Background As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? Material and Methods We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. Results Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy “hormonal treatment”. Conclusions Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.

Introduction: International guidelines recommend genetic testing for women with familial breast cancer at an expected prevalence of pathogenic germline variants (PVs) of at least 10%. In a study sample of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), we have previously shown that women with TNBC diagnosed before the age of 50 years but without a family history of breast or ovarian cancer (sTNBC) meet this criterion. The present study investigates the PV prevalence in BRCA1, BRCA2 and nine additional can-cer predisposition genes in an extended sTNBC study sample including a cohort of women with a later age at sTNBC diagnosis. Patients and methods: In 1600 women with sTNBC (median age at diagnosis 41 years, range 19-78 years) we investigated the association between age at diagnosis and PV occur-rence in cancer predisposition genes using logistic regression. Results: 260 sTNBC patients (16.2%) were found to have a PV in cancer predisposition genes (BRCA1: n=170 [10.6%]; BRCA2: n=46 [2.9%], other: n=44 [2.8%]). The PV prevalence in women diagnosed between 50 and 59 years (n=194) was 11.3% (22/194). Logistic regression showed a significant increase in PV prevalence with decreasing age at diagnosis (OR 1.41 per 10 years younger age at diagnosis; 95%CI 1.21-1.65; p <0.001). The PV prevalence pre-dicted by the model was above 10% for diagnoses before the age of 56.8 years. Conclusion: Based on the data presented, we recommend genetic testing by gene panel analysis for sTNBC patients diagnosed before the age of 60 years. Due to the still wide confi-dence interval (7.6-16-6), we recommend the implementation within the framework of a knowledge-generating care concept.

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