Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer

Abstract

To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings. We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training ( = 194) and validation ( = 100) sets. A radiomics signature (Rad-score) was generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high- and low-risk groups was determined using receiver-operating characteristic curve analysis. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of the radiomics signature, MRI findings, and clinicopathological variables with DFS. A radiomics nomogram combining the Rad-score and MRI and clinicopathological findings was constructed to validate the radiomic signatures for individualized DFS estimation. Higher Rad-scores were significantly associated with worse DFS in both the training and validation sets ( = 0.002 and 0.036, respectively). The radiomics nomogram estimated DFS [C-index, 0.76; 95% confidence interval (CI); 0.74-0.77] better than the clinicopathological (C-index, 0.72; 95% CI, 0.70-0.74) or Rad-score-only nomograms (C-index, 0.67; 95% CI, 0.65-0.69). The radiomics signature is an independent biomarker for the estimation of DFS in patients with invasive breast cancer. Combining the radiomics nomogram improved individualized DFS estimation. .

Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules

“…Many studies have confirmed that radiomics has a high clinical application value [16][17][18]. Compared to the histopathological method, radiomics can be used as a "virtual biopsy" technique, can quantitatively analyze the entire tumor tissue, and has no sampling location limitation, thus providing richer and more complete tumor information for clinical diagnosis, treatment, and prognostic evaluation [19][20][21][22]. The applications of radiomics in the field of lung cancer have been investigated widely, and many studies have confirmed its good prospect [23][24][25][26].…”

Characterization of Sub‐1 cm Breast Lesions Using Radiomics Analysis

“…Early work utilized parameters calculated from gray level co‐occurrence matrices to discriminate between benign and malignant lesions . Subsequent studies have incorporated more complex measures of image texture to both aid diagnosis and to explore the efficacy of texture analysis in prognosis …”

“…12 Subsequent studies have incorporated more complex measures of image texture to both aid diagnosis 13 and to explore the efficacy of texture analysis in prognosis. [14][15][16] The pool of literature that specifically focuses on the characterization of small breast lesions utilizing MRI remains limited. Early work assessed morphologic and kinetic characteristics in a cohort of 43 patients with sub-1 cm lesions.…”

Radiomics Features of 18F-fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer

“…A radiomics signature-based nomogram has been developed and validated for preoperative prediction of survival in breast cancer, gastric cancer, and early-stage non-small cell lung cancer patients [28][29][30]. In our study, the developed nomogram incorporated a radiomics score derived from two components of the PET-based features, pN and LVI.…”

“…First, various computer algorithms have been used for feature extraction, and the types of features extracted by each algorithm were not uniform. Moreover, we applied standardization of features as a preprocessing step before entering the data into the LASSO COX model, following a method that has already been adopted for radiomics analysis in patients with breast or gastric cancer [28,30]. However, most previous studies of PETderived radiomics analysis in patients with CRC did not use standardization as a preprocessing step [17][18][19]43].…”