Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer

BACKGROUND

While important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited.

PURPOSE

To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients.

STUDY TYPE

Retrospective.

POPULATION

In all, 892 female invasive breast cancer patients.

SEQUENCE

Dynamic contrast-enhanced MRI with field strength 1.5 T and 3 T.

ASSESSMENT

Computer vision algorithms were applied to extract a comprehensive set of 529 imaging features quantifying size, shape, enhancement patterns, and heterogeneity of the tumors and the surrounding tissue. Using a development set with 446 cases, we selected 20 imaging features with high prognostic value.

STATISTICAL TESTS

We evaluated the imaging features using an independent test set with 446 cases. The principal statistical measure was a concordance index between individual imaging features and patient distant recurrence-free survival (DRFS).

RESULTS

The strongest association with DRFS that persisted after controlling for known prognostic clinical and pathology variables was found for signal enhancement ratio (SER) partial tumor volume (concordance index [C] = 0.768, 95% confidence interval [CI]: 0.679-0.856), tumor major axis length (C = 0.742, 95% CI: 0.650-0.834), kurtosis of the SER map within tumor (C = 0.640, 95% CI: 0.521-0.760), tumor cluster shade (C = 0.313, 95% CI: 0.216-0.410), and washin rate information measure of correlation (C = 0.702, 95% CI: 0.601-0.803).

DATA CONCLUSION

Quantitative assessment of breast cancer features seen in a routine breast MRI might be able to be used for assessment of risk of distant recurrence.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.

Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival

PURPOSE

The purpose of the study was to define quantitative measures of intra-tumor heterogeneity in breast cancer based on histopathology data gathered from multiple samples on individual patients and determine their association with distant recurrence-free survival (DRFS).

METHODS

We collected data from 971 invasive breast cancers, from 1st January 2000 to 23rd March 2014, that underwent repeat tumor sampling at our institution. We defined and calculated 31 measures of intra-tumor heterogeneity including ER, PR, and HER2 immunohistochemistry (IHC), proliferation, EGFR IHC, grade, and histology. For each heterogeneity measure, Cox proportional hazards models were used to determine whether patients with heterogeneous disease had different distant recurrence-free survival (DRFS) than those with homogeneous disease.

RESULTS

The presence of heterogeneity in ER percentage staining was prognostic of reduced DRFS with a hazard ratio of 4.26 (95% CI 2.22-8.18, p < 0.00002). It remained significant after controlling for the ER status itself (p < 0.00062) and for patients that had chemotherapy (p < 0.00032). Most of the heterogeneity measures did not show any association with DRFS despite the considerable sample size.

CONCLUSIONS

Intra-tumor heterogeneity of ER receptor status may be a predictor of patient DRFS. Histopathologic data from multiple tissue samples may offer a view of tumor heterogeneity and assess recurrence risk.

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

PURPOSE

To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

METHODS

Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.

RESULTS

Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582-0.833, p < 0.002).

CONCLUSIONS

The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.