Researchers at The University of Texas MD Anderson Cancer Center recently published a perspective article describing the Anderson Algorithm (AA), a framework for a personalized surgical approach to ovarian cancer, online at Nature Reviews Clinical Oncology. The article entitled “A framework for a personalized surgical approach to ovarian cancer” highlights the success that the AA has achieved in identifying patients who are more likely to respond favorably to surgical treatment, improving patient survival.
- Optimal resection: in surgical treatment for solid cancerous tumors it is the complete removal of all visible tumor.
- Laparoscopy: a procedure that allows a doctor to look directly at the contents of a patient’s abdomen or pelvis.
The AA stipulates the following:
- Diagnostic laparoscopy for all surgically fit patients with suspected
advanced-stage ovarian cancer.
- Two surgeons to independently score the disease for potential optimal resection.
- A third surgeon to score the disease if the first two disagree.
- Patients with scores under 8 are scheduled for surgery; those at 8 or above first proceed to three rounds of chemo with responders then going to surgery.
About the Anderson Algorithm:
In a recent press release about the article, Dr. Anil Sood, M.D., professor of Gynecologic Oncology and Reproductive Medicine and senior author of the paper, said “Our algorithm allows us to be much smarter about whom we operate on up front, providing a more individualized approach to surgery that’s led to better results for our patients.”
The AA was developed out of work done through MD Anderson’s Moon Shots Program. The program utilizes the expertise of over 175 faculty members including clinicians, surgeons, medical and radiation oncologists, pathologists and basic and translational researchers, all working to change the clinical landscape of ovarian cancer.
In explaining the importance of the Moon Shots Program, Dr. Sood, who is also the co-leader of the Breast and Ovarian Cancer Moon Shot, stated “Achieving the greatest clinical impact that we can with existing knowledge is an important aspect of MD Anderson’s Moon Shots Program. We worked hard to develop this algorithm, but all of it is based on existing knowledge.”
The article highlights previous research that provides clinical evidence for increased survival among patients with no residual disease after surgery. They note that it’s the strongest predictor for overall survival. This lends weight to the importance of the AA for it allows clinicians the ability to determine who should immediately undergo aggressive surgery and who should first receive other treatments, such as chemotherapy prior to surgery, once their tumors have shrunk. This is an important shift in the current standard of care that the article authors predict will greatly improve patient survival.
Ovarian cancer is a type of cancer that forms in the ovary (part of the female reproductive anatomy). It accounts for approximately 3% of cancers among women, but more deaths than any other cancer of the female reproductive system. According to the American Cancer Society estimates for 2015:
- About 21,290 women will receive a new diagnosis of ovarian cancer.
- About 14,180 women will die from ovarian cancer.
A woman’s risk of getting ovarian cancer during her lifetime is about 1 in 75. Her lifetime chance of dying from ovarian cancer is about 1 in 100. This cancer mainly develops in older women. About half of the women who are diagnosed with ovarian cancer are 63 years or older. It is more common in white women than African-American women.
Inspired by America’s drive a generation ago to put a man on the moon, The University of Texas MD Anderson Cancer Center has launched an ambitious and comprehensive action plan called the Moon Shots Program to make a giant leap for patients — to rapidly and dramatically reduce mortality and suffering in several major cancers.
The nation’s top-ranked hospital for cancer care, with its unparalleled resources and capabilities, is uniquely positioned to accelerate the end of cancer.