Cargando…

Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass

BACKGROUND: Conservative management of adnexal mass is warranted when there is imaging-based and clinical evidence of benign characteristics. Malignancy risk is, however, a concern due to the mortality rate of ovarian cancer. Malignancy occurs in 10–15% of adnexal masses that go to surgery, whereas...

Descripción completa

Detalles Bibliográficos
Autores principales: Reilly, Gerard P., Dunton, Charles J., Bullock, Rowan G., Ure, Daniel R., Fritsche, Herbert, Ghosh, Srinka, Pappas, Todd C., Phan, Ryan T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900123/
https://www.ncbi.nlm.nih.gov/pubmed/36756174
http://dx.doi.org/10.3389/fmed.2023.1102437
_version_ 1784882778204012544
author Reilly, Gerard P.
Dunton, Charles J.
Bullock, Rowan G.
Ure, Daniel R.
Fritsche, Herbert
Ghosh, Srinka
Pappas, Todd C.
Phan, Ryan T.
author_facet Reilly, Gerard P.
Dunton, Charles J.
Bullock, Rowan G.
Ure, Daniel R.
Fritsche, Herbert
Ghosh, Srinka
Pappas, Todd C.
Phan, Ryan T.
author_sort Reilly, Gerard P.
collection PubMed
description BACKGROUND: Conservative management of adnexal mass is warranted when there is imaging-based and clinical evidence of benign characteristics. Malignancy risk is, however, a concern due to the mortality rate of ovarian cancer. Malignancy occurs in 10–15% of adnexal masses that go to surgery, whereas the rate of malignancy is much lower in masses clinically characterized as benign or indeterminate. Additional diagnostic tests could assist conservative management of these patients. Here we report the clinical validation of OvaWatch, a multivariate index assay, with real-world evidence of performance that supports conservative management of adnexal masses. METHODS: OvaWatch utilizes a previously characterized neural network-based algorithm combining serum biomarkers and clinical covariates and was used to examine malignancy risk in prospective and retrospective samples of patients with an adnexal mass. Retrospective data sets were assembled from previous studies using patients who had adnexal mass and were scheduled for surgery. The prospective study was a multi-center trial of women with adnexal mass as identified on clinical examination and indeterminate or asymptomatic by imaging. The performance to detect ovarian malignancy was evaluated at a previously validated score threshold. RESULTS: In retrospective, low prevalence (N = 1,453, 1.5% malignancy rate) data from patients that received an independent physician assessment of benign, OvaWatch has a sensitivity of 81.8% [95% confidence interval (CI) 65.1–92.7] for identifying a histologically confirmed malignancy, and a negative predictive value (NPV) of 99.7%. OvaWatch identified 18/22 malignancies missed by physician assessment. A prospective data set had 501 patients where 106 patients with adnexal mass went for surgery. The prevalence was 2% (10 malignancies). The sensitivity of OvaWatch for malignancy was 40% (95% CI: 16.8–68.7%), and the specificity was 87% (95% CI: 83.7–89.7) when patients were included in the analysis who did not go to surgery and were evaluated as benign. The NPV remained 98.6% (95% CI: 97.0–99.4%). An independent analysis set with a high prevalence (45.8%) the NPV value was 87.8% (95% CI: 95% CI: 75.8–94.3%). CONCLUSION: OvaWatch demonstrated high NPV across diverse data sets and promises utility as an effective diagnostic test supporting management of suspected benign or indeterminate mass to safely decrease or delay unnecessary surgeries.
format Online
Article
Text
id pubmed-9900123
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99001232023-02-07 Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass Reilly, Gerard P. Dunton, Charles J. Bullock, Rowan G. Ure, Daniel R. Fritsche, Herbert Ghosh, Srinka Pappas, Todd C. Phan, Ryan T. Front Med (Lausanne) Medicine BACKGROUND: Conservative management of adnexal mass is warranted when there is imaging-based and clinical evidence of benign characteristics. Malignancy risk is, however, a concern due to the mortality rate of ovarian cancer. Malignancy occurs in 10–15% of adnexal masses that go to surgery, whereas the rate of malignancy is much lower in masses clinically characterized as benign or indeterminate. Additional diagnostic tests could assist conservative management of these patients. Here we report the clinical validation of OvaWatch, a multivariate index assay, with real-world evidence of performance that supports conservative management of adnexal masses. METHODS: OvaWatch utilizes a previously characterized neural network-based algorithm combining serum biomarkers and clinical covariates and was used to examine malignancy risk in prospective and retrospective samples of patients with an adnexal mass. Retrospective data sets were assembled from previous studies using patients who had adnexal mass and were scheduled for surgery. The prospective study was a multi-center trial of women with adnexal mass as identified on clinical examination and indeterminate or asymptomatic by imaging. The performance to detect ovarian malignancy was evaluated at a previously validated score threshold. RESULTS: In retrospective, low prevalence (N = 1,453, 1.5% malignancy rate) data from patients that received an independent physician assessment of benign, OvaWatch has a sensitivity of 81.8% [95% confidence interval (CI) 65.1–92.7] for identifying a histologically confirmed malignancy, and a negative predictive value (NPV) of 99.7%. OvaWatch identified 18/22 malignancies missed by physician assessment. A prospective data set had 501 patients where 106 patients with adnexal mass went for surgery. The prevalence was 2% (10 malignancies). The sensitivity of OvaWatch for malignancy was 40% (95% CI: 16.8–68.7%), and the specificity was 87% (95% CI: 83.7–89.7) when patients were included in the analysis who did not go to surgery and were evaluated as benign. The NPV remained 98.6% (95% CI: 97.0–99.4%). An independent analysis set with a high prevalence (45.8%) the NPV value was 87.8% (95% CI: 95% CI: 75.8–94.3%). CONCLUSION: OvaWatch demonstrated high NPV across diverse data sets and promises utility as an effective diagnostic test supporting management of suspected benign or indeterminate mass to safely decrease or delay unnecessary surgeries. Frontiers Media S.A. 2023-01-23 /pmc/articles/PMC9900123/ /pubmed/36756174 http://dx.doi.org/10.3389/fmed.2023.1102437 Text en Copyright © 2023 Reilly, Dunton, Bullock, Ure, Fritsche, Ghosh, Pappas and Phan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Reilly, Gerard P.
Dunton, Charles J.
Bullock, Rowan G.
Ure, Daniel R.
Fritsche, Herbert
Ghosh, Srinka
Pappas, Todd C.
Phan, Ryan T.
Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title_full Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title_fullStr Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title_full_unstemmed Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title_short Validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
title_sort validation of a deep neural network-based algorithm supporting clinical management of adnexal mass
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900123/
https://www.ncbi.nlm.nih.gov/pubmed/36756174
http://dx.doi.org/10.3389/fmed.2023.1102437
work_keys_str_mv AT reillygerardp validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT duntoncharlesj validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT bullockrowang validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT uredanielr validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT fritscheherbert validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT ghoshsrinka validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT pappastoddc validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass
AT phanryant validationofadeepneuralnetworkbasedalgorithmsupportingclinicalmanagementofadnexalmass