AI Model Trained on Video Images Successfully Predicts Treatment Outcomes in Ovarian Cancer Patients

*** EMBARGOED UNTIL 10:00 AM MST 3/19/2022 ***

Newswise — PHOENIX (March 19, 2022) – Artificial intelligence (AI) can predict treatment outcomes in ovarian cancer at the time of pre-surgical assessment with a high degree of accuracy, according to results of a new pilot study. The findings were presented today in a focused plenary at the hybrid Society of Gynecologic Oncology 2022 Annual Meeting on Women’s Cancer, the premier scientific conference focused on the delivery of high-quality gynecologic cancer care.

The study trained an AI model to use still-frame images from pre-treatment laparoscopic surgical videos to predict outcomes in two predefined populations of patients with high-grade serous ovarian cancer (HGSOC): those with excellent response (ER) to standard treatment and those with poor response (PR) to standard therapy.

The model effectively predicted outcomes with an overall accuracy 93%. It successfully identified all patients with ER but misclassified about one-third of patients with PR as ER patients, possibly because of the smaller number images available for these patients in the study.

“This pilot study is an exciting frontier in surgical innovation that shows how we can use machine learning to enhance our clinical approach to treating patients with gynecologic cancers,” said Deanna Glassman, MD, The University of Texas MD Anderson Cancer Center, who co-led the study. “A major implication of our study is that the AI model could identify patients who are likely to have a poor response to traditional therapies, enabling clinicians to alter surgical plans and goals, and providing opportunities for tailoring therapeutic strategies in those patients.”

The study examined videos from 113 HGSOC patients, 75 (66%) of whom had a durable response to the therapy (ER). A total of 435 still-frame images from four anatomical locations – diaphragm, omentum, peritoneum and pelvis – were used to develop the AI model to detect distinct morphological patterns of disease in the patients, correlate those patterns with outcomes, and discriminate between the two patient populations (ER or PR). The images were divided into three sets: 70% for training, 10% for validation and 20% for testing.

“The concept of using an AI model trained on laparoscopic images requires additional validation studies, but in the future it could be extended to other gynecologic cancers to identify patterns of disease, predict treatment outcomes, and distinguish between viable and necrosed malignant tissue at the time of interval debulking surgery (IDS),” Glassman said.

About SGO

The Society of Gynecologic Oncology (SGO) is the premier medical specialty society for health care professionals trained in the comprehensive management of gynecologic cancers. As a 501(c)(6) organization, SGO contributes to the advancement of gyn cancer care by encouraging research, providing education, raising standards of practice, advocating for patients and members and collaborating with other domestic and international organizations. Learn more at www.sgo.org.