Advanced lung cancer is a deadly malignancy that is a common cause of death among Veterans. Significant advancements in lung cancer therapeutics have been made over the past decade and survival outcomes have improved. The Veteran population is older, has more medical comorbidities and frailty compared to the general population. These factors must be accounted for when evaluating patients for treatment and selecting treatment options. This article explores the impact of these important issues in the management of advanced lung cancer. Recent clinical trials leading to the approval of modern therapies will be outlined and treatment outcomes specific to older patients discussed. The impact of key comorbidities that are common in Veterans and their impact on lung cancer treatment will be reviewed. There is no gold standard frailty index for assessment of frailty in patients with advanced lung cancer and the ability to predict tolerability and benefit from systemic therapies. Currently available systemic therapies are associated with higher risk of adverse events and lower potential for clinically meaningful improvement in outcomes. Future research needs to focus on designing better frailty indices and developing novel therapies that are safer and more effective therapies for frail patients, who constitute a considerable proportion of individuals diagnosed with lung cancer.
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http://dx.doi.org/10.1053/j.seminoncol.2022.06.004 | DOI Listing |
Sci Rep
December 2024
Interventional Oncology, Johnson & Johnson Enterprise Innovation, Inc, 10th Floor 255 Main St, 02142, Cambridge, Boston, MA, USA.
The introduction of anti-PD-1/PD-L1 therapies revolutionized treatment for advanced non-small cell lung cancer (NSCLC), yet response rates remain modest, underscoring the need for predictive biomarkers. While a T cell inflamed gene expression profile (GEP) has predicted anti-PD-1 response in various cancers, it failed in a large NSCLC cohort from the Stand Up To Cancer-Mark (SU2C-MARK) Foundation. Re-analysis revealed that while the T cell inflamed GEP alone was not predictive, its performance improved significantly when combined with gene signatures of myeloid cell markers.
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December 2024
Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Background: Benzodiazepines are the third most misused medication, with many patients having their first exposure during a surgical episode. We sought to characterize factors associated with new persistent benzodiazepine use (NPBU) among patients undergoing cancer surgery.
Patients And Methods: Patients who underwent cancer surgery between 2013 and 2021 were identified using the IBM-MarketScan database.
Sci Rep
December 2024
Department of Radiology, Veterans Health Service Medical Center, Seoul, Republic of Korea.
This study aimed to compare computed tomography (CT) findings between basaloid lung squamous cell carcinoma (SCC) and non-basaloid SCC. From July 2003 to April 2021, 39 patients with surgically proven basaloid SCC were identified. For comparison, 161 patients with surgically proven non-basaloid SCC from June 2018 to January 2019 were selected consecutively.
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December 2024
Precision Medicine Center, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China.
Polyomavirus enhancer activator 3 (PEA3), an ETS transcription factor, has been documented to regulate the development and metastasis of human cancers. Nonetheless, a thorough analysis examining the relationship between the PEA3 subfamily members and tumour development, prognosis, and the tumour microenvironment (TME) across various cancer types has not yet been conducted. The expression profiles and prognostic significance of the PEA3 subfamily were evaluated using data from the GEO, TCGA, and PrognoScan databases, in conjunction with COX regression analyses and the Kaplan-Meier Plotter.
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December 2024
The Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays.
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