Background: Fatigue is the most common symptom among cancer survivors. Cancer-related fatigue (CRF) may occur at any point in the cancer care continuum. Multiple factors contribute to CRF development and severity, including cancer type, treatments, presence of other symptoms, comorbidities, and medication side effects. Clinically, increasing physical activity, enhancing sleep quality, and recognizing sleep disorders are integral to managing CRF. Unfortunately, CRF is infrequently recognized, evaluated, or treated in lung cancer survivors despite more frequent and severe symptoms than in other cancers. Therefore, increased awareness and understanding of CRF are needed to improve health-related quality of life in lung cancer survivors. Objectives: 1) To identify and prioritize knowledge and research gaps and 2) to develop and prioritize research questions to evaluate mechanistic, diagnostic, and therapeutic approaches to CRF among lung cancer survivors. Methods: We convened a multidisciplinary panel to review the available literature on CRF, focusing on the impacts of physical activity, rehabilitation, and sleep disturbances in lung cancer. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in the 1) detection and diagnostic evaluation of CRF in lung cancer survivors; 2) timing, goals, and implementation of physical activity and rehabilitation; and 3) evaluation and treatment of sleep disturbances and disorders to reduce CRF. Finally, we present the panel’s initial 32 research questions and seven final prioritized questions. Conclusions: This statement offers a prioritized research agenda to 1) advance clinical and research efforts and 2) increase awareness of CRF in lung cancer survivors.

Background: Fatigue and sleep disorders are prevalent in cancer patients. Obstructive sleep apnea (OSA) commonly causes excessive daytime sleepiness (EDS) and fatigue. We hypothesize that treating cancer patients with OSA using positive airway pressure (PAP) will improve EDS and fatigue. Methods: A retrospective chart review of sleep clinic visits of cancer patients with newly diagnosed OSA was performed. Epworth Sleepiness Scale (ESS) and fatigue reported at baseline and within 6 months of starting PAP therapy were compared between PAP‐adherent and PAP‐non‐adherent patients. Results: 65 cancer patients with OSA and ESS >10 were recommended PAP therapy, including 45 patients with fatigue. 29 patients pursued PAP therapy with 79% ( n = 23) adherent at follow‐up. The median baseline apnea hypopnea index (AHI) for OSA was 24.0 (interquartile range [IQR] 14.3, 32.3) and 23.8 (IQR 10.1, 42.8) events/hour among PAP‐adherent and PAP‐non‐adherent patients, respectively ( p = 0.90). Median baseline ESS was 14.0 (IQR 12.0, 17.0) among adherent and 17.0 (IQR 11.0, 17.3) among non‐adherent patients ( p = 0.73). The median ESS at follow‐up of the adherent and non‐adherent groups was 8.0 (IQR 6.0, 10.0) and 11.0 (IQR 8.0, 15.8), respectively ( p = 0.08). Median ESS change was −5.0 (IQR −7.0, −4.0) in PAP adherent patients and −2.5 (IQR −5.25, −1.50) in PAP‐non‐adherent patients ( p = 0.07). When the groups are examined separately, the median change in the PAP‐adherent group was highly significant ( p = 0.001), while the ESS median change in the PAP‐non‐adherent group was considerably less ( p = 0.04). 17 out of the 21 PAP‐adherent patients reporting fatigue at baseline indicated improvement at follow‐up. Conclusions: PAP therapy for OSA in cancer patients improves EDS and fatigue. Larger studies are necessary to evaluate the efficacy of PAP in improving fatigue in this population.

Background: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk.Study Design and Methods: This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT.Results: From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set.Conclusions: The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.

Background and objectives: Therapeutic agents for cancer can cause unique pulmonary toxicities and mimic other conditions. The advent of new targeted molecular and immune therapies has changed the landscape of cancer treatment. These adverse events pose diagnostic and therapeutic challenges. This review aims to summarize the clinical presentations, radiographic patterns, and management strategies for noninfectious pulmonary complications associated with cancer therapies. Materials and methods: A literature review was conducted focusing on drug-induced lung injury (DILI), radiation-induced lung injury (RILI), pleural disease, pulmonary vascular complications, and other inflammatory conditions in patients with cancer. The data sources included clinical trials, guideline recommendations, observational studies, and expert consensus addressing incidence, pathophysiology, imaging findings, and treatment approaches. Results: Noninfectious pulmonary sequelae of anti-neoplastic therapies encompass a broad spectrum of etiologies. DILI occurs in up to 30% with variable onset and severity. The patterns can be diverse but include interstitial pneumonitis, organizing pneumonia, and diffuse alveolar damage. RILI is common and influenced by the radiation dose, volume, and concurrent therapies, and it may have both acute and chronic clinical and radiographic presentations. Pleural disease may arise from radiation and other agents, and the determination of etiology can impact management. Pulmonary vascular disease arises from many different etiologies, including therapies such as tyrosine kinase inhibitors and proteosome inhibitors, thromboembolic disease, as well as rare processes, including pulmonary veno-occlusive disease. Other conditions such as transfusion-related lung injury, cryptogenic organizing pneumonia, and interstitial lung abnormalities can also further complicate the diagnosis. Conclusions: Noninfectious pulmonary complications related to cancer therapies are diverse and often indistinguishable from infectious or malignant processes. The integration of clinical history, imaging, and selective invasive testing are needed for a timely diagnosis. Management typically involves withdrawal of the offending agent and corticosteroids, with immunosuppressive therapy reserved for severe or refractory cases. The awareness of these entities and early recognition are critical to optimizing outcomes.