BackgroundBreast cancer (BC) is the most common malignant cancer. The prognosis of patients differs according to the location of distant metastasis, with pleura being a common metastatic site in BC. Nonetheless, clinical data of patients with pleural metastasis (PM) as the only distant metastatic site at initial diagnosis of metastatic BC (MBC) are limited.Patient cohort and methodsThe medical records of patients who were hospitalized in Shandong Cancer Hospital between January 1, 2012 and December 31, 2021 were reviewed, and patients eligible for the study were selected. Survival analysis was conducted using Kaplan–Meier (KM) method. Univariate and multivariate Cox proportional-hazards models were used to identify prognostic factors. Finally, based on these selected factors, a nomogram was constructed and validated.ResultsIn total, 182 patients were included; 58 (group A), 81 (group B), and 43 (group C) patients presented with only PM, only lung metastasis (LM), and PM combined with LM, respectively. The KM curves revealed no significant difference in overall survival (OS) among the three groups. However, in terms of survival after distant metastasis (M-OS), the difference was significant: patients with only PM exhibited the best prognosis, whereas those with PM combined with LM exhibited the worst prognosis (median M-OS: 65.9, 40.5, and 32.4 months, respectively; P = 0.0067). For patients with LM in groups A and C, those with malignant pleural effusion (MPE) exhibited significantly worse M-OS than those without MPE. Univariate and multivariate analyses indicated that primary cancer site, T stage, N stage, location of PM, and MPE were independent prognostic factors for patients with PM without other distant metastasis. A nomogram prediction model incorporating these variables was created. According to the C-index (0.776), the AUC values of the 3-, 5-, and 8-year M-OS (0.86, 0.86, and 0.90, respectively), and calibration curves, the predicted and actual M-OS were in good agreement.ConclusionBC patients with PM only at the first diagnosis of MBC exhibited a better prognosis than those with LM only or PM combined with LM. We identified five independent prognostic factors associated with M-OS in this subset of patients, and a nomogram model with good predictive efficacy was established.
This study is aimed to develop and validate a novel nomogram model that can preoperatively predict axillary lymph node pathological complete response (pCR) after NAT and avoid unnecessary axillary lymph node dissection (ALND) for breast cancer patients. A total of 410 patients who underwent NAT and were pathologically confirmed to be axillary lymph node positive after breast cancer surgery were included. They were divided into two groups: patients with axillary lymph node pCR and patients with residual node lesions after NAT. Then the nomogram prediction model was constructed by univariate and multivariate logistic regression. The result of multivariate logistic regression analysis showed that molecular subtypes, molybdenum target (MG) breast, computerized tomography (CT) breast, ultrasound (US) axilla, magnetic resonance imaging (MRI) axilla, and CT axilla (all p < 0.001) had a significant impact on the evaluation of axillary lymph node status after NAT. The nomogram score appeared that AUC was 0.832 (95% CI 0.786–0.878) in the training cohort and 0.947 (95% CI 0.906–0.988) in the validation cohort, respectively. The decision curve represented that the nomogram has a positive predictive ability, indicating its potential as a practical clinical tool.
ObjectivesThe aim of this study was to identify the factors for local–regional recurrence (LRR) after breast-conserving therapy (BCT). We established a practical nomogram to predict the likelihood of LRR after BCT based on hematological parameters and clinicopathological features.MethodsA retrospective analysis was performed on 2,085 consecutive breast cancer patients who received BCT in Shandong Cancer Hospital from 2006 to 2016, including 1,460 patients in the training cohort and 625 patients in the validation cohort. Univariate and multivariate analyses were performed based on hematological parameters (fibrinogen, platelets, mean platelet volume, neutrophils, monocytes, and lymphocytes) and clinicopathological characteristics to identify the independent factors for LRR. Subsequently, a nomogram for predicting LRR was established by logistic regression analysis. The nomogram was validated in 625 patients in the validation cohort.ResultsDuring the median follow-up period of 66 months, 44 (3.01%) patients in the training cohort and 19 (3.04%) patients in the validation cohort suffered from LRR. Multivariate analysis showed six independent factors related to LRR, including molecular subtype, pathological N stage, re-resection, radiotherapy or not, platelet count*MPV*fibrinogen (PMF), and neutrophil count/lymphocyte count ratio (NLR). Six variables were entered into logistic regression to establish the nomogram for predicting LRR. The nomogram of LRR showed excellent discrimination and prediction accuracy. The area under the receiver operating characteristic curve (AUC) was 0.89 (p < 0.001, 95% CI = 0.83, 0.95) in the training cohort and 0.88 (p < 0.001, 95% CI = 0.8, 0.96) in the validation cohort. Calibration curves for the prediction model in the training and validation cohorts both demonstrated satisfactory consistency between the nomogram-predicted and actual LRR.ConclusionThe combination of hematological parameters and clinicopathological characteristics can predict LRR after BCT. The predictive nomogram based on preoperative and postoperative indicators of BCT might serve as a practical tool for individualized prognostication. More prospective studies should be performed to verify the model.
This study is aimed to develop and validate a novel nomogram model that can preoperatively predict axillary lymph node pathological complete response (pCR) after NAT and avoid unnecessary axillary lymph node dissection (ALND) for breast cancer patients. A total of 410 patients who underwent NAT and were pathologically confirmed to be axillary lymph node positive after breast cancer surgery were included. They were divided into two groups: patients with axillary lymph node pCR and patients with residual node lesions after NAT. Then the nomogram prediction model was constructed by univariate and multivariate logistic regression. The result of multivariate logistic regression analysis showed that molecular subtypes, molybdenum target (MG) breast, computerized tomography (CT) breast, ultrasound (US) axilla, magnetic resonance imaging (MRI) axilla, and CT axilla (all p < 0.001) had a significant impact on the evaluation of axillary lymph node status after NAT. The nomogram score appeared that AUC was 0.832 (95%CI: 0.786-0.878) in the training cohort and 0.947 (95%CI: 0.906-0.988) in the validation cohort, respectively. The decision curve represented that the nomogram has a positive predictive ability, indicating its potential as a practical clinical tool.
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