Rationale and Objectives
This study aims to investigate the value of a magnetic resonance imaging–based radiomics classifier for preoperatively predicting the Ki-67 status in patients with breast cancer.
Materials and Methods
We chronologically divided 318 patients with clinicopathologically confirmed breast cancer into a training dataset ( n = 200) and a validation dataset ( n = 118). Radiomics features were extracted from T2-weighted (T2W) and contrast-enhanced T1-weighted (T1+C) images of breast cancer. Radiomics feature selection and radiomics classifiers were generated using the least absolute shrinkage and selection operator regression analysis method. The correlation between the radiomics classifiers and the Ki-67 status in patients with breast cancer was explored. The predictive performances of the radiomics classifiers for the Ki-67 status were evaluated with receiver operating characteristic curves in the training dataset and validated in the validation dataset.
Results
Through the radiomics feature selection, 16 and 14 features based on T2W and T1+C images, respectively, were selected to constitute the radiomics classifiers. The radiomics classifier based on T2W images was significantly correlated with the Ki-67 status in both the training and the validation datasets (both P < .0001). The radiomics classifier based on T1+C images was significantly correlated with the Ki-67 status in the training dataset ( P < .0001) but not in the validation dataset ( P = .083). The T2W image–based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% confidence interval: 0.685, 0.838) and 0.740 (95% confidence interval: 0.645, 0.836) in the training and validation datasets, respectively.
Conclusions
The T2W image–based radiomics classifier was a significant predictor of Ki-67 status in patients with breast cancer. Thus, it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 status in clinical practice.
Introduction
Breast cancer is the most common cancer in women, and the incidence rates are increasing worldwide . As personalized, precision medicine is becoming more prevalent, tumor biomarker assays are playing a more important role in guiding clinical care . Biomarker assay results can indicate the diagnosis and prognosis, as well as the need for monitoring for recurrence or progression in patients with breast cancer according to the American Society of Clinical Oncology guidelines . In particular, the Ki-67 labeling index is known to be a valuable prognostic marker in breast cancer . Moreover, the Ki-67 value is a significant indicator of potential triage to chemotherapy, whereas the Ki-67-based Preoperative Endocrine Prognostic Index is a feasible predictor of the risk of relapse . According to a study by Dowsett et al. , the predictive performance of the recurrence-free survival can be improved by measuring the tumor Ki-67 labeling index in patients undergoing short-term endocrine therapy. However, the accuracy of traditional invasive detection methods, that is, biopsy samples, is influenced by sampling errors, as they ignore intratumoral heterogeneity expression . In addition, research has shown that the Ki-67 status dynamically changes during treatment , and thus it is difficult to evaluate the Ki-67 status using only one biopsy. Therefore, an accurate, noninvasive predictor of the Ki-67 status in patients with breast cancer is clinically desirable.
Radiomics, which differs from the traditional practice of using medical images solely for visual interpretation, is the transition of digital medical images into mineable data through the high-throughput extraction of abundant quantitative features based on shape, intensity, size, or volume, among others . Radiomics data can be applied to build descriptive or predictive models that correlate quantitative image features with phenotypes or gene-protein markers, potentially assisting in the following: cancer detection, diagnosis, and staging; predicting the response to treatment; monitoring the disease status; and assessing the prognosis . Radiomics features have already been shown to aid in the diagnosis , molecular subtyping , prognosis, and prediction of the response to treatment in patients with breast cancer . Furthermore, in a study by Ha et al., 18 F-fluorodeoxyglucose positron emission tomography–based radiomics patterns of locally advanced breast cancer were associated with Ki-67 expression, the pathologic complete response to neoadjuvant chemotherapy, and the risk of recurrence, supporting the potential of such patterns to assist in the development of personalized disease management strategies .
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Materials and Methods
Patients
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Ki-67 Measurements
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MR Image Acquisition
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Region of Interest Delineation
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Radiomics Feature Extraction
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Radiomics Feature Selection and Classifier Building
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The least absolute shrinkage and selection operator (LASSO) logistic regression analysis method, which is suited to the regression of high-dimensional data, was used to perform radiomics feature selection in the training dataset. This method minimizes the sum of squares of residues, with the sum of the absolute values of the selected features coefficients being no more than a tuning parameter (λ) . As λ becomes smaller, the coefficients of more features are set to 0. Features that contributed well to the model were selected and used to construct a radiomics classifier when the area under the receiver operating characteristic (ROC) curve (AUC) of the binary logistic regression model was the largest. Finally, a radiomics score (Rad-score) was calculated for each patient through the linear combination of the selected features weighted by their respective coefficients.
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Statistical Analyses
Patients’ Clinicopathologic Characteristics
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Performance of the Radiomics Classifiers
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Results
Patients’ Clinicopathologic Characteristics
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Radiomics Feature Selection and Classifier Building
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Performance of the Radiomics Classifiers
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TABLE 1
Radiomics Score (Rad-score) for Ki-67 Status in the Training and Validation Dataset
Rad-score Ki-67 (−) Median (IQR) Ki-67(+) Median (IQR)P T2 Training 0.841 (0.446, 1.155) 1.325 (0.963, 1.616) <.0001 Validation 0.956 (0.711, 1.268) 1.390 (1.064, 1.691) <.0001 T1+C Training 0.682 (0.301, 1.229) 1.397 (1.061, 1.654) <.0001 Validation 0.956 (0.625, 1.448) 1.206 (0.836, 1.554) .083
IQR, interquartile range; Rad-score, radiomics score; T1+C, contrast-enhanced T1-weighted images; T2, T2-weighted images.
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TABLE 2
The Predictive Performance of Radiomics Classifier Based on T2WI
Dataset Cutoff AUC (95% CI) SEN SPE Accuracy PPV NPV Training 1.044 0.762 (0.685,0.838) 0.720 0.700 0.715 0.878 0.455 Validation 1.044 0.740 (0.645,0.836) 0.759 0.645 0.729 0.857 0.488
95% CI, 95% confidence interval; AUC, area under curve; NPV, negative predictive value; PPV, positive predictive value; SEN, sensitivity; SPE, specificity.
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Discussion
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Acknowledgments
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Supplementary Data
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Appendix S1
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