BACKGROUND: The alteration of the immune microenvironment in the axillary metastatic lymph nodes of luminal A breast cancer patients is still unclear.
METHODS: Postsurgical tissues from the enrolled luminal A BCs were divided into five categories: primary BC lesion at stage N0 (PL1), primary BC lesion at stage N1 (PL2), negative axillary lymph node at stage N0 BC (LN1), negative axillary lymph node at stage N1 BC (LN2), and positive axillary lymph node at stage N1 BC (LN3). The frequencies of positive immune markers (CD4, CD8, PD1, PD-L1, T-cell immunoglobulin and mucin domain 3 (TIM3), and forkhead box protein 3 (Foxp3)) in the above tissues were quantified by AKOYA Opal Polaris 7 Color Manual IHC Detection Kit.
RESULTS: A total of 50 female patients with luminal A BC were enrolled in this study. Among these patients, 23 had stage N1 disease, and 27 had stage N0 disease. Compared with that in the PL2 subgroup, the frequency of PD-1-positive cells was significantly greater in the PL1 subgroup, whether at the stromal or intratumoral level (P value < 0.05). Both the frequency of CD8 + T cells in LN1 and that in LN2 were significantly greater than that in LN3 (P value < 0.05). The frequency of TIM3 + T cells in LN1 was significantly greater than that in PL1 (P value < 0.05). The frequency of CD8 + TIM3 + T cells was significantly greater in both the LN2 and LN3 groups than in the PL2 group (P value < 0.05). The frequency of CD4 + Foxp3 + T cells was significantly greater in LN1 than in PL1 (P value < 0.05), which was the same for both LN3 and PL2 (P value < 0.05).
CONCLUSIONS: Increased frequencies of CD8 + PD1+, CD8 + TIM3 + and CD4 + Foxp3 + T cells might inhibit the immune microenvironment of axillary metastatic lymph nodes in luminal A breast cancer patients and subsequently promote lymph node metastasis.

暂无摘要。

BACKGROUND: Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results.
METHODS: From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy.
RESULTS: The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851.
CONCLUSIONS: In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

OBJECTIVE: To develop and validate a nomogram for predicting recurrence-free survival (RFS) for clinical T1/2 (cT1/2) clear cell renal cell carcinoma (ccRCC) patients after nephrectomy.
METHODS: Clinicopathological and survival data from 1289 cT1/2 ccRCC patients treated at the Second Hospital of Tianjin Medical University between 2017 and 2020 were included. Cox regression analysis was used to identify independent risk factors in 902 and 387 ccRCC patients in the training and validation cohorts, respectively, and construct the nomogram. The performance of the nomogram was assessed through calibration plots, time-dependent receiver operating characteristic (ROC) curves, C-index (concordance-index), and decision curve analysis (DCA). Kaplan-Meier curves were used to evaluate the probability of RFS in patients with different recurrence risks.
RESULTS: Age, tumor size, surgical approach, Fuhrman grade, and pT3a upstage were identified as independent predictors of RFS. The area under the curve (AUC) for the 3-year and 5-year RFS ROC curves were 0.791 and 0.835 in the training cohort, and 0.860 and 0.880 in the validation cohort. The DCA and calibration plots demonstrated the optimal application and excellent accuracy of the nomogram for predicting 3-year and 5-year RFS. Kaplan-Meier curves revealed significant differences in RFS among the three risk groups in both the training and validation cohorts. Clinically, the developed nomogram provides a more precise tool for risk stratification, enabling tailored postoperative management and surveillance strategies, ultimately aiming to improve patient outcomes.
CONCLUSIONS: We developed a nomogram for predicting RFS in cT1/2 ccRCC patients after nephrectomy with high accuracy. The clinical implementation of this nomogram can significantly enhance clinical decision-making, leading to improved patient outcomes and optimized resource utilization in the management of ccRCC.

