Spectrometric Diagnosis of Ovarian Cancer

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to make an automated —yet transparent— medical diagnosis of ovarian cancer, from a sample of serum analyzed by a mass spectrometer?

Goals & benefits

Identify the proteins involved in cancer, from the spectrum bands.

Enhance medical knowledge by helping gynecologists and oncologists understand the causal relationships between specific proteins, their combination, and the presence of cancer.

Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.

Contribute to improving patient care (pain, survival, duration of treatment) and extend access to high-level diagnoses even in medical deserts.


We get a Predictive Model that is:


A Decision System composed of 2 unchained gradual rules using only the 3 variables that XTRACTIS identified as significant (out of 15,154 Potential Predictors that are (mass/charge) ratios originating from the spectrum of each sample).


Perfect Real Performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 09/2022 (v1.3)

Results by
XTRACTIS® GENERATE 12.1.42004 (06/2022)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 2 cases
  5. Top-Models Benchmark