Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance.

A multidisciplinary team and patient perspective on omission of surgery after neoadjuvant systemic therapy for early breast cancer: A European Society of Surgical Oncology (ESSO) Research Academy survey.

Background: Surgical de-escalation aims to reduce morbidity without compromising oncological outcomes. Trials to de-escalate breast cancer (BC) surgery among exceptional responders after neoadjuvant systemic therapy (NST) are ongoing. Combined patient and clinician insights on this strategy are unknown.