OBJECTIVE: Accumulating evidence indicates that the use of antibiotics (ATBs) in cancer patients is potentially correlated with patient prognosis. Interestingly, the use of these agents is not uncommon in colorectal cancer (CRC) patients during surgery; however, their prognostic value in the clinic has never been addressed.
METHODS: Data on ATB use during surgery, including the cumulative defined daily dose (cDDD) and the number of categories, were collected. Differences in the clinical data between the low and high cDDD subgroups and between subgroups with ≤ 4 and >4 categories. Additionally, the disease-free survival (DFS) and overall survival (OS) among these subgroups and the specific categories were compared. Finally, a Cox proportional hazard model was used to validate the risk factors for the outcome.
RESULTS: The number of categories, rather than the cDDD, was a significant predictor of both DFS (P = 0.043) and OS (P = 0.039). Patients with obstruction are more likely to have a high cDDD, whereas older patients are more likely to have multiple categories. There were no significant differences in the DFS (log rank = 1.36, P = 0.244) or OS (log rank = 0.40, P = 0.528) between patients in the low- and high-cDDD subgroups, whereas patients with ≤ 4 categories had superior DFS (log rank = 9.92, P = 0.002) and OS (log rank = 8.30, P = 0.004) compared with those with >4 categories. Specifically, the use of quinolones was harmful to survival (DFS: log rank = 3.67, P = 0.055; OS: log rank = 5.10, P = 0.024), whereas the use of macrolides was beneficial to survival (DFS: log rank = 12.26, P < 0.001; OS: log rank = 9.77, P = 0.002). Finally, the number of categories was identified as an independent risk factor for both DFS (HR = 2.05, 95% CI: 1.35-3.11, P = 0.001) and OS (HR = 1.82, 95% CI: 1.14-2.90, P = 0.012).
CONCLUSIONS: The cDDD of ATBs during surgery in stage I-III CRC patients did not correlate with outcome; however, patients in multiple categories or a specific category are likely to have inferior survival. These results suggest that particular caution should be taken when selecting ATBs for these patients in the clinic.

BACKGROUND: This study aimed to evaluate the diagnostic abilities of the non-invasive serum biomarkers to predict liver fibrosis staging and evaluate the progress of hepatitis B.
METHODS: We enrolled 433 patients with chronic HBV infection had complete medical data available for the study, who underwent percutaneous liver biopsy. The extent of fibrosis was assessed using the modified METAVIR score. The predictive values of the non-invasive serum biomarkers were evaluated by the areas under the receiving operator characteristics curves (AUROCs) with 95% confidence intervals.
RESULTS: The proportion of males with progressive stages of liver fibrosis was relatively larger, and the average age of patients with cirrhosis stages is older than the non-cirrhotic stages. We found PLT, GGT, ALP, TB, FIB4 and GPR to be significantly associated with liver fibrosis in our cohort. GGT showed a sensitivity of 71.4% and specificity of 76.7% in distinguishing cirrhosis (F4) from non-cirrhotic stages (F1-3), with an AUROC of 0.775 (95%CI 0.711-0.840).The AUROCs of the GPR in distinguishing cirrhosis (F4) from non-cirrhotic stages (F1-3) was 0.794 (95%CI 0.734-0.853), but it had a lower sensitivity of 59.2%. Additionally, GGT, FIB4, and GPR could differentiate advanced fibrosis (F3-4) from non-advanced fibrosis (F1-2) among individuals with chronic hepatitis B, with AUROCs of 0.723 (95%CI 0.668-0.777), 0.729 (95%CI 0.675-0.782), and 0.760 (95%CI: 0.709-0.811) respectively.
CONCLUSIONS: GGT was a better biomarker to distinguish cirrhosis (F4) from non-cirrhotic stages (F1-3), while GPR was a better biomarker to identify advanced fibrosis (F3-4) and non-advanced fibrosis (F1-2) in patients with chronic hepatitis B.

