UK Study Claims AI Reading of CT Scans Almost Twice as Accurate at Grading Some Cancers as Clinical Laboratory Testing of Sarcoma Biopsies
Radiological method using AI algorithms to detect, locate, and identify cancer could negate the need for invasive, painful clinical laboratory testing of tissue biopsies
Clinical laboratory testing of cancer biopsies has been the standard in oncology diagnosis for decades. But a recent study by the Institute of Cancer Research (ICR) and the Royal Marsden NHS Foundation Trust in the UK has found that, for some types of sarcomas (malignant tumors), artificial intelligence (AI) can grade the aggressiveness of tumors nearly twice as accurately as lab tests, according to an ICR news release.
This will be of interest to histopathologists and radiologist technologists who are working to develop AI deep learning algorithms to read computed tomography scans (CT scans) to speed diagnosis and treatment of cancer patients.
“Researchers used the CT scans of 170 patients treated at The Royal Marsden with the two most common forms of retroperitoneal sarcoma (RPS)—leiomyosarcoma and liposarcoma—to create an AI algorithm, which was then tested on nearly 90 patients from centers across Europe and the US,” the news release notes.
The researchers then “used a technique called radiomics to analyze the CT scan data, which can extract information about the patient’s disease from medical images, including data which can’t be distinguished by the human eye,” the new release states.
The scientists published their findings in The Lancet Oncology titled, “A CT-based Radiomics Classification Model for the Prediction of Histological Type and Tumor Grade in Retroperitoneal Sarcoma (RADSARC-R): A Retrospective Multicohort Analysis.”
The research team sought to make improvements with this type of cancer because these tumors have “a poor prognosis, upfront characterization of the tumor is difficult, and under-grading is common,” they wrote. The fact that AI reading of CT scans is a non-invasive procedure is major benefit, they added.
“This is the largest and most robust study to date that has successfully developed and tested an AI model aimed at improving the diagnosis and grading of retroperitoneal sarcoma using data from CT scans,” said the study’s lead oncology radiologist Christina Messiou, MD, (above), Consultant Radiologist at The Royal Marsden NHS Foundation Trust and Professor in Imaging for Personalized Oncology at The Institute of Cancer Research, London, in a news release. Invasive medical laboratory testing of cancer biopsies may eventually become a thing of the past if this research becomes clinically available for oncology diagnosis. (Photo copyright: The Royal Marsden.)
Study Details
RPS is a relatively difficult cancer to spot, let alone diagnose. It is a rare form of soft-tissue cancer “with approximately 8,600 new cases diagnosed annually in the United States—less than 1% of all newly diagnosed malignancies,” according to Brigham and Women’s Hospital.
In their published study, the UK researchers noted that, “Although more than 50 soft tissue sarcoma radiomics studies have been completed, few include retroperitoneal sarcomas, and the majority use single-center datasets without independent validation. The limited interpretation of the quantitative radiological phenotype in retroperitoneal sarcomas and its association with tumor biology is a missed opportunity.”
According to the ICR news release, “The [AI] model accurately graded the risk—or how aggressive a tumor is likely to be—[in] 82% of the tumors analyzed, while only 44% were correctly graded using a biopsy.”
Additionally, “The [AI] model also accurately predicted the disease type [in] 84% of the sarcomas tested—meaning it can effectively differentiate between leiomyosarcoma and liposarcoma—compared with radiologists who were not able to diagnose 35% of the cases,” the news release states.
“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma, who currently have poor outcomes,” said the study’s first author Amani Arthur, PhD, Clinical Research Fellow at The Institute of Cancer Research, London, and Registrar at The Royal Marsden NHS Foundation Trust, in the ICR news release.
“The disease is very rare—clinicians may only see one or two cases in their career—which means diagnosis can be slow. This type of sarcoma is also difficult to treat as it can grow to large sizes and, due to the tumor’s location in the abdomen, involve complex surgery,” she continued. “Through this early research, we’ve developed an innovative AI tool using imaging data that could help us more accurately and quickly identify the type and grade of retroperitoneal sarcomas than current methods. This could improve patient outcomes by helping to speed up diagnosis of the disease, and better tailor treatment by reliably identifying the risk of each patient’s disease.
“In the next phase of the study, we will test this model in clinic on patients with potential retroperitoneal sarcomas to see if it can accurately characterize their disease and measure the performance of the technology over time,” Arthur added.
Importance of Study Findings
Speed of detection is key to successful cancer diagnoses, noted Richard Davidson, Chief Executive of Sarcoma UK, a bone and soft tissue cancer charity.
“People are more likely to survive sarcoma if their cancer is diagnosed early—when treatments can be effective and before the sarcoma has spread to other parts of the body. One in six people with sarcoma cancer wait more than a year to receive an accurate diagnosis, so any research that helps patients receive better treatment, care, information and support is welcome,” he told The Guardian.
According to the World Health Organization, cancer kills about 10 million people worldwide every year. Acquisition and medical laboratory testing of tissue biopsies is both painful to patients and time consuming. Thus, a non-invasive method of diagnosing deadly cancers quickly, accurately, and early would be a boon to oncology practices worldwide and could save thousands of lives each year.
—Kristin Althea O’Connor
Related Information:
AI Twice as Accurate as a Biopsy at Grading Aggressiveness of Some Sarcomas
AI Better than Biopsy at Assessing Some Cancers, Study Finds
AI Better than Biopsies for Grading Rare Cancer, New Research Suggests
