European radiology · 2025
A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.
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Hadidchi Roham, Agrawal Anchita, Liu Michael Z, Maldijan Takouhie, Zhu Yihui, Nguyen Hien Quang, Lu Jinyu, Makower Della, Fineberg Susan, Duong Tim Q
Аннотация
OBJECTIVE: The Nottingham Histologic Grade (NHG) informs prognosis and treatment decisions in breast cancer, but NHG2 tumors are biologically heterogeneous, leading to both under- and over-treatment.
MATERIALS AND METHODS: Clinical and imaging data from the Duke-Breast-Cancer-MRI (n = 877) and advanced-MRI-breast-lesions (n = 37) datasets were used to develop DeepRadGrade (DRG), a convolutional neural network model trained to differentiate NHG1 from NHG3 tumors on dynamic contrast-enhanced (DCE) MRI. The model then classified 456 NHG2 tumors into DRG2- (NHG1-like) and DRG2+ (NHG3-like) subgroups. Recurrence-free survival (RFS) was assessed with Kaplan-Meier and Cox models adjusting for age, lymph node invasion, tumor stage, and molecular subtype.
RESULTS: DRG achieved an AUC of 0.84 [95% CI: 0.83-0.86] in training, 0.82 [0.71-0.91] in testing, and 0.84 [0.69-0.96] in external validation. Among NHG2 tumors, 315 were classified as DRG2- and 131 as DRG2+. Patients with DRG2+ tumors had significantly worse RFS compared to DRG2- (adjusted hazard ratio = 2.39 [95% CI: 1.29-4.45], p = 0.0059), independent of standard prognostic factors. Incorporating DRG classification improved the Cox model's C-index from 0.68 to 0.73 (p = 0.040).
CONCLUSIONS: A deep learning model applied to routine DCE MRI effectively stratified NHG2 breast tumors into clinically meaningful subgroups with distinct recurrence risk. This approach offers a cost-effective tool for individualized risk stratification and could help tailor treatment to minimize over- and under-treatment in intermediate-grade breast cancer.
KEY POINTS: Question Difficulty in deciding between treatment options (neoadjuvant chemotherapy or primary surgery) in patients with intermediate risk breast cancer (Nottingham Grade 2). Findings Deep learning model reclassified grade 2 tumors into grade 1- and 3-like based on MRI. Patients with grade 3-like tumors had worse RFS: adjusted hazard ratio = 2.39 [95% CI: 1.29-4.45], p = 0.0059). Clinical relevance Risk stratification could inform treatment choice to minimize over- and under-treatment in patients with intermediate-risk breast cancer.
MATERIALS AND METHODS: Clinical and imaging data from the Duke-Breast-Cancer-MRI (n = 877) and advanced-MRI-breast-lesions (n = 37) datasets were used to develop DeepRadGrade (DRG), a convolutional neural network model trained to differentiate NHG1 from NHG3 tumors on dynamic contrast-enhanced (DCE) MRI. The model then classified 456 NHG2 tumors into DRG2- (NHG1-like) and DRG2+ (NHG3-like) subgroups. Recurrence-free survival (RFS) was assessed with Kaplan-Meier and Cox models adjusting for age, lymph node invasion, tumor stage, and molecular subtype.
RESULTS: DRG achieved an AUC of 0.84 [95% CI: 0.83-0.86] in training, 0.82 [0.71-0.91] in testing, and 0.84 [0.69-0.96] in external validation. Among NHG2 tumors, 315 were classified as DRG2- and 131 as DRG2+. Patients with DRG2+ tumors had significantly worse RFS compared to DRG2- (adjusted hazard ratio = 2.39 [95% CI: 1.29-4.45], p = 0.0059), independent of standard prognostic factors. Incorporating DRG classification improved the Cox model's C-index from 0.68 to 0.73 (p = 0.040).
CONCLUSIONS: A deep learning model applied to routine DCE MRI effectively stratified NHG2 breast tumors into clinically meaningful subgroups with distinct recurrence risk. This approach offers a cost-effective tool for individualized risk stratification and could help tailor treatment to minimize over- and under-treatment in intermediate-grade breast cancer.
KEY POINTS: Question Difficulty in deciding between treatment options (neoadjuvant chemotherapy or primary surgery) in patients with intermediate risk breast cancer (Nottingham Grade 2). Findings Deep learning model reclassified grade 2 tumors into grade 1- and 3-like based on MRI. Patients with grade 3-like tumors had worse RFS: adjusted hazard ratio = 2.39 [95% CI: 1.29-4.45], p = 0.0059). Clinical relevance Risk stratification could inform treatment choice to minimize over- and under-treatment in patients with intermediate-risk breast cancer.