Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

A Novel Endoscopic Approach for Treating Breast Cancer: Haigui-1 Hole.

OBJECTIVE: Endoscopic surgery is an effective technique for preserving the nipple and areola, as well as for sentinel lymph node biopsy and breast implant reconstruction. However, the technical challenges associated with endoscopic surgery have limited its widespread adoption. METHODS: In the normal single-port endoscopic surgery, the ultrasonic knife was accessed through the retractor. In our modified procedure, a tiny 5 mm incision was made at the lateral margin underneath the breast, serving as the second entry port for the ultrasonic scalpel, which was referred to as the "Haigui-1 hole". Preoperative and postoperative indicators such as blood loss, operative time, and postoperative drainage volume were collected. Differences between parameters were compared using Student's t test. RESULTS: Endoscopic surgery with the assistance of the "Haigui-1 hole" led to preserved breast aesthetics with minimal scarring. Moreover, "Haigui-1 hole" surgery significantly reduced the operation time, intraoperative bleeding, and postoperative drainage volume compared to normal single-port endoscopic surgery. CONCLUSION: The "Haigui-1 hole" procedure, which involves the addition of a second entrance to improve the maneuverability of the ultrasonic knife, is worthy of further promotion.

Effect of mastectomy on gut microbiota and its metabolites in patients with breast cancer.

Background: The relationship between gut microbiota and breast cancer has been extensively studied; however, changes in gut microbiota after breast cancer surgery are still largely unknown. Materials and methods: A total of 20 patients with breast cancer underwent routine open surgery at the First Affiliated Hospital of Hainan Medical College from 1 June 2022 to 1 December 2022. Stool samples were collected from the patients undergoing mastectomy for breast cancer preoperatively, 3 days later, and 7 days later postoperatively. The stool samples were subjected to 16s rRNA sequencing. Results: Surgery did not affect the α-diversity of gut microbiota. The ß-diversity and composition of gut microorganisms were significantly affected by surgery in breast cancer patients. Both linear discriminant analysis effect size (LEfSe) analysis and between-group differences analysis showed that surgery led to a decrease in the abundance of Firmicutes and Lachnospiraceae and an increase in the abundance of Proteobacteria and Enterobacteriaceae. Moreover, 127 differential metabolites were screened and classified into 5 categories based on their changing trends. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed significant changes in the phenylalanine metabolic pathway and exogenous substance metabolic pathway. Eight characterized metabolites were screened using ROC analysis. Conclusion: Our study found that breast cancer surgery significantly altered gut microbiota composition and metabolites, with a decrease in beneficial bacteria and an increase in potentially harmful bacteria. This underscores the importance of enhanced postoperative management to optimize gut microbiota.

Identifying the Preoperative Radiological Risk Features in Patients with Leptomeningeal Carcinomatosis Undergoing Cerebrospinal Fluid Drainage.

BACKGROUND: Patients with leptomeningeal carcinomatosis (LMC) experience a poor prognosis and rapid progression, and cerebrospinal fluid drainage (CSFD) is used to manage intracranial hypertension and hydrocephalus in LMC patients. This study aims to describe a novel discovery of preoperative radiological features in patients who underwent CSFD for LMC. METHODS: A retrospective review was conducted during the past 5 years of LMC patients with intracranial hypertension and hydrocephalus who underwent CSFD. We evaluated the patients' preoperative radiological features, clinical characteristics, and survival times. RESULTS: A total of 36 patients were included. Of the 36 patients, 34 underwent ventriculoperitoneal shunting, and 2 patients underwent only external ventricular drainage due to rapid progression. The median preoperative Karnofsky performance scale score was 40.0 (interquartile range [IQR], 20.0-40.0). The median survival time after surgery was 5 months (IQR, 0.00-10.43 months). Of the 36 patients, 24 (66.7%) had supratentorial cerebral edema before surgery, including 14 patients (38.9%) with features of disproportionately enlarged subarachnoid space hydrocephalus (DESH). Four patients (11.1%) exhibited cerebellar swelling and had a median survival time of 0.27 month (IQR, 0.00-0.56 month). Nine patients (25%) have enhancement lesions on the cerebellum. The survival curve analysis shows that patients with features of cerebellar enhancement have shorter survival times than other patients. Patients with DESH features have longer survival times compared with those with global cerebral edema. CONCLUSIONS: Patients with radiological features of cerebellar enhancement have shorter postoperative survival than other patients; however, those with supratentorial cerebral edema, especially features of DESH, could benefit from CSFD. Patients with cerebellar swelling should avoid undergoing CSFD.

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.