Prediction of the need of enteral nutrition during radiation therapy for head and neck cancers.

Introduction: Patients with a head and neck (HN) cancer undergoing radiotherapy risk critical weight loss and oral intake reduction leading to enteral nutrition. We developed a predictive model for the need for enteral nutrition during radiotherapy in this setting. Its performances were reported on a real-world multicentric cohort.

Clinical, Dosimetric and Radiomic Features Predictive of Lung Toxicity After (Chemo)Radiotherapy.

Background: Treatment of locally advanced non small cell lung cancer (LA-NSCLC) is based on (chemo)radiotherapy, which may cause acute lung toxicity: radiation pneumonitis (RP). Its frequency seems to increase by the use of adjuvant durvalumab therapy.

Aims: To identify clinical, dosimetric, and radiomic factors associated with grade (G)≥2 RP and build a prediction model based on selected parameters.

Malnutrition and radiation therapy in head and neck cancers, a systematic review on reported definitions and associated factors.

Radiation therapy is a major treatment in head and neck cancers that can induce mucositis, pain, and dysgeusia that could impair oral intake and lead to weight loss and malnutrition. Intensity modulation has diminished toxicity of radiation therapy. We performed a review to assess the rate of malnutrition and how malnutrition was defined across cohorts of patients undergoing modern curative radiation therapy.

Nodal radiotherapy for prostate adenocarcinoma recurrence: predictive factors for efficacy.

Background: Nodes are the second site for prostate cancer recurrence. Whole-pelvic radiotherapy (WPRT) has shown superiority over nodal stereotactic body radiotherapy (SBRT) in two retrospective cohorts. We aimed to compare both modalities and assess factors associated with treatment outcomes.

Patients Facing Large Language Models in Oncology: A Narrative Review.

The integration of large language models (LLMs) into oncology is transforming patients' journeys through education, diagnosis, treatment monitoring, and follow-up. This review examines the current landscape, potential benefits, and associated ethical and regulatory considerations of the application of LLMs for patients in the oncologic domain.

Artificial intelligence and radiotherapy: Evolution or revolution?

The integration of artificial intelligence, particularly deep learning algorithms, into radiotherapy represents a transformative shift in the field, enhancing accuracy, efficiency, and personalized care. This paper explores the multifaceted impact of artificial intelligence on radiotherapy, the evolution of the roles of radiation oncologists and medical physicists, and the associated practical challenges. The adoption of artificial intelligence promises to revolutionize the profession by automating repetitive tasks, improving diagnostic precision, and enabling adaptive radiotherapy.

Fully automated radiotherapy treatment planning: A scan to plan challenge.

Background And Purpose: Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible.

Materials And Methods: A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up.

Radiation Therapy for Locally Advanced Prostate Cancer: How Do We Treat Patients with cN1 Disease?

Radiotherapy (RT) for high-risk localized prostate cancer (HRLPC) can be controversial in the context of increasing detection of suspicious lymph nodes via advanced imaging techniques. The EORTC 22683 trial initially established RT with androgen deprivation therapy (ADT) as the standard of care for HRLPC, but many patients remain uncured. GETUG-AFU-12 showed that addition of docetaxel and estramustine to ADT improved relapse-free survival but not overall survival.

Artificial intelligence in radiotherapy: Current applications and future trends.

Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but allowed for more precise and reproducible treatment. As a result, these advances required the accurate delineation of many more volumes, raising questions about how to delineate them, in a uniform manner across centers.

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

Background And Purpose: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers.

Materials And Methods: The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.

Background: We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC.

Method: We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans.

Salvage stereotactic reirradiation for intraprostatic cancer recurrence: A large retrospective study.

Introduction: Prostate cancer is the most common cancer in men. Thirty to forty-seven percent of patients treated with exclusive radiotherapy for prostate cancer will experience intraprostate recurrence. The use of radiotherapy in stereotactic conditions allows millimetric accuracy in irradiation to the target zone that minimizes the dose to organs at risk.

Ethical Aspects of Artificial Intelligence in Radiation Oncology.

Radiation oncology is a field that heavily relies on new technology. Data science and artificial intelligence will have an important role in the entire radiotherapy workflow. A new paradigm of routine healthcare data reuse to automate treatments and provide decision support is emerging.

Stereotactic Body Radiation Therapy for the Management of Hepatocellular Carcinoma: Efficacy and Safety.

This study aimed to describe patient characteristics, treatment efficacy, and safety in patients with hepatocellular carcinoma (HCC) undergoing stereotactic body radiation therapy (SBRT). We retrospectively analyzed data of 318 patients with 375 HCC treated between June 2007 and December 2018. Efficacy (overall survival [OS], relapse-free survival, and local control) and acute and late toxicities were described.

Bladder preservation in older adults with muscle-invasive bladder cancer: A retrospective study with concurrent chemotherapy and twice-daily hypofractionated radiotherapy schedule.

Introduction: Bladder cancer occurs mainly in older adults and surgery is not always possible when there are geriatric conditions and comorbidities. Trimodal treatment (TMT) combining trans-urethral resection of bladder tumour (TURBT) followed by concurrent chemoradiation (CRT) would be a curative alternative in such patients.

Materials And Methods: All consecutive patients 75 years of age and older with non-metastatic muscle-invasive bladder cancer (MIBC) treated with TMT by Georges Pompidou European Hospital team were retrospectively analysed.

Assessing the performances of a chatbot to collect real-life data of patients suffering from primary headache disorders.

Background: There are many scales for screening the impact of a disease. These scales are generally used to diagnose or assess the type and severity of a disease and are carried out by doctors. The chatbot helps patients suffering from primary headache disorders through personalized text messages.

Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality.

Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality.

Psychological distress during the COVID-19 pandemic in France: a national assessment of at-risk populations.

Background: Lockdowns were implemented to limit the spread of COVID-19. Peritraumatic distress (PD) and post-traumatic stress disorder have been reported after traumatic events, but the specific effect of the pandemic is not well known.

Aim: The aim of this study was to assess PD in France, a country where COVID-19 had such a dramatic impact that it required a country-wide lockdown.

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.

Purpose: Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care.