ObjectiveTo evaluate the effects of multi-disciplinary diagnosis and treatment model based on doctor-patient shared decision making on treatment outcomes, quality of life and postoperative complications of breast cancer patients. MethodsA total of 100 breast cancer patients were included in this study through a prospective randomized controlled design, and were randomly divided into control group and intervention group, with 50 patients in each group. The control group received traditional treatment mode, while the intervention group implemented a multidisciplinary treatment mode based on doctor-patient sharing decision making. The results of treatment, quality of life and postoperative complication rate were compared between the two groups. ResultsThe completion rate of adjuvant radiotherapy and chemotherapy in the intervention group was 94.0%, which was higher than that in the control group (80.0%), and the difference was statistically significant (P=0.037). The satisfaction rate of postoperative breast appearance in the intervention group was 90.0%, which was higher than that in the control group (60.0%), with statistical significance (P<0.001). There was no significant difference in grade Ⅲ/Ⅳ toxicity between the two groups (P>0.05). After treatment, the scores of patients’ quality of life in the intervention group were higher than those in the control group, and the difference was statistically significant (P<0.05). The incidence of postoperative complications in the intervention group was 6.0%, which was lower than that in the control group (22.0%), and the difference was statistically (P=0.021). ConclusionsThe application of multidisciplinary diagnosis and treatment model based on doctor-patient sharing decision-making in the treatment of breast cancer patients has significantly improved the treatment effect and quality of life, and effectively reduced the rate of postoperative complications. This model provides a new approach to the treatment of breast cancer that is more personalized, comprehensive and efficient.
With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.