BACKGROUND: The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA.
METHODS: Patient data from the SEER registry (2010-2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models.
RESULTS: The study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time.
CONCLUSIONS: Positive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system.

UNASSIGNED: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.
UNASSIGNED: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.
UNASSIGNED: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.
UNASSIGNED: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.

BACKGROUND: The oncological outcomes of fertility-sparing surgery (FSS) compared to radical surgery (RS) in patients with stage I epithelial ovarian cancer (EOC) remain a subject of debate. We evaluated the risk ratios (RRs) for outcomes in patients with stage I EOC who underwent FSS versus RS.
METHODS: We conducted a systematic search of PubMed, Web of Science, and Embase for articles published up to November 29, 2023. Studies that did not involve surgical procedures or included pregnant patients were excluded. We calculated the RRs for disease-free survival, overall survival, and recurrence rate. The quality of the included studies was assessed using the Cochrane Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) tool. The meta-analysis was registered on PROSPERO (CRD42024546460).
RESULTS: From the 5,529 potentially relevant articles, we identified 83 articles for initial screening and included 12 articles in the final meta-analysis, encompassing 2,906 patients with epithelial ovarian cancer. There were no significant differences between the two groups in disease-free survival (RR [95% confidence interval {CI}], 0.90 [0.51, 1.58]; P = 0.71), overall survival (RR [95% CI], 0.74 [0.53, 1.03]; P = 0.07), and recurrence rate (RR [95% CI], 1.10 [0.69, 1.76]; P = 0.68). In sensitivity analyses, the significant difference was observed only for overall survival (before exclusion: RR [95% CI], 0.74 [0.53-1.03], P = 0.07; after exclusion: RR [95% CI], 0.70 [0.50-0.99]; P = 0.04).
CONCLUSIONS: This is the first and only individual patient data meta-analysis comparing disease-free survival, overall survival, and recurrence rate of patients with early-stage epithelial ovarian cancer undergoing FSS and RS. FSS was associated with similar disease-free survival and risk of recurrence as RS. We hypothesized that the decreased overall survival in the FSS group could not be attributed to distant metastases from epithelial ovarian cancer.

BACKGROUND: Despite evidence supporting the high correlation of the novel platelet-to-albumin ratio (PAR) with survival in diverse malignancies, its prognostic relevance in nasopharyngeal carcinoma (NPC) remains underexplored. This study aimed to examine the link between PAR and overall survival (OS) in NPC and to establish a predictive model based on this biomarker.
METHODS: We retrospectively assembled a cohort consisting of 858 NPC patients who underwent concurrent chemoradiotherapy (CCRT). Utilizing the maximally selected log-rank method, we ascertained the optimal cut-off point for the PAR. Subsequently, univariate and multivariate Cox proportional hazards models were employed to discern factors significantly associated with OS and to construct a predictive nomogram. Further, we subjected the nomogram\'s predictive accuracy to rigorous independent validation.
RESULTS: The discriminative optimal PAR threshold was determined to be 4.47, effectively stratifying NPC patients into two prognostically distinct subgroups (hazard ratio [HR] = 0.53; 95% confidence interval [CI]: 0.28-0.98, P = 0.042). A predictive nomogram was formulated using the results from multivariate analysis, which revealed age greater than 45 years, T stage, N stage, and PAR score as independent predictors of OS. The nomogram demonstrated a commendable predictive capability for OS, with a C-index of 0.69 (95% CI: 0.64-0.75), surpassing the performance of the conventional staging system, which had a C-index of 0.56 (95% CI: 0.65-0.74).
CONCLUSIONS: In the context of NPC patients undergoing CCRT, the novel nutritional-inflammatory biomarker PAR emerges as a promising, cost-efficient, easily accessible, non-invasive, and potentially valuable predictor of prognosis. The predictive efficacy of the nomogram incorporating the PAR score exceeded that of the conventional staging approach, thereby indicating its potential as an enhanced prognostic tool in this clinical setting.