Rotational gland dissection for refractory granulomatous mastitis: A single-center retrospective study.

BACKGROUND: Refractory granulomatous mastitis (RGM) is a chronic benign breast disease that commonly occurred in women of childbearing age and is usually treated with surgery, with numerous cases suffering from unsatisfied postoperative recovery of breast shape, high rates of surgical complications, and even high recurrence. This study tries to evaluate the efficacy of an innovative surgical procedure, the rotational gland dissection for the treatment of RGM. METHODS: 129 patients with RGM who underwent surgical treatment at the Second Affiliated Hospital of Xi'an Jiaotong University between Apr. 2017 and May. 2021 were retrospectively included in this study. The article analyzed the age, local symptoms, lesion location, and size, days in hospital, recurrence rate, and satisfaction rate of the patients. RESULTS: Patients ranged in age from 19 to 58 years, with a median age of onset of 32 years. In 63 patients (48.84%), their lesions coverage exceeded two quadrants, and 52.71% of patients had lesions larger than 10 cm2. The average days in hospital of patients was 7.5 days, and 85.27% of them were satisfied with their post-surgery breast appearance. Within the median follow-up of 56 months, only 3.10% of patients experienced a recurrence of mastitis on the operation side. CONCLUSION: This novel surgical procedure we created is an effective treatment for RGM with a high success rate, high patient satisfaction, and low recurrence rate, and is significantly superior to other studies for it has the largest sample size and longest follow-up in this field.

A novel web-based prognostic nomogram and the features influencing the curative effect of chemotherapy and radiotherapy for Paget's disease with invasive ductal carcinoma.

Paget's disease (PD) of the breast is a rare underlying malignant tumor. Approximately 50% to 60% of patients with mammary PD are concurrently diagnosed with invasive ductal carcinoma (PD-IDC), a condition associated with a worse prognosis than IDC without PD. Thus far, there has been a lack of an accurate and efficient prognostic model for PD-IDC, and the factors influencing the effectiveness of chemotherapy and radiotherapy for these patients remain unknown. In this study, we developed a web-based nomogram based on the data from the Surveillance Epidemiology and End Results (SEER) database. We subjected the model to a series of validation methods, including area under the curve (AUC) values, receiver operating characteristic curve (ROC) analysis, calibration curves, and decision curve analysis (DCA). Our results demonstrated that our model exhibited high discrimination, accuracy, and clinical applicability in predicting the overall survival (OS) of patients with PD-IDC (testing set: three- and five-year AUCs, 0.831 and 0.841, respectively). To further validate our nomogram, we used external data from both our institution and sister hospitals (external data: three- and five-year AUCs, 0.892 and 0.914, respectively). Multivariable Cox regression analysis identified several independent unfavorable prognostic factors for the OS of patients with PD-IDC, including increasing age, high grade, widowed status, higher T stages, and the presence of bone metastases. Furthermore, propensity score matching (PSM)-adjusted analysis was conducted, revealing that chemotherapy did not significantly improve the survival of patients with PD-IDC across molecular subtypes, except for those in the grade III/IV group, where it improved both OS and breast cancer-specific survival (BCSS). Additionally, our findings indicated that only patients with PD-IDC with T4 and N3 stages benefited from radiotherapy, leading to improvements in both OS and BCSS. In conclusion, we have comprehensively analyzed the clinical characteristics and prognosis of patients with PD-IDC, culminating in the development of a user-friendly web-based nomogram for predicting their survival. Our predictive model is not only highly accurate but also offers simplicity, making it accessible for healthcare providers and patients. Furthermore, our stratified analysis highlights that the pathological grade, rather than the molecular subtype, plays a pivotal role in determining the efficacy of chemotherapy in improving the prognosis for patients with PD-IDC, while radiotherapy confers survival benefits to patients with PD-IDC in T4 and N3 stages.

Lymph node and bone metastasis of pulmonary intestinal adenocarcinoma: A case report.

Pulmonary enteric adenocarcinoma (PEAC) is a rare pathological type of lung adenocarcinoma, accounting for ~0.6% of primary lung adenocarcinoma, which has similar morphological and immunohistochemical characteristics to colorectal adenocarcinoma. Making a certain differential diagnosis of PEAC based on morphological and immunohistochemical results is difficult. It is known that PEAC may metastasize to the pancreas, skin, soleus muscle and intestine, but no bone metastasis has been reported. At our department, a rare case of PEAC with bone and lymph node metastasis was previously diagnosed. The present case study reports on a 58-year-old male patient encountered at our hospital with pain in the lumbar, back and right iliac with no obvious cause. Chest CT indicated a space-occupying lesion in the left upper lung lobe, enlarged lymph nodes in the mediastinum and left lung, and partial vertebral bone destruction. Enhanced CT results indicated multiple foci of active bone metabolism in the body, while rectal colonoscopy showed no obvious abnormalities. Histopathological and immunohistochemical results after right iliac bone puncture suggested stage IV PEAC with secondary malignancies in bones, mediastinal lymph node, hilar lymph node and left supraclavicular lymph node.

