2024-01-29 ジョージア工科大学
◆研究者はこれが卵巣がんだけでなく他のがんの早期検出にも有望な方向性を示しており、技術を商業化するためのスタートアップを設立し、FDAの承認を目指す計画です。卵巣がんは通常症状がなく進行が進むと難治性になるため、正確で早期の診断が喫緊の課題とされています。
<関連情報>
- https://research.gatech.edu/researchers-leverage-ai-develop-early-diagnostic-test-ovarian-cancer
- https://www.sciencedirect.com/science/article/pii/S0090825823016360?via%3Dihub
卵巣癌診断への個人化された確率的アプローチ A personalized probabilistic approach to ovarian cancer diagnostics
Dongjo Ban, Stephen N. Housley, Lilya V. Matyunina, L. DeEtte McDonald, Victoria L. Bae-Jump, Benedict B. Benigno, Jeffrey Skolnick, John F. McDonald
Gynecologic Oncology Available online:23 January 2024
DOI:https://doi.org/10.1016/j.ygyno.2023.12.030
Highlights
•Predictive models derived from machine learning analyses of serum metabolic profiles can accurately detect ovarian cancer.
•Only a minority of the most predictively informative metabolites is currently annotated (7%).
•Lipids predominate among the most predictively informative metabolites currently annotated.
•The frequency distribution of model-derived patient scores were used to develop a clinical tool for the diagnosis of OC.
Abstract
Objective
The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer.
Methods
Serum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal.
Results
Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer.
Conclusions
An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.
