دورية أكاديمية

Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk.

التفاصيل البيبلوغرافية
العنوان: Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk.
المؤلفون: Pappas TC; Department of Research & Development, Aspira Women's Health, Austin, TX, United States of America., Roy Choudhury M; Department of Research & Development, Aspira Women's Health, Austin, TX, United States of America., Chacko BK; Aspira Labs, Aspira Women's Health, Austin, TX, United States of America., Twiggs LB; Division of Clinical Operations and Medical Affairs, Aspira Women's Health, Austin, TX, United States of America., Fritsche H; Aspira Labs, Aspira Women's Health, Austin, TX, United States of America., Elias KM; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, United States of America; Harvard Medical School, Boston, United States of America., Phan RT; Department of Research & Development, Aspira Women's Health, Austin, TX, United States of America; Aspira Labs, Aspira Women's Health, Austin, TX, United States of America; Division of Clinical Operations and Medical Affairs, Aspira Women's Health, Austin, TX, United States of America. Electronic address: rtphan@caa.columbia.edu.
المصدر: Gynecologic oncology [Gynecol Oncol] 2024 May 03; Vol. 187, pp. 21-29. Date of Electronic Publication: 2024 May 03.
Publication Model: Ahead of Print
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Academic Press Country of Publication: United States NLM ID: 0365304 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-6859 (Electronic) Linking ISSN: 00908258 NLM ISO Abbreviation: Gynecol Oncol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York, Academic Press.
مستخلص: Objective: We recently characterized the clinical performance of a multivariate index assay (MIA3G) to assess ovarian cancer risk for adnexal masses at initial presentation. This study evaluated how MIA3G varies when applied longitudinally to monitor risk during clinical follow-up.
Method: The study evaluated women presenting with adnexal masses from eleven centers across the US. Patients received an initial blood draw at enrollment and at the standard-of-care follow-up visits. MIA3G was determined for all visits but physicians did not have access to MIA3G scores to determine clinical management. The primary outcome was the relative change value (RCV) of MIA3G over the period of clinical observation.
Results: A total of 510 patients of 785 enrolled met study criteria. Of these, 30.8% had a second, 25.4% a third and 22.2% a fourth blood draw following initial collection. The median duration from initial draw was 131 d to second draw, 301.5 d to the third draw and 365.5 d to the fourth draw. MIA3G RCV of >50% was observed in 22-26% patients, whereas 70-75% patients had MIA3G RCV >5%. An empirical baseline RCV of 56% - transformed to 1 in logarithmic scale - was calculated from averaging RCVs of all patients who had no malignancy risk after 210 days. RCV > 1 log was associated with higher incidence of surgical intervention (29.6%) compared to RCV < 1 log (16.9%).
Conclusions: Variation in MI3AG does not change the accuracy of the test for excluding malignancy, while marked changes may be associated with a slightly higher likelihood of surgical intervention. In addition to MIA3G score itself, the MIA3G RCV may be important for clinical management.
Competing Interests: Declaration of competing interest None.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
فهرسة مساهمة: Keywords: Adnexal mass; Benign; Machine learning; Malignancy; Serum biomarker; Surgery
تواريخ الأحداث: Date Created: 20240504 Latest Revision: 20240504
رمز التحديث: 20240505
DOI: 10.1016/j.ygyno.2024.04.020
PMID: 38703674
قاعدة البيانات: MEDLINE
الوصف
تدمد:1095-6859
DOI:10.1016/j.ygyno.2024.04.020