With clinical medicine science transforming from traditional medicine to evidence-based medicine, how to practice evidence-based medicine has become a new challenge to clinical doctors. Therapy studies play an important part in clinical studies and how to practice evidence-based medicine in the therapy of diseases is an important question that doctors are concerned. This paper will introduce as on how to practice evidence-based medicine in the therapy of diseases.
Evidence-based education (EBE) is the integration of professional wisdom and the best available experimental evidence to make educational guiding decisions. EBE aims to improve the scientificity and effectiveness of educational policies, decisions and practices through the combination of evidence-based research and personal professional experience, so as to improve the quality of education and teaching. This paper focuses on the definition, connotation, characteristics, implementation principles and the background of EBE.
Objective To investigate the effect of radiotherapy after neoadjuvant chemotherapy and modified radical surgery on breast cancer specific survival (BCSS) of patients with stage cT1–2N1M0 breast cancer. Methods A total of 917 cT1–2N1M0 stage breast cancer patients treated with neoadjuvant chemotherapy and modified radical surgery from 2010 to 2017 were extracted from the The Surveillance, Epidemiology, and End Results (SEER) database. Of them 720 matched patients were divided into radiotherapy group (n=360) and non-radiotherapy group (n=360) by using propensity score matching (PSM). Cox proportional hazard regression model was used to explore the factors affecting BCSS. Results Patients were all interviewed for a median follow-up of 65 months, and the 5-year BCSS was 91.9% in the radiotherapy group and 93.2% in the non-radiotherapy group, there was no significant difference between the 2 groups (χ2=0.292, P=0.589). The results were the same in patients with no axillary lymph node metastasis, one axillary lymphnode metastasis, two axillary lymph node metastasis and 3 axillary lymph node metastasis group (χ2=0.139, P=0.709; χ2=0.578, P=0.447; χ2=2.617, P=0.106; χ2=0.062, P=0.803). The result of Cox proportional hazard regression analysis showed that, after controlling for Grade grade, time from diagnosis to treatment, efficacy of neoadjuvant chemotherapy, number of positive axillary lymph nodes, molecular typing, and tumor diameter at first diagnosis, radiotherapy had no statistically significant effect on BCSS [HR=1.048, 95%CI (0.704, 1.561), P=0.817]. Conclusions The effect of radiotherapy on the BCSS of patients with stage cT1–2N1M0 breast cancer who have received neoadjuvant chemotherapy and modified radical surgery with 0 to 3 axillary lymph nodes metastases is limited, but whether to undergo radiotherapy should still be determined according to the comprehensive risk of individual tumor patients.
There are a great number of uncertainties in medical practice, causing considerable difficulties in medical activities such as diagnosis and prognostic prediction. Neural-fuzzy system (NFS) combines the advantages of artificial neural networks and fuzzy logic very well, and has become a new type of artificial intelligence model which is capable of acquiring knowledge from data and expressing it in the form of fuzzy rules. Because of its strong capability of classification and processing fuzzy information, NFS is more and more used in medical practice. Adaptive neural-fuzzy inference system (ANFIS) is one of the most popular forms of NFS. This review focuses on the use of ANFIS in medical practice.
ObjectiveTo evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with multislice computed tomography (MSCT) in the evaluation of neoadjuvant chemotherapy (NACT) for breast cancer. MethodsThe clinical, imaging, and pathological data of breast cancer patients who received NACT in the Affiliated Hospital of Southwest Medical University from February 2019 to August 2021 were retrospectively collected. Based on the results of postoperative pathological examination, the patients were assigned into significant remission (Miller-Payne grade Ⅰ–Ⅲ) and non-significant remission (Miller-Payne grade Ⅳ–Ⅴ). The variables with statistical significance by univariate analysis or factors with clinical significance judged based on professional knowledge were included to conduct the logistic regression multivariate analysis to screen the risk factors affecting the degree of pathological remission after NACT. Then, the screened risk factors were used to establish a prediction model for the degree of pathological remission of breast cancer after NACT, and the efficacy of this model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. ResultsAccording to the inclusion and exclusion criteria, a total of 211 breast cancer patients who received NACT were collected, including 116 patients with significant remission and 95 patients with non-significant remission. Logistic regression multivariate analysis results showed that the human epidermal growth factor receptor 2 positive, lower early enhancement rate after NACT, lower arterial stage net increment after NACT, and lower CT value of arterial phase of lesions would increase the probability of significant remission in patients with breast cancer after NACT (P<0.05). The area under the ROC curve of the model for predicting the degree of pathological remission of breast cancer after NACT was 0.984, the specificity was 93.7%, and the sensitivity was 95.7%. The calibration curve showed that the model result fit well with the actual result, and the DCA result showed that it had a high clinical net benefit value. ConclusionFrom the results of this study, DCE-MRI combined with MSCT enhanced scanning has a good predictive value for pathological remission degree after NACT for breast cancer, which can provide clinical guidance for further treatment.
ObjectiveTo construct a prediction model for the postoperative recurrence risk of granulomatous lobular mastitis (GM) based on multiple systemic inflammatory indicators and clinicopathologic characteristics, with the aim of guiding clinical treatment. MethodsThe GM patients who underwent lesion resection at Sichuan Provincial Hospital for Women and Children from January 2017 to March 2024 were retrospectively collected. The univariate and multivariate logistic regression analyses were used to screen the risk factors for recurrence after GM lesion resection, and a nomogram prediction model was constructed based on the risk factors. The test level was set at α=0.05. ResultsA total of 533 patients with GM were included in this study, of whom 118 cases (22.1%) developed postoperative recurrence. The results of multivariate analysis showed that the not taking oral bromocriptine, having microabscess formation in postoperative pathological examination, systemic immune inflammation index (SII) >789.0×109/L, and immunoglobulin E (IgE) >64.4 U/mL were the independent risk factors for recurrence after GM lesion resection. Based on the risk factors, the nomogram predicting recurrence risk was constructed. The area under the receiver operating characteristic curve (95%CI) was 0.913 (0.895, 0.932), and its sensitivity and specificity were 90.5% and 88.9%, respectively. The calibration curve showed that the probability of recurrence after GM lesion resection predicted by using the nomogram was highly consistent with the actual recurrence probability. The decision curve analysis showed that the nomogram had a good clinical net benefit. ConclusionsThe findings of this study suggest that close postoperative monitoring for recurrence is warranted in patients who did not receive oral bromocriptine treatment, presented with microabscess formation on pathological examination, and exhibited elevated SII and IgE level. The postoperative GM recurrence prediction nomogram model constructed based on risk factors demonstrates a good predictive performance, providing a valuable reference for early treatment and management strategies of GM.