Computational Models for Diagnosing and Treating Endometriosis

Wangui Mbuguiro, Adriana Noemi Gonzalez, Feilim Mac Gabhann
Frontiers in reproductive health · 2021 · vol. 3 , pp. 699133 · doi:10.3389/frph.2021.699133 · PMID:36303959 · PMC9580807 · W4200382956
review OA: gold CC0 ⤵ 2 in-corpus citations
📄 Open PDF View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This review explores how regression, pharmacokinetic/pharmacodynamic, and quantitative systems pharmacology models have been used to improve endometriosis diagnosis and treatment, discussing their scope, data integration, and predictive capabilities.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This paper is a review describing how computational modeling methods are used to study, diagnose, and treat endometriosis, focusing on three approaches: regression/ML, pharmacokinetics/pharmacodynamics modeling, and quantitative systems pharmacology. It summarizes how these models use different scopes of variables and incorporate experimental and clinical data to generate diagnostic predictions or to model therapy effects, while comparing their benefits and limitations and discussing how models can be combined to better reflect system-wide immune, hormone, and vascular mechanisms. The review notes important caveats such as endometriosis staging not correlating well with symptoms/outcomes and the field’s current limitations in mechanistic modeling fidelity and generalizability. This paper is centrally about endometriosis — it reviews computational modeling approaches for endometriosis diagnosis and treatment, including mechanistic and data-driven frameworks.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.

My notes (saved in your browser only)

Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (61)

Cited by (2)

Source provenance

europepmc
last seen: 2026-06-13T06:22:48.782012+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-06-04T00:34:24.405165+00:00
License: CC0 · commercial use OK