Development of a fertility risk calculator to predict individualized chance of ovarian failure after chemotherapy.

Journal Article (Journal Article)

PURPOSE: To develop an innovative machine learning (ML) model that predicts personalized risk of primary ovarian insufficiency (POI) after chemotherapy for reproductive-aged women. Currently, individualized prediction of a patient's risk of POI is challenging. METHODS: Authors of published studies examining POI after gonadotoxic therapy were contacted, and six authors shared their de-identified data (N = 435). A composite outcome for POI was determined for each patient and validated by 3 authors. The primary dataset was partitioned into training and test sets; random forest binary classifiers were trained, and mean prediction scores were computed. Institutional data collected from a cross-sectional survey of cancer survivors (N = 117) was used as another independent validation set. RESULTS: Our model predicted individualized risk of POI with an accuracy of 88% (area under the ROC 0.87, 95% CI: 0.77-0.96; p < 0.001). Mean prediction scores for patients who developed POI and who did not were 0.60 and 0.38 (t-test p < 0.001), respectively. Highly weighted variables included age, chemotherapy dose, prior treatment, smoking, and baseline diminished ovarian reserve. CONCLUSION: We developed an ML-based model to estimate personalized risk of POI after chemotherapy. Our web-based calculator will be a user-friendly decision aid for individualizing risk prediction in oncofertility consultations.

Full Text

Duke Authors

Cited Authors

  • Chung, EH; Acharya, CR; Harris, BS; Acharya, KS

Published Date

  • November 2021

Published In

Volume / Issue

  • 38 / 11

Start / End Page

  • 3047 - 3055

PubMed ID

  • 34495476

Pubmed Central ID

  • PMC8609057

Electronic International Standard Serial Number (EISSN)

  • 1573-7330

Digital Object Identifier (DOI)

  • 10.1007/s10815-021-02311-0

Language

  • eng

Conference Location

  • Netherlands