The efficacy of immediate lymphatic reconstruction after axillary lymph node dissection - A meta-analysis.

Breast cancer related lymphedema (BCRL) is a common complication following mastectomy and axillary lymph node dissection (ALND). Patients with BCRL are often fraught with restricted mobility of the upper limb and higher risk of infections which negatively impact their quality of life. Immediate lymphatic reconstruction (ILR) has gained popularity in recent years due to its positive results in lowering BCRL rates. The objective of this study is to summarize evidence from the current available literature on the efficacy of ILR in preventing BCRL following ALND. A comprehensive search across PubMed and Web of Science was conducted. Studies involving ILR performed at the time of ALND for breast cancer were included. Exclusion criteria included secondary lymphatic reconstruction for established BCRL, literature reviews, animal studies, case reports and studies detailing surgical technique. To evaluate the efficacy of ILR, only studies with both intervention groups (ILR) and control groups were included. A systematic search yielded data from 10 studies and 1487 breast cancer patients who underwent ALND at the time of surgery. Meta-analysis revealed that in the ILR group, 50 of 637 (7.85 %) patients developed BCRL whereas in the control group, 177 of 850 patients (20.8 %) developed BCRL. Patients treated with ILR in this analysis had a relative risk of 0.31 (95 % CI, 0.19 to 0.51) for developing BCRL when compared to the controls (p < 0.0001). ILR decreases the risk of developing lymphedema following ALND for breast cancer.

被引量：- 发表：1970

Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.

Precise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selection of subsequent treatment strategies. Most current predictive models for pCR focus primarily on pre-treatment factors, neglecting the dynamic systemic changes that occur during neoadjuvant chemoradiotherapy, and are constrained by low accuracy and lack of integrity. This study devised a novel predictor of pCR using dynamic alterations in systemic inflammation-nutritional marker indexes (SINI) during neoadjuvant therapy and developed a machine-learning model to predict pCR. Two cohorts of patients with LARC from center one from 2012 to 2017 and from center two from 2020 to 2023 were integrated for analysis. This study compared dynamic changes in blood indexes before and after neoadjuvant therapy and surgical operation. A least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to mitigate collinearity and identify key indexes, constructing the SINI. Univariate and multiple logistic regression analyses were used to identify the independent risk factors associated with pCR. Additionally, 10 machine learning algorithms were employed to develop predictive models to assess risk. The hyperparameters of the machine learning models were optimized using a random search and 10-fold cross-validation. The models were assessed by examining various metrics, including the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal and external validation cohorts. Additionally, Shapley's additive explanations (SHAP) were employed to interpret the machine learning models. The study cohort comprised 677 patients from the center one and 224 patients from the center two. Six key indexes were identified, and a predictive index, SINI, was constructed. Univariate and multiple logistic regression analyses revealed that SINI, clinical T-stage, clinical N-stage, tumor size, and the distance from the anal verge were independent risk factors for pCR in patients with LARC following NCRT. The mean AUC value of the extreme gradient boosting (XGB) model in the 10-fold cross-validation of the training set was 0.877. The XGB model demonstrated superior performance in the internal and external validation sets. Specifically, in the internal test set, the XGB model achieved an AUC of 0.86, AUPRC of 0.707, accuracy of 0.82, and precision of 0.80. In the external validation set, the XGB model exhibited an AUC of 0.83, AUPRC of 0.702, accuracy of 0.81, and precision of 0.81. Additionally, the predictions generated by the XGB model were analyzed using SHAP. This study involved developing and validating an XGB model using SINI to predict pCR in patients with LARC. Besides, a SINI-based machine learning model shows promise in accurately predicting pCR following NCRT in patients with resectable LARC, offering valuable insights for personalized treatment approaches.

被引量：- 发表：1970

Redefining melanoma surveillance: The controversial utility of serum S100B and the potential of liquid biopsy.

被引量：- 发表：1970

Reply to: Enhancing frailty assessment and management in surgical oncology.

被引量：- 发表：1970

Reply to: Comparison of the LiMAx test vs. the APRI+ALBI score - Incorrect comparison parameters lead to questionable results.

被引量：- 发表：1970