Published online Sep 24, 2021. doi: 10.5306/wjco.v12.i9.808
Peer-review started: February 23, 2021
First decision: June 16, 2021
Revised: June 24, 2021
Accepted: August 3, 2021
Article in press: August 3, 2021
Published online: September 24, 2021
There is evidence in the literature that breast cancer (BC) molecular subtypes often have characteristic imaging features on mammogram (MG), ultrasound (US) and magnetic resonance imaging. These imaging features on MG and US are of particular interest as they are cost-effective and widely available even in many developing countries.
Thus far, research into the correlation between MG and US imaging features and BC subtypes has been based on populations of symptomatic patients, with the lack of data on an asymptomatic (screening) population highlighted as an area for future research. We wanted to thus use our data which consists of both screening and symptomatic patients to add to the body of knowledge on this issue. Also, our population includes patients with ductal carcinoma-in-situ (DCIS) which only a few papers have examined.
To correlate the MG and US imaging features with the molecular subtypes of BC (hormone receptor positive vs hormone receptor negative, triple-negative vs non-triple negative and HER2 positive vs HER2 negative) and DCIS in our population of screening and symptomatic patients.
Our study is retrospective, with a population of 328 consecutive patients in 2017-18 with histologically confirmed BC. 237 (72%) were symptomatic, and 91 (28%) were detected via a screening program. All the patients underwent MG and US imaging prior to biopsy. The images were retrospectively interpreted by two breast-imaging radiologists with 5-10 years of experience who were blinded to the histology results to ensure statistical independence. To test the hypothesis that imaging features are correlated with tumor subtypes, univariate binomial and multinomial logistic regression models were performed. Also, multivariate logistic regression (with and without interaction terms) was utilized to identify combinations of MG and US imaging characteristics predictive of molecular subtypes.
Circumscribed margins, posterior enhancement, and large size are correlated with triple-negative BC (TNBC). High-risk microcalcifications and microlobulated margins is predictive of HER2-enriched cancers. DCIS is characterized by small size on US, absence of posterior acoustic features, and the presence of architectural distortion on MG. Hormone receptor positive subtypes tend to be small, with spiculated margins and posterior acoustic shadowing. These results are broadly consistent with findings from prior studies. In addition, we also find that US lesion size signals a higher odds ratio for TNBC if presented during screening.
Several MG and US imaging features were shown to independently predict molecular subtypes of BC, in a population of both screening and symptomatic patients. Knowledge of such correlations could help clinicians stratify BC patients, possibly enabling earlier treatment for patients with triple negative cancer. This could also aid therapeutic decisions in countries where receptor testing is not readily available.
To further research in this field, machine learning algorithms may be trained to recognize both the imaging characteristics as well as the radionomic characteristics of BC molecular subtypes, to see if this can further improve the predictive accuracy of imaging. More studies with asymptomatic populations of patients, and with differing ethnicities would also be useful to corroborate the results in our study.