Background and purpose Evidence suggests that distinct biologic phenomenon produce different patterns of distant metastatic (DM) failures. We attempted to identify prognostically poor sites of first DM and to define factors predictive of their development. Methods and materials A total of 1074 patients treated with ≥60 Gy definitive radiation for initially non-metastatic non-small cell lung cancer (NSCLC) were analyzed. Uni- and multivariate Cox regression was utilized to associate clinical factors with DM site, and metastatic site with overall survival (OS). To account for competing events, multivariate Fine and Gray regression was utilized to identify treatment and disease factors predictive of site-specific metastases. Results Sites of first DM associated with worse survival were liver (median OS: 5 months after DM) and bone (median OS: 6.7 months after DM). Multivariate regression identified non-squamous histology to be associated with first DM within the liver (HR = 2.04, 95% CI 1.16-3.60, p = 0.01), while delay between diagnosis and RT (third vs. first tertile: HR = 2.3, 95% CI 1.26-4.21, p = 0.007) in addition to advanced stage (stage III vs. II/I: HR = 2.37, 95% CI 1.11-5.06, p = 0.03) were associated with first DM within bone. Conclusions Liver and bone as site of first DM is associated with worse prognosis and are predicted by different disease and treatment factors.
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http://dx.doi.org/10.3109/0284186X.2016.1154602 | DOI Listing |
Colorectal Dis
January 2025
Division of General Surgery, Department of Surgery, Queen's University, Kingston, Ontario, Canada.
Aim: Local excision (LE) for T1 rectal cancer may be recommended in those with low-risk disease, while resection is typically recommended in those with a high risk of luminal recurrence or lymph node metastasis. The aim of this work was to compare survival between resection and LE.
Method: This was a population-based retrospective cohort study set in the Canadian province of Ontario.
Cancers (Basel)
January 2025
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
: We constructed a prediction model to predict a 2-year locoregional recurrence based on the clinical features and radiomic features extracted from the machine learning method using computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal cancer. : A total of 264 patients (156 in Beijing, 87 in Tianjin, and 21 in Jiangsu) were included in this study. All those locally advanced esophageal cancer patients received definite radiotherapy and were randomly divided into five subgroups with a similar number and divided into training groups and validation groups by five cross-validations.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Department of Radiation Oncology, Istituto del Radio O. Alberti, Spedali Civili Hospital, Piazzale Spedali Civili 1, 25121 Brescia, Italy.
Methods And Materials: Patients with ongoing or planned anticancer treatment at 19 Italian Radiation Oncology centers were included in the study retrospectively from 3 February 2020 to 31 December 2020 and prospectively from 1 January 2021 to 31 May 2021. Anonymized data were processed through a specific website and database. Antineoplastic treatment characteristics and timing and outcomes of COVID-19 and its impact on radiotherapy or systemic therapy were described.
View Article and Find Full Text PDFUrol Oncol
January 2025
Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD. Electronic address:
Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death among men in the United States. The global burden of this disease is rising, placing significant strain on healthcare systems worldwide. Although definitive therapies like surgery and radiation are often effective, prostate cancer can recur and progress to castration-resistant prostate cancer in some cases.
View Article and Find Full Text PDFStrahlenther Onkol
January 2025
Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Background: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.
Methods: Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc.
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