Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments.
Francesca GrassiVincenza GranataRoberta FuscoFederica De MuzioCarmen CutoloMichela GabelloniAlessandra BorgheresiGinevra DantiCarmine PiconeAndrea GiovagnoniVittorio MieleNicoletta GandolfoAntonio BarileValerio NardoneRoberta GrassiPublished in: Journal of clinical medicine (2023)
The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
Keyphrases
- artificial intelligence
- early stage
- minimally invasive
- locally advanced
- machine learning
- emergency department
- high resolution
- prostate cancer
- radiation therapy
- rectal cancer
- intensive care unit
- squamous cell carcinoma
- coronary artery disease
- systemic sclerosis
- deep learning
- interstitial lung disease
- rheumatoid arthritis
- atrial fibrillation
- replacement therapy
- percutaneous coronary intervention
- idiopathic pulmonary fibrosis
- surgical site infection
- data analysis
- mechanical ventilation