Using AI to Predict Outcomes in High-grade Extrauterine Serous Carcinoma

Artificial intelligence (AI) was used to predict response of high-grade extrauterine serous carcinoma (HGSC) to adjuvant platinum chemotherapy.

For the first time, researchers used artificial intelligence (AI) to predict response of high-grade extrauterine serous carcinoma (HGSC), a highly aggressive cancer with frequent chemotherapy resistance and a poor survival rate, to adjuvant platinum chemotherapy.

As explained in Scientific Reports, there are currently no validated tissue-based prognostic or predictive markers for primary platinum-based treatment in use for HGSC, a type of ovarian cancer; about 75% of ovarian cancer deaths are from HGSC. Typically presenting at stage III or stage IV, the 5-year survival rate for HGSC is less than 50% and about 25% in advanced disease.

These tumors are marked by genetic and morphologic heterogeneity and outcomes are uncertain, even when patients appear to have favorable characteristics, such as younger age and lower stage. About 15% of patients can survive for a decade or more. Standard treatment (debulking surgery and platinum-based chemotherapy) hasn’t changed for this disease in decades, and despite the arrival of poly ADP ribose polymerase inhibitors, treatment response is still unpredictable.

Patients with a progression-free interval (PFI) of less than 6 months are considered platinum resistant, while those with a PFI beyond 12 months are considered platinum sensitive; only 25% of women have a PFI of greater than 18 months.

The researchers, from Finland, said they believed the differences that exist in HGSC tumors—which ones are refractory to therapy immediately and which ones have later resistance—could be detectable using AI. The study aimed to see if a weakly supervised convolutional neural network trained on whole slide images (WSI) can accurately classify HGSC into outcome groups using the tumor morphology on the images. WSI refer to conventional slides that are scanned to create digital files.

The HGSC patient cohort (the test set) included 30 women with similar presentations who experienced very different treatment responses (PFIs of either ≤ 6 months or ≥ 18 months and 205 slide images).

In 3 steps, the neural network was trained to identify morphologic regions (digital biomarkers) that are highly associated with one or the other treatment response group. The classifier was tested using a separate 22-slide test set, and 18 of 22 slides were correctly classified.

The method was able to discern extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%).

The authors said the proof-of-concept results are novel since it is the first time prospective prognostic information is identified specifically within HGSC tumor morphology. The finding is important since unlike other tumor types, “pathologist evaluation of HGSC tumor tissue provides almost no predictive or prognostic information beyond the diagnosis itself,” the authors said.


Laury AR, Blom S, Ropponen T, Virtanen A, Carpén OM. Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone. Sci Rep. Published online September 27, 2021. doi:10.1038/s41598-021-98480-0

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