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13th December 2021
An MRI radiomics model based on deep learning has shown good predictive accuracy at identifying whether patients with triple negative breast cancer (TNBC) would have systemic recurrence within three years of neoadjuvant chemotherapy, according to researchers from the Department of Radiology, Peking University, Beijing, China.
A triple negative breast cancer describes an aggressive tumour which lacks expression of oestrogen receptor, progesterone receptor and HER2 and accounts for around 15% of all breast cancers. Moreover, TNBC has been found to have the lowest 4-year survival rate at 77%.
Neoadjuvant chemotherapy is seen as the mainstay of treatment for early stage TNBC followed by definitive surgery although relapse within 3 years has been found to occur in up to 97% of patients. Magnetic resonance imaging (MRI) has been used to enable prediction of disease recurrence although these MRI radiomics models have required a radiologist to manually draw regions of interest (ROIs) as the input which is very time-consuming.
In the present study, the Chinese team used radiomics based on deep learning models and which utilised automated segmented ROIs of breast tumours from MRI images to predict whether patients with TNBC who had received neoadjuvant chemotherapy, would experience disease recurrence within three years of treatment. The team undertook a retrospective analysis of consecutive female patients with unilateral primary TNBC and for whom pre- and post-neoadjuvant MRI imaging was available. The researchers also collected clinico-pathologic factors from the patient medical records, e.g., menopausal status, the histological type of cancer, the clinical T and N stages. They developed a MRI radiomics model based on three separate features; pre-treatment features (model 1), post-treatment features (model 2) and a combination of pre and post-treatment MRI features (model 3). Multivariate analysis was used to assess associations between clinical factors and disease recurrence and the predictive performance of the models was assessed using the receiver operating characteristic curves and the area under the curves (AUC).
A total of 147 women with a median age of 49.5 years were included in the analysis, 104 (22 with disease recurrence) were used for the training cohort and 43 for the testing cohort (9 with disease recurrence) and the median time to disease recurrence was 17 months.
In both the univariate and multivariate analysis the clinical T stage and pathological T stage were significantly associated with systemic disease recurrence within three years of treatment (p < 0.05). Using these two variables in a clinical model yielded AUCs of 0.75 for the training cohort and 0.74 in the testing cohort. In comparison, the AUCs for each of the MRI radiomics model was 0.88 (model 1), 0.91 (model 2) and 0.96 (model 3).
Model 3 had a perfect sensitivity (i.e., 1) and a specificity of 0.85 compared to a sensitivity of 0.67 and specificity of 0.82 for the clinical model and this difference was statistically significant (p < 0.05). However, neither model 1 or 2 were significantly better than the clinical model.
Commenting on their findings, the authors speculated that the predictive ability of the third MRI radiomics model was likely to have a greater prognostic value by inclusion of post-neoadjuvant chemotherapy features that were reflective of treatment-induced changes.
They concluded that the value of their non-invasive model was in being able to more easily identify those at risk of disease recurrence and to therefore strengthen the treatment of these patients after surgery to improve their prognosis.