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Press Releases

Take a look at a selection of our recent media coverage:

MRI radiomics model predicts disease recurrence in triple negative breast cancer after neoadjuvant chemotherapy

13th December 2021

An MRI radiomics model shows good predictive accuracy for the 3-year triple negative breast cancer recurrence after neoadjuvant chemotherapy

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.


Ma M et al. Radiomics features based on automatic segmented MRI images: Prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy Eur J Radiol 2021

Apatinib neoadjuvant chemotherapy effective in advanced gastric cancer

15th July 2021

Positive findings that apatinib neoadjuvant chemotherapy produces an R0 section rate of 75% in advanced gastric cancer required confirmation.

Internationally, data from 2018 reveal how there were just over 1 million cases of gastric cancer, leading to 782,000 deaths. Fortunately, early treatment of gastric cancer either through endoscopic resection or surgery, provides a cure rate of over 90%. In contrast, in patients with locally advanced gastric cancer, prognosis is poor, with a 5-year overall survival rate of 20–30% for surgery-only patients. Among those with advanced gastric cancer, systemic chemotherapy has become the standard treatment, in particular, the combination of S-1, which is an oral fluoropyrimidine derivative and oxaliplatin (SOX) and this regime has been shown to be effective. Another therapeutic approach involves treatments targeting vascular endothelial growth factor (VEGF), which has been shown to have an important role in promoting tumour angiogenesis. Apatinib is a small molecule tyrosine kinase inhibitor that selectively binds to vascular endothelial growth factor receptor 2 and which inhibits the action of VEGF. In fact, early data suggest that apatinib can improve overall survival in advanced gastric cancer.

In a review of 14 trials, the use of neoadjuvant chemotherapy, has been shown to improve tumour stage and survival in patients with advanced gastric cancer. However, little is known about the value of Apatinib in combination with neoadjuvant chemotherapy. This led a team from the Department of Gastric Surgery, Fujian Medical University Union Hospital, Fujian Province, China, to examine the effectiveness of this combination in patients with locally advanced gastric cancer. They conducted a multi-centre, open-label phase II, non-randomised controlled trial, in patients with confirmed primary gastric adenocarcinoma, without previous surgery, chemotherapy, radiotherapy or targeted therapy and no evidence of metastases. Patients with MO (i.e., no metastases) and either T2 to T4 (where the tumour has spread through the layers of the muscle into the connective tissue outside the stomach. All enrolled participants received 2 to 5 preoperative and 6 postoperative cycles of apatinib plus SOX every three weeks. Apatinib was given at a dose of 500 mg daily for 21 days and S1 twice daily on days 1 to 14 and intravenous oxaliplatin 130mg/square metre on day 1. Patients achieving a good response underwent surgery whereas those with a poor response received two further courses of adjuvant chemotherapy. The primary endpoint was the RO section rate (i.e., margin-negative resection with no tumour in the primary tumour bed) and which is the goal of surgery. The radiologic response was assessed using contrast-enhanced CT or magnetic resonance imaging.

A total of 48 patients with a mean age of 63.2 years (77.1% male) were included. All participants received 156 preoperative cycles neoadjuvant chemotherapy. The majority of patients (39.5%) had stage T3 cancer and nearly a third (36.9%) had stage T4 disease. Overall, 40 underwent surgery (38 radical gastrectomy and 2 exploratory laparotomy), with an R0 section rate of 75%. The radiological response rate was 75% and the pathological response rate, 54.2%.

Commenting on these early findings, the authors stated how the data indicated the effectiveness of apatinib and SOX chemotherapy as a neoadjuvant therapy for locally advanced gastric cancer. They concluded that apatinib plus SOX appeared to be an effective and well-tolerated regime and called for more studies to confirm this preliminary conclusion.

Lin JX et al. Effectiveness and Safety of Apatinib Plus Chemotherapy
as Neoadjuvant Treatment for Locally Advanced Gastric Cancer A Nonrandomised Controlled Trial
. JAMA Oncol 2021