Will advances in AI lead to more effective screening practices for ovarian cancer?

Yes.

Screening techniques for ovarian cancer are nonexistent. Studies have looked at combinations of blood markers and ultrasounds, but they did not show a survival improvement. Ultimately, if a patient undergoes screening, it should lead to some improvement in outcome or prevention of the cancer altogether.

nakayama_john_80x106.jpg John M. Nakayama

AI can help us in this area because cancer results from a combination of genetic and environmental factors. We can try to account for more factors using AI, such as other genetic markers, epigenetic markers, changes in lipids, earlier CT or ultrasound findings that could be predictive. Putting those together would generate huge amounts of data. In fact, in a clinical commentary published in Gynecologic Oncology, McDonald and colleagues reported that the amount of genomic data alone doubles every 6 to 7 months and is predicted to exceed 40 exabytes a year within the next decade.

Given the overwhelming amount of complex data, the only way to get through it and make correlations among it all is with the help of machines. AI has generally worked in finding correlations among data.

Two studies explored this issue. One looked at the metabolome, meaning researchers used machine learning to look at different lab values and found they were able to predict ovarian cancer. The use of a support vector machine-based learning algorithm to identify 16 diagnostic metabolites detected early-stage ovarian cancer with 100% accuracy.

The other study screened for microRNAs and found just a handful of them were useful in predicting if a patient had ovarian cancer, borderline cancer or if it was benign. The researchers developed a micro-RNA algorithm for diagnosis that outperformed CA125 screening and appeared accurate regardless of patient age, histology or stage. The network also had 100% specificity for epithelial ovarian cancer when tested in a group of 454 patients with various diagnoses.

This work is preliminary and needs to be validated in a prospective manner, but these are the kinds of changes that have to happen in order for us to get effective screening for ovarian cancer. They have to be done algorithmically.

Deep learning can help with anything as long as it’s coded. The data are out there. It might be cost prohibitive to label it in some situations, but is it possible to find it and turn it into a screening method? The answer is yes.

perspectiveseparator.gif

References:

Gaul DA, et al. Sci Rep. 2015;doi:10.1038/srep16351.

McDonald JF. Gynecol Oncol. 2018;doi:10.1016/j.ygyno.2018.03.053.

Elias KM, et al. Elife. 2017;doi:10.7554/eLife.28932.

John M. Nakayama, MD, is an obstetrician-gynecologist at University Hospital Cleveland Medical Center. He can be reached at john.nakayama@uhhospitals.org. Disclosure: Nakayama reports no relevant financial disclosures.

Will advances in AI lead to more effective screening practices for ovarian cancer?

Will advances in AI lead to more effective screening practices for ovarian cancer?

Cancer-News

2 months
21 Views
Share
Want to watch this again later?
Sign in to add this video to a playlist. Login
0 0
Up Next Autoplay
Melanoma Rates Decline Found in Younger Population
Melanoma Rates Decline Found in Younger Population
Category: Acute Lymphoblastic Leukemia
1 Views
Cancer-News 3 days
Syndax Announces First Patient Dosed in Phase 1/2 AUGMENT-101 Trial of SNDX-5613 for the Treatment of Adults with Relapsed/Refractory Acute Leukemias
Syndax Announces First Patient Dosed in Phase 1/2 AUGMENT-101 Trial of SNDX-5613 for the Treatment of Adults with Relapsed/Refractory Acute Leukemias
Category: Acute Lymphoblastic Leukemia
6 Views
Cancer-News 1 week
What is PFS2 in Advanced NSCLC, and Should we Care_ Unique Analysis of the KEYNOTE-024 trial [720p] - OncologyTube
What is PFS2 in Advanced NSCLC, and Should we Care_ Unique Analysis of the KEYNOTE-024 trial [720p] - OncologyTube
Category: Acute Lymphoblastic Leukemia
36 Views
Stan 2 months
Patient Priorities Should be Paramount when Measuring Quality in Cancer Care According to Panelists at NCCN Policy Summit
Patient Priorities Should be Paramount when Measuring Quality in Cancer Care According to Panelists at NCCN Policy Summit
Category: Acute Lymphoblastic Leukemia
20 Views
Cancer-News 2 months
IASLC World Conference on Lung Cancer—Press Briefing Summary from Sunday, September 8th in Barcelona
IASLC World Conference on Lung Cancer—Press Briefing Summary from Sunday, September 8th in Barcelona
Category: Acute Lymphoblastic Leukemia
32 Views
Cancer-News 2 months
IPS based approach for treating virus-induced tumors
IPS based approach for treating virus-induced tumors
Category: Acute Lymphoblastic Leukemia
12 Views
Cancer-News 2 months
Pivotal New Data from Merck’s Broad Oncology Portfolio at ESMO 2019 Congress
Pivotal New Data from Merck’s Broad Oncology Portfolio at ESMO 2019 Congress
Category: Acute Lymphoblastic Leukemia
48 Views
Cancer-News 2 months
AMGEN ANNOUNCES NEW CLINICAL DATA EVALUATING NOVEL INVESTIGATIONAL KRASG12C INHIBITOR IN LARGER  PATIENT GROUP AT WCLC 2019
AMGEN ANNOUNCES NEW CLINICAL DATA EVALUATING NOVEL INVESTIGATIONAL KRASG12C INHIBITOR IN LARGER PATIENT GROUP AT WCLC 2019
Category: Acute Lymphoblastic Leukemia
28 Views
Cancer-News 2 months
Soricimed and Image Analysis Group Partner to Validate Predictive Imaging Markers for SOR-C13 Treatment Response in Solid Cancer Tumors
Soricimed and Image Analysis Group Partner to Validate Predictive Imaging Markers for SOR-C13 Treatment Response in Solid Cancer Tumors
Category: Acute Lymphoblastic Leukemia
15 Views
Cancer-News 2 months
Intraoperative Teamwork of Breast Cancer Surgeon and Pathologist Greatly Reduces Need for Second Surgery After Lumpectomy
Intraoperative Teamwork of Breast Cancer Surgeon and Pathologist Greatly Reduces Need for Second Surgery After Lumpectomy
Category: Acute Lymphoblastic Leukemia
23 Views
Cancer-News 2 months