Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography

doi:10.21203/rs.3.rs-4975514/v1

Background

Cone-beam breast computed tomography (CBBCT) can provide detailed information about breast tissue, but whether such information can help predict treatment response is unclear.

Purpose

To develop a nomogram based on findings from CBBCT as well as conventional clinical variables to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Materials and Methods

Medical data were retrospectively analyzed for a consecutive series of women with breast cancer who underwent NAC followed within three months by resection surgery at our hospital between September 2019 and March 2022. Patients were randomized into a development cohort and validation cohort. A nomogram to predict pCR after chemotherapy was formulated based on uni- and multivariate logistic regression of pre-treatment data from the development cohort, and it was tested against data from the validation cohort. The performance of the nomogram was evaluated in terms of the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis.

Results

Of the 215 breast cancer patients in this study, 69 (32.1%) achieved pCR after NAC. Multivariate logistic regression of the development cohort linked such response independently to absence of estrogen receptor (ER) expression, expression of human epidermal growth factor receptor 2 (HER-2), small tumor diameter and non-mass enhancement (NME) on CBBCT. The resulting nomogram predicted response with AUCs of 0.841 (95% CI: 0.78–0.90) in the development cohort (n = 150) and 0.836 (95% CI: 0.74–0.94) in the validation cohort (n = 65), and it was efficient against data from both cohorts based on calibration curves. Decision curve analysis suggested that the nomogram is clinically useful.

Conclusion

A nomogram incorporating molecular biomarkers and findings from CBBCT may help predict breast cancer patients more likely to respond to NAC.

Globally breast cancer is the most frequent cancer and cause of cancer-related death among women^{[1, 2]}. Administering neoadjuvant chemotherapy (NAC) before resection surgery can reduce the volume and stage of breast tumors and improve prognosis^[3–6], making it a popular first-line treatment for locally advanced breast cancer^[7–11]. Such treatment is particularly effective when it achieves pathologic complete response (pCR), which is associated with significantly higher recurrence-free survival^[3]. However, fewer than 30% of patients achieve this response^{[3, 6, 8, 12]}, and other patients may not receive sufficient benefit from NAC to justify the substantial risk of toxic side effects^[13]. These considerations highlight the need for a reliable way to identify patients more likely to show pCR after NAC, which would help individualize treatment planning^[3].

We wondered whether it might be possible to predict pCR using the emerging technique of cone-beam breast computed tomography (CBBCT)^[14], which can eliminate the influence of tissue overlap to visualize the tumor and its microenvironment in three dimensions^{[15, 16]}. While delivering a radiation dose similar to that of mammography, CBBCT can detect breast tumors with sensitivity similar to that of magnetic resonance imaging (MRI)^{[4, 17–19]}. At the same time, fewer patients have contraindications to CBBCT than to MRI, and tomographic scanning exposes patients to less discomfort^{[15, 20, 21]}. Given that MRI findings have been shown to predict pathologic response to NAC^{[9, 11, 13]}, especially when combined with conventional clinical factors^{[3, 7, 10]}, we wanted to know whether the same might be true of findings from CBBCT. One study has shown that CBBCT findings after NAC were associated with pCR, leading us to wonder whether pre-treatment CBBCT findings could predict pCR ^[4].

Thus, we retrospectively analyzed data from breast cancer patients who underwent NAC followed by surgery at our hospital in order to identify clinicodemographic factors and CBBCT findings at baseline that may be associated with pCR after NAC. We found that a nomogram combining the two types of information accurately predicted such response, so it may be a useful tool for personalizing treatment.

Patients and neoadjuvant chemotherapy

This retrospective study was approved by the Ethics Review Board of Guangxi Medical University Cancer Hospital, which waived the requirement for written informed consent because patients at admission consented to the analysis and publication of their anonymized medical data for research purposes. We screened a consecutive series of patients with breast cancer who underwent NAC followed within three weeks by resection surgery at our hospital between September 2019 and March 2022. We excluded patients if they did not undergo CBBCT before initiating chemotherapy, if their CBBCT images were of inadequate quality to determine breast tissue parameters, if they were biopsied (puncture or excision) before CBBCT, or if they received treatments in addition to NAC and surgery. The patient enrollment flowchart is shown in Fig. 1.

The NAC for all enrolled patients included anthracycline and taxane. Those whose tumors expressed human epidermal growth factor receptor 2 (HER-2) also received trastuzumab.

CBBCT

All CBBCT in this study was performed on a KBCT-1000 system (Corning, Tianjin, China), which has been approved for clinical use by the Food and Drug Administration in the US and the State Food and Drug Administration in China. Voltage in the X-ray tube was 49 kVp, and current was adjusted between 50 and 160 mA according to the volume and density of the breast. Patients lay prone on the examination bed, and the breast was allowed to drop naturally into the scanning area. The ipsilateral arm lay naturally at the body's side, while the contralateral arm was raised up. The healthy breast was scanned first, followed by the affected breast. Each breast was scanned for 8 sec. Next, CBBCT was performed by injecting patients intravenously with the non-ionic contrast agent iodohexyl alcohol (350 mg I/mL) using a double-chamber syringe at a rate of 2 mL/sec up to a cumulative dose of 1.5-2.0 mL/kg. The healthy and affected breasts were scanned at 60 and 110 sec after the end of contrast agent injection.

All CBBCT images were retrospectively reviewed independently by two radiologists, both of whom had more than 10 years of experience and were blinded to other data about the patients. Any discrepancies in the assessments by the two radiologists were resolved through discussion. The radiologists assessed the images for abnormal enhancement, which they classified as "mass enhancement" or "non-mass enhancement" (NME); and for the presence of dense breast tissue, which was defined as tissue showing compositions C or D according to published guidelines^[22]. They also measured tumor diameter by selecting the longest of the diameters determined from horizontal, sagittal and coronal images. For patients with multiple lesions, the tumor diameter was defined as the diameter of the largest tumor. The rate of enhancement was measured during phase I and phase II acccording to the formula:

Rate = (attenuation before enhancement - attenuation after enhancement) / attenuation before enhancement × 100%.

Some descriptive features of breast tumors were adapted from features routinely assessed during mammography or MRI as described in the BI-RADS Atlas^[22] and other literature. Several other disease features were extracted from CBBCT scans as described^{[15, 23]}, including microcalcification, irregular margins, lobulation, spiculation, and Increased vascularity.

Pathologic analysis before NAC

Breast tissue of all patients was biopsied after CBBCT and before NAC. Biopsies were immunostained against estrogen receptor (ER) and progesterone receptor (PR). Expression of HER-2 was assessed using immunohistochemistry and/or fluorescence in situ hybridization. Positive staining of > 1% of all examined cells was defined as the presence of expression.

Assessment of pathologic response after NAC

Breast tissue was excised after the last NAC cycle and assessed for pathologic response using histopathology and the Miller-Payne system^[24]. Complete response was defined as grade 5, corresponding to the absence of histological evidence of malignancy at the site of the primary tumor, or the presence of only carcinoma in situ.

Development of a nomogram to predict pCR

Patients were randomly allocated in the ratio 7:3 into a development cohort and a validation cohort. Univariate logistic regression was performed on data from the development cohort in order to identify pre-treatment factors significantly associated with pCR to NAC. Factors that were associated with P < 0.05 were included in multivariable logical stepwise regression to identify independent predictors of pCR. The resulting predictors were assembled into a nomogram, whose predictive performance against data from the validation cohort was assessed in terms of area under the receiver operating characteristic curve (AUC), calibration plots, and decision curves.

Other statistical analyses

Continuous data were reported as mean ± SD if normally distributed or as median (interquartile range) if skewed. Intergroup differences were assessed for significance using the Mann–Whitney U test. Categorical data were reported as n (%), and intergroup differences were assessed using the chi-squared or Fisher’s exact test.

We included 215 breast cancer patients in the study, and their randomization led to development and validation cohorts that did not differ significantly from each other in any of the baseline characteristics that we examined (Table 1), or in the rate of pCR (Table 2).

Univariate analysis identified the following variables as associated with such response (Table 3): postmenopausal status; expression status of ER, PR and HER-2; Ki-67 index; and the three CBBCT parameters of tumor diameter, whether enhancement was mass or NME, and whether breast tissue was dense. Of these variables, the ones that emerged from multivariable analysis as independent predictors of pCR were ER negative, HER-2 positive, small tumor diameter, and NME. Representative examples of breast tumors showing pCR or no-pCR are given in Figs. 2 and 3, respectively.

Using these four independent predictors, we constructed a nomogram to predict pCR after NAC (Fig. 4). The nomogram showed an AUC of 0.841 (95% CI: 0.78–0.90) in the development cohort and 0.836 (95% CI: 0.74–0.94) in the validation cohort (Fig. 5), indicating good predictive ability. In both cohorts, calibration curves indicated that the nomogram was efficient (Fig. 6). Decision curve analysis of both cohorts showed that if the threshold probability was > 5%, the nomogram was superior to assuming that all patients would show pCR or that none would (Fig. 7).

Here we provide evidence that the combination of two routinely assessed characteristics of breast cancer and two parameters determined from CBBCT can help predict which patients are more likely to show pCR after NAC. The nomogram included four pre-treatment factors as independent predictors: ER negative, HER-2 positive, small tumor diameter, and NME.

In our cohort, the findings of ER negative and HER-2 positive independently predicted pCR. Previous studies have linked each finding to greater likelihood of pCR^[25-27], perhaps because tumors with these characteristics proliferate quite fast, making them more sensitive to NAC^{[27, 28]}. The fact that patients with HER-2 positive breast cancer received trastuzumab, a specific targeted therapy for HER2-positive tumors, may also have contributed to the observed association between HER-2 expression and pCR^[29-31].

In our cohort, NME based on CBBCT was associated with higher probability of pCR. The majority of previous research on NME has focused on the differentiation between benign and malignant lesions^[32-35], and we are unaware of literature specifically addressing the association between NME and the probability of pCR after NAC. According to our study, the probability of achieving pCR after NAC in patients with NME on CBBCT is 3.2-fold higher than that in patients whose lesions show mass enhancement. This finding extends the clinical usefulness of NME. NME may be associated with tumors that are more aggressive and proliferate more rapidly, and are therefore more vulnerable to chemotherapy.

Smaller tumors before NAC were associated with greater likelihood of pCR in our cohort. Reduction of tumor burden is the important factor to achieve pCR after NAC^{[36, 37]}. Previous studies have found that chemotherapy drugs can reduce tumor cell structure to varying degrees. The extent of tumor cell loss after NAC is important for assessing the response to NAC^[27]. The smaller the diameter of the tumor before NAC, the greater the proportion of cell structure destroyed by the chemotherapy drug, and the easier it is to achieve pCR.

Although CBBCT, unlike MRI, can display calcification stereoscopically^[4], the absence or presence of microcalcification in breast tissue was not associated with likelihood of pCR in our cohort. While some work has suggested that changes in calcifications after NAC correlate with tumor response to NAC^{[38, 39]}, other studies have observed no association between microcalcification and pCR^{[40, 41]}. Future research should explore the prognostic significance, if any, of breast microcalcifications in patients undergoing NAC.

Our findings should be interpreted with caution given that our sample came from a single center and was analyzed retrospectively, increasing risk of selection bias. Our results should be verified and extended in larger, preferably prospective studies from multiple centers. Such work should take into account axillary status as a potential predictor of response to NAC, since axillary status after NAC is beneficial to the selection of surgical methods after NAC.^[42]. Consensus guidelines on interpreting images from CBBCT are needed in order to ensure that consistent practices lead to robust evidence for or against using this imaging technique to diagnose breast cancer and predict prognosis.

Despite these limitations, our results suggest that the combination of conventional clinical characteristics and two parameters from CBBCT, NME and tumor diameter, may improve the prediction of which breast cancer patients will show pCR after NAC. Both of those CBBCT parameters can also be assessed using MRI and breast mammography, implying that our nomogram may be compatible with other imaging techniques.

Funding

This research was funded by Natural Science Foundation of Guangxi Province (

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YJZ, YNJ, NBL, XYH, ZML, YJY, YNM. The first draft of the manuscript was written by YJZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Table 1. Clinicodemographic characteristics of patients in the study

Characteristic	All patients (N = 215)	Development cohort (n = 150)	Validation cohort (n = 65)	P
Menstrual status				0.157
Postmenopausal	78	59	19
Premenopausal	137	91	46
Expression of ER				0.648
Negative	81	58	23
Positive	134	92	42
Expression of PR				0.648
Negative	81	58	23
Positive	134	92	42
Expression of HER-2				0.648
Negative	114	78	36
Positive	101	72	29
Ki-67 index	46.9±22.9	47.3±22.7	45.9±23.6	0.580
pCR Yes No	69 146	51 99	18 47	0.363

Values are mean ± SD or n, unless otherwise noted. ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; pCR, pathologic complete response.

Table 2. Pre-treatment CBBCT findings for patients in the study.

Characteristic	All patients (N = 215)	Development cohort (n = 150)	Validation cohort (n = 65)	P
Tumor diameter, cm	3.9±1.7	3.9±1.7	3.8±1.7	0.314
Type of enhancement				0.458
Mass	172	104	54
NME	43	32	11
Multifocal disease				0.711
Absent	63	45	18
Present	151	104	47
Unknown	1	1	0
Dense tissue				0.620
Absent	51	37	14
Present	164	113	51
Microcalcification				0.122
Absent	73	46	27
Present	142	104	38
Irregular margins				0.426
Absent	54	40	14
Present	161	110	51
Lobulation				0.118
Absent	40	32	8
Present	175	118	57
Spiculation				0.506
Absent	100	72	28
Present	115	78	37
Increased vascularity				0.590
Absent	48	35	13
Present	167	115	52
Phase I enhancement rate, %	2.5±5.3	2.1±3.5	3.3±8.0	0.108
Phase II enhancement rate, %	2.5±5.4	2.1±3.0	3.5±8.7	0.095

Values are mean ± SD or n, unless otherwise noted. CBBCT, cone-beam breast computed tomography; NME, non-mass enhancement.

Table 3. Uni- and multivariate analysis of the development cohort to identify pre-treatment factors associated with pCR to neoadjuvant chemotherapy

Factor	Univariate		Multivariate
Factor	OR (95% CI)	P	OR (95% CI)	P
Postmenopausal status	2.20 (1.06-4.58)	0.034	1.76 (0.65-4.80)	0.271
ER negative	7.23 (3.40-15.35）	＜0.001	6.80 (2.02-22.92)	0.002
PR negative	2.80 (1.39-5.63）	0.004	1.13 (0.37-3.50)	0.833
HER-2 positive	4.8 (2.30-10.13)	＜0.001	3.90 (1.57-9.73)	0.003
Ki-67 index	1.02 (1.00-1.04)	0.015	1.01 (0.98-1.03)	0.641
Tumor diameter	0.78 (0.62-0.98)	0.028	0.61 (0.44-0.86)	0.004
NME	2.80 (1.26-6.24)	0.012	3.20 (1.01-10.21)	0.049
Multifocal disease present	0.80 (0.39-0.1.66）	0.548
Dense tissue type present	4.40 (1.60-12.12)	0.004	2.89 (0.79-10.54)	0.108
Microcalcification present	0.72 (0.35-1.49)	0.379
Irregular margins present	0.52 (0.25-1.10)	0.089
Lobulation present	0.50 (0.22-1.10)	0.086
Spiculation present	0.52 (0.26-1.02)	0.058
Increased vascularity present	0.71 (0.33-1.55)	0.393
Phase I enhancement rate	1.04 (0.95-1.15)	0.397
Phase II enhancement rate	1.04 (0.94-1.16)	0.445

CI, confidence interval; ER, estrogen receptor; OR, odds ratio; PR, progesterone receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response.

No competing interests reported.

Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography

Status:

Version 1

Abstract

Background

Purpose

Materials and Methods

Results

Conclusion

Figures

Introduction

Methods

Patients and neoadjuvant chemotherapy

CBBCT

Pathologic analysis before NAC

Assessment of pathologic response after NAC

Development of a nomogram to predict pCR

Other statistical analyses

Results

Discussion

Declarations

Funding

Author Contribution

References

Tables

Additional Declarations

Status:

Version 1