Visualized machine learning models combined with propensity score matching analysis in single PR-positive breast cancer prognosis: a multicenter population-based study.

The characteristics of single PR-positive (ER-PR+, sPR+) breast cancer (BC) and its prognosis are not well elucidated due to its rarity and conflicting evidence. There is a lack of an accurate and efficient model for predicting survival, thereby rendering treatment challenging for clinicians. Whether endocrine therapy should be intensified in sPR+ BC patients was another controversial clinical topic. We constructed and cross-validated XGBoost models that showed high precision and accuracy in predicting the survival of patients with sPR+ BC cases (1-year: AUC=0.904; 3-year: AUC=0.847; 5-year: AUC=0.824). The F1 score for the 1-, 3-, and 5-year models were 0.91, 0.88, and 0.85, respectively. The models exhibited superior performance in an external, independent dataset (1-year: AUC=0.889; 3-year: AUC=0.846; 5-year: AUC=0.821). Further, intensified endocrine therapy did not provide a significant overall survival benefit compared to initial or no endocrine therapy (P=0.600, HR: 1.46; 95% CI: 0.35-6.17). Propensity-score matching (PSM)-adjusted data showed that there was no statistically significant difference in the prognosis between ER-PR+HER2+ and ER-PR-HER2+ BC. Patients having the ER-PR+HER2- subtype had a slightly worse prognosis than those with the ER-PR-HER2- subtype. In conclusion, XGBoost models can be highly reproducible and effective in predicting survival in patients with sPR+ BC. Our findings revealed that patients with sPR-positive BC may not benefit from endocrine therapy. Patients with sPR+ BC may benefit from intensive adjuvant chemotherapy compared to endocrine therapy.

Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients.

BACKGROUND: Occult breast cancer (OBC) is an uncommon malignant tumor and the prognosis and treatment of OBC remain controversial. Currently, there exists no accurate prognostic clinical model for OBC, and the treatment outcomes of chemotherapy and surgery in its different molecular subtypes are still unknown. METHODS: The SEER database provided the data used for this study's analysis (2010-2019). To identify the prognostic variables for patients with ODC, we conducted Cox regression analysis and constructed prognostic models using six machine learning algorithms to predict overall survival (OS) of OBC patients. A series of validation methods, including calibration curve and area under the curve (AUC value) of receiver operating characteristic curve (ROC) were employed to validate the accuracy and reliability of the logistic regression (LR) models. The effectiveness of clinical application of the predictive models was validated using decision curve analysis (DCA). We also investigated the role of chemotherapy and surgery in OBC patients with different molecular subtypes, with the help of K-M survival analysis as well as propensity score matching, and these results were further validated by subgroup Cox analysis. RESULTS: The LR models performed best, with high precision and applicability, and they were proved to predict the OS of OBC patients in the most accurate manner (test set: 1-year AUC = 0.851, 3-year AUC = 0.790 and 5-year survival AUC = 0.824). Interestingly, we found that the N1 and N2 stage OBC patients had more favorable prognosis than N0 stage patients, but the N3 stage was similar to the N0 stage (OS: N0 vs. N1, HR = 0.6602, 95%CI 0.4568-0.9542, p < 0.05; N0 vs. N2, HR = 0.4716, 95%CI 0.2351-0.9464, p < 0.05; N0 vs. N3, HR = 0.96, 95%CI 0.6176-1.5844, p = 0.96). Patients aged >80 and distant metastases were also independent prognostic factors for OBC. In terms of treatment, our multivariate Cox regression analysis discovered that surgery and radiotherapy were both independent protective variables for OBC patients, but chemotherapy was not. We also found that chemotherapy significantly improved both OS and breast cancer-specific survival (BCSS) only in the HR-/HER2+ molecular subtype (OS: HR = 0.15, 95%CI 0.037-0.57, p < 0.01; BCSS: HR = 0.027, 95%CI 0.027-0.81, p < 0.05). However, surgery could help only the HR-/HER2+ and HR+/HER2- subtypes improve prognosis. CONCLUSIONS: We analyzed the clinical features and prognostic factors of OBC patients; meanwhile, machine learning prognostic models with high precision and applicability were constructed to predict their overall survival. The treatment results in different molecular subtypes suggested that primary surgery might improve the survival of HR+/HER2- and HR-/HER2+ subtypes, however, only the HR-/HER2+ subtype could benefit from chemotherapy. The necessity of surgery and chemotherapy needs to be carefully considered for OBC patients with other subtypes.