features of the nodule including maximum diameter, location, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis was used to establish statistical correlation between risk factors and postoperative pathological diagnosis. Receiver operating characteristic (ROC) curve was drawn by different predictive models for the malignant probability of SPN to get areas under the curves (AUC), sensitivity, specificity, positive predictive values, negative predictive values for each model. The predictive effectiveness of each model was statistically assessed subsequently.
Results
Among 117 patients, 93 (79.5%) were malignant and 24 (20.5%) were benign. Statistical difference was found between the benign and malignant group in age, maximum diameter, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC value was 0.813±0.051 (Mayo model), 0.697±0.066 (VA model) and 0.854±0.045 (Peking University People's Hospital model), respectively.
Conclusion
Age, maximum diameter of the nodule, serum level of CEA and Cyfra21-1, spiculation, lobulation and calcification are potential independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index into the prediction model as a new risk factor and adjusting the weight of age in the model may improve the accuracy of prediction for SPN.
ObjectiveTo construct a nomogram prediction model for pain crisis occurrence based on clinical data of patients with advanced non-small cell lung cancer (NSCLC), with the aim of providing a scientific basis for clinical decision-making.MethodsA total of patients with advanced non-small cell lung cancer (NSCLC) admitted to our hospital from January 2022 to January 2024 were selected as the study subjects. Demographic data, disease information, pain severity (assessed using the Numerical Rating Scale, NRS), psychological status (anxiety and depression assessed using the Self-Rating Anxiety Scale, SAS, and the Self-Rating Depression Scale, SDS), and social support (assessed using the Perceived Social Support Scale, PSSS) were collected. Univariate and multivariate Logistic regression analyses were performed to identify independent factors influencing pain crisis. The R software was used to visualize the nomogram, and the Receiver Operating Characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow test were employed to evaluate the discrimination and calibration of the model.ResultsA total of 500 questionnaires were distributed, and 448 qualified questionnaires were collected, with a qualification rate of 89.6%. The patients were divided into a modeling group (n=314) and a validation group (n=134). Univariate analysis showed significant differences between the pain crisis group and the pain-free group in terms of gender, age, education level, PSSS score, bone metastases, pleural metastases, depression and anxiety levels, and antitumor efficacy (P<0.05). Multivariate Logistic regression analysis showed that bone metastasis, PSSS score, age, depression, and anxiety levels were independent factors influencing pain crisis in patients with advanced NSCLC. Based on the results of the multivariate Logistic regression analysis, a nomogram prediction model for pain crisis occurrence in patients with advanced NSCLC was constructed. The Area Under the Curve (AUC) of the ROC curve in the modeling and validation groups was 0.948 and 0.921, respectively, indicating high discrimination of the model. The calibration curve and Hosmer-Lemeshow test results showed good consistency of the model.ConclusionThis study successfully constructed and validated a nomogram prediction model based on independent factors such as bone metastasis, social support (PSSS score), age, depression, and anxiety levels. This model can objectively and quantitatively predict the risk of pain crisis occurrence in patients with advanced NSCLC, providing a scientific basis for clinical decision-making. It helps identify high-risk patients with pain crisis in advance and optimize pain management strategies, thereby improving patient prognosis and quality of life.
Objective To externally validate a prediction model based on clinical and CT imaging features for the preoperative identification of high-grade patterns (HGP), such as micropapillary and solid subtypes, in early-stage lung adenocarcinoma, in order to guide clinical treatment decisions. Methods This study conducted an external validation of a previously developed prediction model using a cohort of patients with clinical stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model, which incorporated factors including tumor size, density, and lobulation, was assessed for its discrimination, calibration performance, and clinical impact. Results A total of 650 patients (293 males, 357 females; age range: 30-82 years) were included. The validation showed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). After recalibration, the model's calibration performance was improved. Decision curve analysis (DCA) indicated that at a threshold probability>0.6, the number of HGP patients predicted by the model closely approximated the actual number of cases. Conclusion This study confirms the effectiveness of a clinical and imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patients' prognosis. Despite certain limitations, these findings provide new directions for future research.
