Amivantamab as a salvage therapy post-EGFR-tyrosine kinase inhibitor failure in patients with mutated EGFR non-small cell lung cancer.

Background: Amivantamab is an approved dual epidermal growth factor receptor (EGFR) and mesenchymal-epithelial transition (MET) inhibitor for the treatment of EGFR exon 20 insertion (EGFRex20ins) mutations. Recent data support the use of amivantamab for both common and uncommon EGFR mutations after previous therapies. In this study, we investigated the role of adding amivantamab to the ongoing EGFR-tyrosine kinase inhibitor (TKI) in later lines of therapy upon progression.

Anaemia of Inflammation Preceding Dyspnoea, Dry Cough and Weight Loss in Primary Pulmonary Lymphoma.

Introduction: There is little information in the literature on the early, sub-clinical stage and laboratory test results in patients with primary mucosa-associated lymphoid tissue (MALT) lymphoma of the lung, a rare disease.

Case Description: In a 75-year-old man, an open lung biopsy-confirmed diagnosis of primary pulmonary lymphoma was preceded by almost six months of anaemia of inflammatory disease and monocytosis without any pulmonary symptoms. When he developed a dry cough, increasing dyspnoea and marked weight loss, these changes deepened and became associated with reactive thrombocytosis; markedly increased ferritin and C-reactive protein (positive acute-phase reactants), as well as reduced albumin and transferrin (negative acute-phase reactants).

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations.