Original Articles
 

By Dr. Esther Una Cidon
Corresponding Author Dr. Esther Una Cidon
Oncology Department, Royal Bournemouth Hospital, Castle Lane East - United Kingdom BH7 7DW
Submitting Author Dr. Esther Una Cidon
CANCER

Triple negative, Breast cancer, ultrasound, neoadjuvant, chemotherapy, radiological response

Una Cidon E. Triple negative breast cancer under neoadjuvant treatment: the role of assessment ultrasound.. WebmedCentral CANCER 2021;12(1):WMC005687

This is an open-access article distributed under the terms of the Creative Commons Attribution License(CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
No
Submitted on: 10 Jan 2021 03:29:02 PM GMT
Published on: 12 Jan 2021 03:39:39 AM GMT

Abstract


Triple-negative breast cancer is a separate entity that comprises several molecular subtypes driven by specific genetic alterations that might potentially be targeted. However, currently most patients continue to receive standard chemotherapy regimens.

Neoadjuvant treatment is accepted for larger tumours and locally advanced disease as it might offer surgical and oncological advantages; among them, it allows a live assessment of tumour sensitivity to treatment, leading to a prompt discontinuation of ineffective therapies to avoid unnecessary toxicities.

It is well known that TNBC is very responsive to chemotherapy, with high rates of pathologic complete responses that can also be quite rapid. 

In this scenario, an accurate assessment of residual tumour size and extension becomes crucial for an adequate surgical planification and a prognostic prediction. 

Patients should be assessed before, half-way through neoadjuvant chemotherapy and at the end of this treatment. It seems that MRI is the most accurate technique to assess this, but ultrasound and mammogram are the most widely used. 

In this context of uncertain diagnosis, we decided to evaluate our results in an audit of TNBC patients receiving neoadjuvant treatment. Our aim was to know the role of ultrasound alone in assessing the pathological response in our patients. 

Background


Triple-negative breast cancer (TNBC) is a heterogeneous subgroup of breast cancer (BC) defined by the lack of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This entity comprises several histological and molecular subtypes driven by specific genetic alterations. These could potentially be targeted, however, most of these patients continue nowadays to receive standard chemotherapy regimens.

Neoadjuvant treatment is considered standard for patients with larger tumours (> 2.0 cm) and locally advanced disease [1] as this might offer surgical and oncological advantages, such as reduction in tumour size which might allow surgical resections in those initially ineligible [1] or switching from mastectomy to breast conservative treatment [1] [2].

In addition to these, it allows as well an in vivo assessment of tumour sensitivity to treatment, [1] leading to a prompt discontinuation of ineffective therapies to avoid unnecessary toxicities [1].

It is well known that TNBC is very responsive to chemotherapy, with high rates of pathologic complete responses (pCR] [3,4] that can also be quite rapid. In fact, Huober, et al. [5] have reported responses after only two cycles of treatment.

This has a significant clinical impact as it has been demonstrated a survival advantage for patients who show pCR over those with residual disease after neoadjuvant treatment. [6,7].

In this scenario, an accurate assessment of residual tumour size and extension becomes crucial for an adequate surgical planification and a prognostic prediction. Nowadays, surgery is still unavoidable even in the context of pCR, but there are trials underway which could give us further answers. 

Patients should be assessed before, half-way through neoadjuvant chemotherapy and at the end of this treatment [8]. But in this context, the question arising is:  can any imaging technique allow an early prediction of pCR in these patients?

Old studies such as the published by Atkins et al in 2012, have reported that breast ultrasound and MRI were more accurate than mammogram in predicting residual tumour size following neoadjuvant chemotherapy in TNBC patients, showing, however, that none of these modalities was predictive of a pCR [9].

Other studies have shown that breast ultrasound is clearly more accurate than mammogram in predicting the size of residual disease (91.3% compared to only 51.9% respectively).  However, once again, there was no difference in their ability to predict a pCR [10].

MRI has an excellent ability for assessing both residual disease extent and early treatment response [11,12] and also a better correlation with pathological findings.

However, despite these benefits, it is not performed routinely in all centres, whereas mammogram and ultrasound continue to be the preferred radiological evaluations. Both modalities are performed together rather than separately and predict the amount of residual disease and complete pathological response before any surgical planning.

With all these inconsistent findings, we decided to assess our results in an audit of our TNBC patients. Our aim was to evaluate the role of ultrasound alone in assessing the pathological response in TNBC patients receiving neoadjuvant chemotherapy.

Audit results


We evaluated the data of 33 patients treated within the previous 12 months. All were women, with a mean age 50 years old (27-70). All received neoadjuvant chemotherapy. Different regimens were used, either Carboplatin and Paclitaxel +/- Olaparib within the Partner trial or the standard FEC/Docetaxel or FEC/Paclitaxel.

Patients were assessed initially, half-way through the chemotherapy and at the end with an US of the breast and axilla. All of them performed a mammogram as well but this was not taken into consideration for our audit purpose.  

Our results showed that the US helped classify correctly 62.5% of the patients, with a tendency to maximise real pathological benefits as shown in the table 1 and 2.

Table 1: 

Response

Radiological

Pathological

Wrong US classification/ real pathological response

CR

12

7

5 / PR

 

 

 

 

PR

18

12

6 / 1 SD and 5 CR

 

 

 

 

SD

1

0

1 / 1 PD

 

 

 

 

PD

1

1

0


CR – complete response, PR – partial response, SD – stable disease, PR – progressive disease

Table 2: 

US classification

 

Diminishing real response

Maximising real response

Correct

20 (62.5%)

 

 

Wrong

12 (37.5%)

6 (50%)

6 (50%)

However, if we consider only response, regardless of complete or partial, and add SD as well to this group, the US is able to classify correctly 96.8% of patients (Table 3). 

Table 3: 

US response (CR + PR + SD)

 

Correct

31 (96.8%)

Wrong

1 (3.2%)

When we assessed only the ability of predicting pathological CR (pCR), our audit showed 12 pCR but only 5 were correctly diagnosed by the US (41.6%).

Brief discussion


Our results have clearly evidenced that the US, as radiological evaluation of tumour response in TNBC patients receiving neoadjuvant chemotherapy, is far away from ideal in terms of correct classification of pCR. However, it is able to guess correctly if a patient is responding to chemotherapy in most cases. We only found that it clearly failed when showing radiologically SD as the pathological finding showed clear PD.

Another interesting data from our audit is the fact that the US could underestimate the real benefit but it can also overestimate it in a similar proportion of cases. 

There are several studies in the published literature showing that although there are different radiological techniques used to assess tumour response in this group of patients, MRI has been found the most accurate of all in patients receiving NACT [13,14].

The definitions of radiological CR are different among imaging techniques even if the procedures use the WHO [15] or the EORTC/ RECIST [16] criteria.

As examples, the study by Schott et al determined the sensitivity of mammogram, US, and MRI for detecting pathological CR (pCR) in this context as 50%, 25%, and 25%, respectively [17].

Shin et al published an accuracy of pCR prediction of 38% for mammography, 13% for US, and 75% for MRI [18]. Our study shows that US is able to predict pCR in >40% of cases.

In our population of patients we seem to have obtained better figures for the role of US in the assessment of response. However, our study is very small and carried out in a single centre, focused only on a specific entity of breast cancer, factors that will diminish the possibility of extrapolation but not the value of these findings to our patients. 

Conclusion


Despite our better results, the assessment US continues to give diagnostic uncertainty. Our findings, give us a clear idea of how to interpret US assessments and how to explain adequately to our patients, taking into account the pitfalls of this technique. In this way, patients could be prepared for the potential change in pathological results after surgery when comparing to radiological findings, and also understand clearly why at this point in time, surgery is unavoidable.  

References


  1. Thompson M, Moulder-Thompson SL (2015) Neoadjuvant treatment of breast cancer. Ann Surg Oncol 22: 1425-1433.
  2. Mamounas EP (2015) Impact of neoadjuvant chemotherapy on locoregional surgical treatment of breast cancer. Ann Surg Oncol 22: 1425-1433.
  3. Brouckaert O, Wildiers H, Floris G, Neven P (2012) Update on triple-negative breast cancer: Prognosis and management strategies. Int J Womens Health 4: 511-520.
  4. Von Minckwitz G, Martin M (2012) Neoadjuvant treatments for triple-negative breast cancer (TNBC). Ann Oncol 23.
  5. Huober J, Von Minckwitz G, Denkert C, Tesch H, Weiss E, et al. (2010) Effect of neoadjuvant anthracycline-taxane-based chemotherapy in different biological breast cancer phenotypes: Overall results from the Gepar Trio study. Breast Cancer Res Treat 124: 133-140.
  6. Kuerer HM, Newman LA, Smith TL, Ames FC, Hunt KK, et al. (1999) Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol 17: 460-469.
  7. Guarneri V, Broglio K, Kau SW, Cristofanilli M, Buzdar AU, et al. (2006) Prognostic value of pathological complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol 24: 1037-1044.
  8. Bhattacharyya M, Ryan D, Carpenter R, Vinnicombe S, Gallagher CJ (2008) Using MRI to plan breast-conserving surgery following neoadjuvant chemotherapy for early breast cancer. Br J Cancer 98: 289-293.
  9. Jordan J. Atkins, Catherine M. Appleton, Carla S. Fisher, Feng Gao, Julie A. Margenthaler,(2013) "Which Imaging Modality Is Superior for Prediction of Response to Neoadjuvant Chemotherapy in Patients with Triple Negative Breast Cancer?", Journal of Oncology.  https://doi.org/10.1155/2013/964863
  10. Lobbes M, Prevos R, Smidt M (2012) Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using breast MRI - a review of current knowledge. J Cancer Ther Res 1: 1-9.
  11. McLaughlin R, Hylton N (2011) MRI in breast cancer therapy monitoring. NMR Biomed 24: 712-720.
  12. Keune JD, Jeffe DB, Schootman M, Hoffman A, Gillanders WE, Aft RL. (2010) Accuracy of ultrasonography and mammography in predicting pathologic response after neoadjuvant chemotherapy for breast cancer. Am J Surg. 199(4):477-484.
  13. Croshaw R, Shapiro-Wright H, Svensson E, Erb K, Julian T (2011) Accuracy of clinical examination, digital mammogram, ultrasound, and MRI in determining postneoadjuvant pathologic tumor response in operable breast cancer patients. Ann Surg Oncol 18(11): 3160–3163.
  14. Marinovich ML, Houssami N, Macaskill P, Sardanelli F, Irwig L, Mamounas EP, von Minckwitz G, Brennan ME, Ciatto S (2013) Meta-analysis of magnetic resonance imaging in detecting residual breast cancer after neoadjuvant therapy. J Natl Cancer Inst 105(5): 321–333.
  15. Miller AB, Hoogstraten B, Staquet M, Winkler A (1981) Reporting results of cancer treatment. Cancer 47(1): 207–214.
  16. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2): 228–247.
  17. Schott AF, Roubidoux MA, Helvie MA, Hayes DF, Kleer CG, Newman LA, Pierce LJ, Griffith KA, Murray S, Hunt KA, Paramagul C, Baker LH (2005) Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy. Breast Cancer Res Treat 92(3): 231–238.
  18. Shin HJ, Kim HH, Ahn JH, Kim SB, Jung KH, Gong G, Son BH, Ahn SH (2011) Comparison of mammography, sonography, MRI and clinical examination in patients with locally advanced or inflammatory breast cancer who underwent neoadjuvant chemotherapy. Br J Radiol 84(1003): 612–620.

Source(s) of Funding


n/a

Competing Interests


n/a

Reviews
0 reviews posted so far

Comments
0 comments posted so far

Please use this functionality to flag objectionable, inappropriate, inaccurate, and offensive content to WebmedCentral Team and the authors.

 

Author Comments
0 comments posted so far

 

What is article Popularity?

Article popularity is calculated by considering the scores: age of the article
Popularity = (P - 1) / (T + 2)^1.5
Where
P : points is the sum of individual scores, which includes article Views, Downloads, Reviews, Comments and their weightage

Scores   Weightage
Views Points X 1
Download Points X 2
Comment Points X 5
Review Points X 10
Points= sum(Views Points + Download Points + Comment Points + Review Points)
T : time since submission in hours.
P is subtracted by 1 to negate submitter's vote.
Age factor is (time since submission in hours plus two) to the power of 1.5.factor.

How Article Quality Works?

For each article Authors/Readers, Reviewers and WMC Editors can review/rate the articles. These ratings are used to determine Feedback Scores.

In most cases, article receive ratings in the range of 0 to 10. We calculate average of all the ratings and consider it as article quality.

Quality=Average(Authors/Readers Ratings + Reviewers Ratings + WMC Editor Ratings)