How Could Machine Learning and Radiomics Improve Ovarian Cancer Care?


A score derived from CT imaging using a machine learning radiomics approach was able to reliably identify epithelial ovarian cancer patients with poor survival outcomes.

A score derived from CT imaging using a machine learning radiomics approach was able to reliably identify epithelial ovarian cancer patients with poor survival outcomes, according to a new study. The method could be used to guide a more personalized approach to therapy in this malignancy, and is potentially transferable to other cancers as well.

Radiomics involves quantifying tumor characteristics based on imaging and advanced bioinformatics tools. It could be useful in epithelial ovarian cancer, given the heterogeneity of response to therapy; also, recent research has used molecular subtyping to try and determine risk profiles for epithelial ovarian cancer. “It remains challenging, however, to translate these molecularly determined characteristics into clinically relevant biomarkers due to intratumor heterogeneity, additional high assay cost, and time delays,” wrote study authors led by Haonan Lu, of Imperial College London. “Therefore, a noninvasive, real-time, and cost-effective prognostic marker approach is warranted to reliably guide personalized treatment of epithelial ovarian cancer patients.”

The researchers developed a program that summarized 657 features from contrast-enhanced CT scans of 364 epithelial ovarian cancer patients at their initial presentation; these features related to the shape, size, intensity, texture, and wavelet decompositions of the scans. Using a machine learning approach, they derived a statistic they termed the Radiomic Prognostic Vector (RPV). The study was published in Nature Communications.

In a discovery dataset, RPV was continuously associated with overall survival, with a hazard ratio of 3.83 (95% CI, 2.27–6.46; P = 5.11 × 10-7). This was also true in two separate validation datasets, and RPV fared better than some existing prognostic markers-including C125 and a transcriptome-based molecular subtype-in predicting survival. Along with survival, a high RPV was also associated with primary chemotherapy resistance, shorter progression-free survival, and poor surgical outcomes.

“We demonstrate, based on the strong association between RPV and response to primary chemotherapy or surgery, that patients with high RPV have a significantly high risk of failing quality surgery or systemic strategies and suggest that they possibly need to be directed towards alternative therapeutic approaches including stroma modifying therapies,” the authors wrote. The tool, they added, “convincingly fulfills an unmet need” in the setting of epithelial ovarian cancer.

The field of machine learning–guided radiomics is rapidly expanding, in this and other malignancies. “Radiomics appears to offer a nearly limitless supply of imaging biomarkers that could potentially aid cancer detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status,” wrote several experts led by Robert J. Gillies, PhD, of the department of cancer imaging at the H. Lee Moffitt Cancer Center in Tampa, Florida, in 2015.

And since then, that potential has started to be realized further: at present, 38 trials involving radiomics are active or recruiting, according to These include trials in breast, lung, colorectal, esophageal, and many other malignancies. In one study using radiomics in lung cancer published in late 2018, authors led by Ahmed Hosny, of Dana-Farber Cancer Institute, wrote, “Deep learning algorithms that learn from experience offer access to unprecedented states of intelligence that, in some cases, match human intelligence. … This emerging approach allows for early diagnosis and customized patient-specific treatments, thus delivering the appropriate medical care to the right patient at the right time.”

Related Videos
Guidelines from the Society of Gynecologic Oncology may help with managing the ongoing chemotherapy shortage in the treatment of patients with gynecologic cancers, according to Brian Slomovitz, MD, MS, FACOG.
Interim data reveal favorable responses in patients with low-grade serous ovarian cancer treated with avutometinib plus defactinib, according to Susana N. Banerjee, MD.
Brian Slomovitz, MD, MS, FACOG, notes that sometimes there is a need to substitute cisplatin for carboplatin, and vice versa, to best manage gynecologic cancers during the chemotherapy shortage.
Findings from the phase 3 MIRASOL trial support mirvetuximab soravtansine as a standard treatment option for platinum-resistant ovarian cancer, according to Ritu Salani, MD.
Trastuzumab deruxtecan appears to elicit ‘impressive’ responses among patients with HER2-positive gynecologic cancers regardless of immunohistochemistry in the phase 2 DESTINY-PanTumor02 trial.
Ritu Salani, MD, highlights the possible benefit of a novel targeted therapy and autologous tumor vaccine in patients with platinum-resistant ovarian cancer, and in the maintenance setting after treatment for platinum-sensitive disease.
In addition to potentially moving mirvetuximab into earlier lines of treatment for those with platinum-sensitive ovarian cancer, Ritu Salani, MD, also discusses combining the agent with carboplatin to decrease toxicities and improve quality of life.
Treatment with mirvetuximab soravtansine appears to produce a 3-fold improvement in objective response rate vs chemotherapy among patients with folate receptor-α–expressing, platinum-resistant ovarian cancer in the phase 3 MIRASOL trial.
Related Content