Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.

Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings. Keywords: MRI, Genital/Reproductive, Prostate Segmentation, Deep Learning Systematic review registration link: osf.io/nxaev © RSNA, 2024.

Demonstrating the Value of Interventional Radiology.

While national healthcare expenditures per capita in the United States exceed those in all other Organisation for Economic Co-operation and Development (OECD) countries, measures of health outcomes in the United States lag behind those in peer nations. This combination of high healthcare spending and relatively poor health has led to attempts to identify high- and low-value healthcare services and to develop mechanisms to reimburse health care providers based on the value of the care delivered. This article investigates the meaning of value in healthcare and identifies specific services delivered by interventional radiologists that have accrued evidence that they meet criteria for high-value services. Recognizing the shift in reimbursement to high-value care, it is imperative that interventional radiology (IR) develop the evidence needed to articulate to all relevant stakeholders how IR contributes value to the system.

Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants.

MOTIVATION: Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. RESULTS: We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets. AVAILABILITY AND IMPLEMENTATION: Code is available at http://github.com/bagherilab/networkinference/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative analysis of cancer pathway progression and coherence.

We analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The database provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, have not been analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into discrete X and Y scales and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distribution of mutation targets. The X scale corresponds to the downstream location in a pathway, whereas the Y scale corresponds to the stage of the tumor. The gene expression values of the early stage pathway members are significantly higher than of the rest of the pathway members in colorectal adenoma tissues. The colorectal cancer pathway shows some degree of coherence in the carcinoma samples. The correlated gene pairs responsible for the coherence of the colorectal cancer pathway in the carcinoma samples are supported, in part, by the literature and may suggest novel regulatory associations. Finally, there are more mutation targets in the nucleus as well as the late tumor stages of the KEGG colorectal cancer pathway.