Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer

Authors

Casey E Stowers
Chengyue Wu
Zhan Xu
Sidharth Kumar
Clinton Yam
Jong Bum Son
Jingfei Ma
Jonathan I Tamir
Gaiane M Rauch
Thomas E Yankeelov

Language

English

Publication Date

1-1-2025

Journal

Radiology: Artificial Intelligence

DOI

10.1148/ryai.240124

PMID

10.1148/ryai.240124

PMCID

PMC11791743

PubMedCentral® Posted Date

11-6-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Purpose

To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC).

Materials and Methods

In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC).

Results

The study included 118 female patients (median age, 51 years [range, 29–78 years]). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the concordance correlation coefficient values were 0.95 (95% CI: 0.90, 0.98) and 0.94 (95% CI: 0.87, 0.97), respectively. CNN-predicted TTC and TTV had an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.34, 0.94) and 0.72 (95% CI: 0.40, 0.95) for predicting tumor status at the time of surgery, respectively.

Conclusion

Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient’s tumor during NAC using only pre-NAC MRI data.

Keywords

Adult, Aged, Female, Humans, Middle Aged, Chemotherapy, Adjuvant, Deep Learning, Magnetic Resonance Imaging, Neoadjuvant Therapy, Neural Networks, Computer, Retrospective Studies, Treatment Outcome, Triple Negative Breast Neoplasms, Clinical Trials as Topic, Triple-Negative Breast Cancer, Neoadjuvant Chemotherapy, Convolutional Neural Network, Biology-based Mathematical Model

Comments

Supplemental material is available for this article.

Clinical trial registration no. NCT02276443

Published Open-Access

yes

Recommended Citation

Stowers, Casey E; Wu, Chengyue; Xu, Zhan; et al., "Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer" (2025). Faculty, Staff and Student Publications. 6224.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6224