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Background: Posttransplant lymphoproliferative disorder (PTLD), a term that encompasses a wide array of malignancies that occur after transplant, can be one of the most devastating transplant complications. While there have been major advancements in care, especially after the landmark PTLD-1 trial in 2012, there is a paucity of information on hospitalizations for PTLD and the changes in hospitalizations over time. Methods: This retrospective cohort study used the National Inpatient Sample to identify hospitalizations for PTLD that occurred between 2009 and 2018. We extracted data for hospitalizations with a primary or secondary diagnosis of PTLD and examined a range of variables, including age, gender, race, hospital type, hospital location, and disposition status. We also collected data on hospital region, median household income, insurance status, and bed size. Results: There was a statistically significant increase in the number of hospitalizations from 2009 to 2019 and an increasing rate of hospitalizations over the study period. Hypertension, electrolyte imbalances, renal failure, and anemia were among the most common comorbidities. We found an increased mortality rate, but this was not statistically significant. Conclusion: Our study provides insight into the changes in hospitalizations for PTLD over nearly a decade, showing an increase in hospitalizations and reports of comorbidities. [ABSTRACT FROM AUTHOR]

The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Rentiya et al.)