Currently, the reference standard treatment for locally advanced rectal cancer, meaning tumours that have grown deeper into the layers of the rectal wall and/or involved nearby lymph nodes, but have not yet spread to the rest of the body – is to submit patients to chemotherapy and radiotherapy before surgery. This “total neoadjuvant therapy” reduces the risk of the cancer coming back in the rectum and can lower the risk of metastases, but has a cost for the patients because of the detrimental effects of radiation. 

The Lucerne Consortium, a multidisciplinary group of European cancer societies and researchers that creates guidelines for early breast cancer care, published, in the December issue of the journal The Lancet Oncology, the conclusions of its Lucerne Toolbox 3 initiative, which pave the way to better treatment for patients in early stages of breast cancer.

The Lucerne consortium has already presented consensus recommendations for locoregional therapy after primary systemic therapy and for axillary management in early breast cancer. Now comes the turn of digital tools and AI.

With more than three decades of pioneering work in breast cancer prevention, Dr Andrea De Censi, has joined the Champalimaud Foundation as the new Director of the Breast Unit. An internationally recognised medical oncologist and a leader in chemoprevention, Dr De Censi brings a vision that integrates prevention, clinical care and translational research in a patient-centred model.

Between February 2017 and December 2020, the team performed approximately 200 robotic procedures. Over the subsequent five years, from December 2020 to December 2025, this number more than doubled, reaching 500 procedures, an achievement that reflects the consolidation of a nationally recognised reference programme with extensive experience in robotic gynaecological surgery.

The meeting brought together over 200 specialists from 25 countries, representing a truly international effort to advance research and clinical practice in the field of kidney diseases associated with monoclonal gammopathies.

The scientific programme featured a rich series of sessions addressing key areas in the field, including new pathophysiological insights into monoclonal gammopathies and kidney disease, innovations in diagnostic tools and biomarkers, emerging therapeutic strategies, and the latest updates from ongoing clinical trials.

Sleep disturbances in breast cancer patients undergoing neoadjuvant chemotherapy – designed to shrink the tumours prior to surgery – are one of the common side effects of these treatments, which can not only exacerbate other side effects, but also impact prognosis. One of the possible, non-pharmacological strategies to mitigate these disturbances is thought to be physical exercise.

Traditional manual analysis of medical reports is extremely time consuming but crucial for accurate diagnosis and effective patient screening. 

In a recently published scientific study, Luís Elvas - Health Data Engineer in the Breast Cancer Research Programme at the Champalimaud Foundation - and colleagues, have shown that fine-tunning language models can be used for automated analysis of medical reports. This approach can streamline daily clinical workflows, improve categorisation of diseases in patients and offer a tool for new clinical research analysis.

Lung cancer is the leading cause of cancer-related deaths. One of the reasons for this is that it is difficult to detect and diagnose at an early stage because the currently available methods are impracticable at the population level. 

It was a happy coincidence: the final text of the Recommendations of the Multidisciplinary and Multiprofessional Working Group on the Clinical Use of Psychedelic Substances was released on the same day that the Portuguese National Health System announced its decision to fully reimburse the treatment of resistant depression with a substance called esketamine.

Analysing these scans can also be time-consuming and complex, as doctors need to pore over countless images, looking for often tiny details.

So, any new imaging analysis technique that is faster and more precise is always welcome: a new paper (recently published in the Journal of Nuclear Medicine) reveals that the Champalimaud Foundation’s Nuclear Medicine-Radiopharmacology Unit has managed just that by using Deep Learning (DL) Artificial Intelligence (AI).