# Mach-LETSGO: Machine-LEarning of Treatment, Survey, and Genetics towards Obtaining Correct Classification of Chronic Conditions in Adult Survivors in the Childhood Cancer Survivor Study - CCSS Suppl

> **NIH CA U24** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2026 · $325,043

## Abstract

ABSTRACT
Mach-LETSGO leverages machine learning to improve the accuracy of chronic health condition classification in
the Childhood Cancer Survivor Study (CCSS; U24 CA055727; PI: Armstrong), addressing limitations of its self-
reported data. By leveraging reference-standard clinical assessments performed in the St. Jude Lifetime Cohort
(SJLIFE; U01 CA195547; CA301480; MPI: Hudson/Armstrong) among 2,436 survivors who have participated in
both CCSS and SJLIFE, in addition to germline whole genome sequencing, and detailed childhood cancer
treatment data, this proposal aims to refine chronic health condition classification for chronic health conditions
such as diabetes, hypertension, and cardiomyopathy. Machine learning methods will identify patterns in
misclassification, leveraging predictors such as treatment exposures, genetic risk scores, demographic factors,
and complex dependencies among survey responses. With training data from 2,000 survivors participating in
both CCSS and SJLIFE, along with 25,735 CCSS participants, the study will develop robust predictive models
of CCSS participants’ chronic health condition classifications, evaluated in the training dataset through advanced
cross-validation techniques along with regularization, ensemble methods, and interpretability tools (SHAP, LIME)
to ensure avoidance of overfitting, followed by an independent validation in the remaining 436 survivors
participating in both CCSS and SJLIFE. This transformative approach will enhance the accuracy of chronic health
condition outcomes in the 25,735 CCSS survivors, strengthen epidemiological analyses, and ensure the
continued global impact of CCSS, the largest resource for survivorship research. Findings from this pilot will
provide methodological insights to inform future CCSS analyses.

## Key facts

- **NIH application ID:** 11471611
- **Project number:** 3U24CA055727-32S1
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** Gregory  Armstrong
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** CA
- **Fiscal year:** 2026
- **Award amount:** $325,043
- **Award type:** 3
- **Project period:** 1993-07-20T00:00:00 → 2026-11-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11471611

## Citation

> US National Institutes of Health, RePORTER application 11471611, Mach-LETSGO: Machine-LEarning of Treatment, Survey, and Genetics towards Obtaining Correct Classification of Chronic Conditions in Adult Survivors in the Childhood Cancer Survivor Study - CCSS Suppl (3U24CA055727-32S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11471611. Licensed CC0.

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