Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is a quasi-experimental design that can be used to estimate the causal effect of treatments that are assigned based on a running variable crossing a threshold. Such threshold-based rules are ubiquitous in healthcare, where predictive and prognostic biomarkers frequently guide treatment decisions. However, standard RD estimators rely on complete outcome data, an assumption often violated in time-to-event analyses where censoring arises from loss to follow-up. To address this issue, we propose a nonparametric approach that leverages doubly robust censoring corrections and can be paired with existing RD estimators. Our approach can handle multiple survival endpoints, long follow-up times, and covariate-dependent variation in survival and censoring. We discuss the relevance of our approach across multiple areas of applications and demonstrate its usefulness through simulations and the prostate component of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial where our new approach offers several advantages, including higher efficiency and robustness to misspecification. We have also developed an open-source software package, $\texttt{rdsurvival}$, for the $\texttt{R}$ language.

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving de-tection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol out-lines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Notes: PRISMA figure adapted from Liberati A, Altman D, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration.

The FDA has authorized the marketing of Paige Prostate, an AI-based software platform to help pathologists identify prostate cancer when they review slide images from prostate biopsies.1 The standard biopsy review process involves the pathologist examining digitally scanned slide images from prostate biopsies to find areas that are suspicious for cancer. Paige Prostate provides a supplementary assessment of the image and locates the area with the highest probability of harboring cancer. The pathologist can then examine this specific area further if they did not identify it on their initial assessment. "Pathologists examine biopsies of tissue suspected for diseases, such as prostate cancer, every day. Identifying areas of concern on the biopsy image can help pathologists make a diagnosis that informs the appropriate treatment," Tim Stenzel, MD, PhD, director of the Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health, stated in a press release.

Researchers from Google Health found that using artificial intelligence (AI) to aid in the review of prostate biopsies improved the quality, efficiency, and consistency of cancer detection and grading. In a prostate biopsy, tissue is removed and assessed for cell abnormalities that may be linked to prostate cancer. The standard grading system for this procedure is the Gleason grade (GG) system, involving classification into 1 of 5 prognostic groups. Expert-level AI algorithms for prostate biopsy grading, like this one from Gooogle Health, have recently been developed to combat interpathologist variability associated with grading. In this diagnostic study, retrospective grading of prostate core needle biopsies was conducted at two medical laboratories in the US between October 2019 and January 2020.

A deep-learning system developed at Google outperformed general pathologists for Gleason grading of prostate cancer biopsies, company researchers reported.

Leader in AI-powered cancer diagnostics, Ibex Medical Analytics and provider of digital pathology services in the NHS, LDPath, have announced the UK's first rollout of clinical grade AI application for cancer detection in pathology. This platform will support pathologists in enhancing diagnostic accuracy and efficiency. Over the years, a global increase in cancer cases has coincided with a decline in the number of pathologists around the world. Traditional pathology involves manual processes that have remained the same for years. These processes involve slides to be analysed by pathologists using microscopes, and reporting is often carried out on pieces of paper.

Basics: What is a Gleason Score? One important component of staging your cancer is the grade of the cancer. While the stage of your cancer looks at where the cancer is present in your body -- how it is behaving at the macro level -- the grade describes what the actual cancer cells look like under a microscope -- how they are behaving on a micro level. Traditionally, prostate cancer grades were described according to the Gleason Score, a system named for the pathologist who developed it in the 1960s. Dr. Donald Gleason realized that cancerous cells fall into 5 distinct patterns as they change from normal cells to tumor cells.