Optimizing Outcomes through a Multidisciplinary Team Approach in Endometrial Cancer.
Lucia MangoneFrancesco MarinelliIsabella BiscegliaMaria Barbara BraghiroliValentina MastrofilippoAnnamaria PezzarossiFortunato MorabitoLorenzo AguzzoliVincenzo Dario MandatoPublished in: Healthcare (Basel, Switzerland) (2023)
This study aimed to assess the impact of a multidisciplinary team (MDT) approach on outcomes with endometrial cancer (EC) patients, utilizing 2013-2020 data from the Reggio Emilia Cancer Registry. Recurrence rate, treatments, and outcome indicators were compared between the MDT (319 cases) and non-MDT (324 cases) groups. Among 643 cases, 52.4% were over 65 years old, 98% had microscopic confirmation, and 73% were in stage I. Surgery was performed in 89%, with 41% receiving adjuvant therapies. Recurrence rates (10%) were similar between the two groups, but MDT patients who were older and predominantly in stage I exhibited 79% recurrence within one year (21% in the non-MDT group). Disease-free survival (DFS) showed no significant difference [HR 1.1; 95% CI 0.7-1.6], while differences in overall survival (OS) were notable [HR 1.5; 95% CI 1.0-2.4]. The 5-year OS rates were 87% and 79% in the MDT and non-MDT groups. Comparing the 2013-2015 to 2016-2020 study periods, a shift towards caring for older women, more advanced-stage patients, and those residing outside the metropolitan area, along with a greater number of relapsed cases (from 16% to 76%), were accounted for. These findings underscore the impact of an MDT on EC outcomes, highlighting the evolving patient demographics over time.
Keyphrases
- endometrial cancer
- free survival
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- palliative care
- minimally invasive
- acute myeloid leukemia
- early stage
- type diabetes
- acute lymphoblastic leukemia
- case report
- diffuse large b cell lymphoma
- electronic health record
- coronary artery bypass
- young adults
- machine learning
- papillary thyroid
- metabolic syndrome
- deep learning
- big data
- atrial fibrillation
- glycemic control