Objective: We retrospectively analyzed the clinical prognostic value of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for luminal A breast cancer.
Methods: Using both the anatomic and prognostic staging in the 8th edition of AJCC cancer staging system, we restaged patients with luminal A breast cancer treated at the Breast Disease Center, Peking University First Hospital from 2008 to 2014. Follow-up data including 5-year disease free survival (DFS), overall survival (OS) and other clinic-pathological data were collected to analyze the differences between the two staging subgroups.
Results: This study included 421 patients with luminal A breast cancer (median follow-up, 61 months). The 5-year DFS and OS rates were 98.3% and 99.3%, respectively. Significant differences in 5-year DFS but not OS were observed between different anatomic disease stages. Significant differences were observed in both 5-year DFS and OS between different prognostic stages. Application of the prognostic staging system resulted in assignment of 175 of 421 patients (41.6%) to a different group compared to their original anatomic stages. In total, 102 of 103 patients with anatomic stage IIA changed to prognostic stage IB, and 24 of 52 patients with anatomic stage IIB changed to prognostic stage IB, while 1 changed to prognostic stage IIIB. Twenty-two of 33 patients with anatomic stage IIIA were down-staged to IIA when staged by prognostic staging system, and the other 11 patients were down-staged to IIB. Two patients with anatomic stage IIIB were down-staged to IIIA. Among seven patients with anatomic stage IIIC cancer, two were down-staged to IIIA and four were down-staged to stage IIIB.
Conclusions: The 8th edition of AJCC prognostic staging system is an important supplement to the breast cancer staging system. More clinical trials are needed to prove its ability to guide selection of proper systemic therapy and predict prognosis of breast cancer.
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http://dx.doi.org/10.21147/j.issn.1000-9604.2017.04.08 | DOI Listing |
Med Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
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School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences and Research University, New Delhi, India, 110017.
The biopharmaceutical industry has witnessed significant growth in the development and approval of biosimilars. These biosimilars aim to provide cost-effective alternatives to expensive originator biosimilars, alleviating financial pressures within healthcare. The manufacturing of biosimilars is a highly complex process that involves several stages, each of which must meet strict regulatory standards to ensure that the final product is highly similar to the reference biologic.
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Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone).
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January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
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January 2025
Business School, Hebei University of Economics and Business, Shijiazhuang, 050062, China.
The development and implementation of county carbon control action plans in the Yellow River Basin (YRB) are crucial for realizing the "dual carbon" goals and modernizing national governance. Utilizing remote sensing data from 2001 to 2020, this study constructs a light-carbon conversion model and a carbon footprint model to simulate the carbon footprint of county energy consumption in the YRB. Employing spatial autocorrelation and spatial Durbin models, the study examines the temporal-spatial evolution characteristics and spatial effect mechanism.
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