1.1.

Involution of the Lactating Mammary Gland

Animal studies have demonstrated that the involution of the lactating mammary gland is a two-phase process.¹⁷ The first phase is reversible and involves extensive cell death of the milk-producing secretory epithelium in the alveoli. During this phase, the suckling of the mother’s offspring can reinitiate lactation. In the second phase, the mammary tissue is remodeled, which involves the breakdown of extracellular matrix, remodeling of blood vessels,¹⁸ and the re-emergence of lipid-filled adipocytes.¹⁷ MRI data from human volunteers have confirmed that significant changes occur during mammary involution in fibroglandular tissue content, as well as milk duct diameter.¹⁰ This process is also accompanied with changes in human milk protein, lactose, chloride, and sodium concentrations.¹⁹ The second involution phase is irreversible and returns the breast to a pre-pregnant state, albeit that epigenetic changes in the mammary tissue can facilitate lactation in subsequent reproductive cycles.²⁰ In mice, the duration of the first involution phase is $\sim 48\mathrm{h}$, whereas that of the second phase is $\sim 72$ to 144 h.¹⁷ In larger mammals, the total duration of the involution process may vary from several weeks to several months.^21,22 To our knowledge, the total duration of the involution of the human lactating breast has not yet been assessed objectively in the literature.

1.2.

Hypothesis and Objectives

On the macroscopic scale that is evaluated by DOSI, we expect several changes to occur in human mammary tissue composition during the involution process. Due to the replacement of fibroglandular tissue by adipose tissue, we hypothesize that mammary water content will decrease, and lipid content will increase during the involution process.¹⁷ We further hypothesize that mammary blood content decreases, due to vascular regression.¹⁸ The objective of this study was, therefore, to investigate whether DOSI can be employed to evaluate these involution related changes in mammary tissue composition, as well as the changes in oxygen saturation (${\mathrm{StO}}_2$) and the light scattering properties of tissue. Furthermore, we investigated whether we can use these changes in tissue composition to estimate the currently unreported time-dependence and duration of the mammary involution process.

2.1.

Participant

The participant in this case study was a 37-year-old woman, who was breastfeeding her first child unilaterally with her left breast. Four years prior to this study, the participant was treated for an early stage high-grade invasive ductal carcinoma of the right (contralateral) breast. Treatment consisted of unilateral mastectomy and adjuvant chemotherapy with docetaxel, carboplatin, and trastuzumab.

After a lactation period of 14 months, breastfeeding was ceased and the DOSI measurements were started. Data were acquired between June 2015 and February 2016, over a total time lapse of 237 days (8 months). During the first 104 days (3.5 months), the breast was scanned with DOSI every 1 to 3 weeks. One more DOSI scan was taken at 237 days, resulting in a total of 9 DOSI scans in time. Briefly after this last DOSI scan, the participant discovered that she was pregnant with her second child and the study was halted. The participant did not breastfeed or extract any milk during the study period. At the time of cessation, the participant had a large, palpable duct in her lactating breast, which did not have a suspicious appearance under ultrasonography and MRI. As involution continued, the palpable duct gradually became less noticeable.

Ethical approval for this study was obtained from the Institutional Review Board of the University of California, Irvine (#1995-563), and the participant gave written informed consent prior to the study.

2.2.

Diffuse Optical Spectroscopic Imaging

Instrumentation. DOSI measurements were performed with a system that combines frequency domain photon migration (FDPM) and broadband near infrared (NIR: 650 to 1000 nm) spectroscopy for quantitative, model-based measurements of tissue absorption and scattering properties and recovery of tissue oxy-hemoglobin (${\mathrm{ctHbO}}_2$), deoxy-hemoglobin (ctHHb), water (${\mathrm{ctH}}_2\mathrm{O}$), and lipid.²³ The custom-built DOSI instrument utilized an FDPM module with fiber-coupled, modulated (50 to 500 MHz) laser diode sources at four wavelengths (660, 690, 780, and 830 nm) to illuminate the breast. FDPM light was collected with a temperature-controlled avalanche photodiode sensor (1-mm diameter active area), built in to the handheld probe that was placed in direct contact with the skin during data acquisition. The broadband NIR component of the system consisted of a fiber-coupled white light source and spectrometer to measure continuous-wave reflectance. The separation between the light sources and detectors was 28 mm, which corresponds to $\sim 10$ to 15 mm of depth sensitivity. Standardized procedures were developed for data collection and to monitor instrument performance. Two sets of three calibration measurements on two custom-fabricated solid breast-tissue simulating phantoms (Institut National d’Optique, Quebec, Canada; and UC Irvine Beckman Laser Institute, California) and on one reflectance standard (SRS-99-020, Labsphere Inc.) were performed before and after each subject measurement. These measurements were used for determining the instrument response-function, and monitoring instrument performance over the duration of the study.²⁴

Spectroscopy. The approach to DOSI data acquisition and analysis has been previously described²³ and is briefly summarized here. Model-based fits of DOSI data were used to retrieve the wavelength resolved tissue absorption coefficient ${\mu }_a(\lambda )$ and reduced scattering coefficient ${\mu }_s^{\prime }(\lambda )$. Values for water, ctHHb, ${\mathrm{ctH}}_2\mathrm{O}$, and bulk lipid were calculated by fitting a linear combination of their known molar extinction coefficient spectra to the tissue absorption coefficient values. From these quantities, the total hemoglobin (THb) concentration ($\mathrm{THb}=\mathrm{ctHHb}+{\mathrm{ctHbO}}_2$) and percent ${\mathrm{StO}}_2$ (${\mathrm{StO}}_2={\mathrm{ctHbO}}_2/\mathrm{THb}$) were calculated. Optical scattering was characterized by parameterizing the reduced scattering spectrum as ${\mu }_s^{\prime }=a(\lambda /{\lambda }_0{)}^{-b}$, where $a$ is a scattering prefactor that indicates the reduced scattering coefficient at ${\lambda }_0=500\mathrm{nm}$ and $b$ is the scattering power, which describes the wavelength dependence of scattering.

Imaging procedure. DOSI measurements were performed using a standard protocol. The subject was measured in a supine or reclining position [Fig. 1(a)]. The handheld DOSI probe was placed against the breast tissue, and sequential measurements were recorded in a rectangular grid pattern using 10-mm spacing. Each measurement took $\sim 3\mathrm{s}$. The dimension of the grid was $9\times 11{\mathrm{cm}}^2$, resulting in 120 measurement locations [Fig. 1(b)]. The grid size was chosen to fully capture the breast. For consecutive DOSI visits, individual static landmarks such as moles or freckles were recorded on a transparency sheet and provided a fixed reference to coregister the imaging grid at follow-up DOSI sessions.

Fig. 1

Overview of DOSI. (a) Handheld DOSI probe and measurement setting for breast imaging demonstrated by researchers. (b) Measurement grid on the breast. Red and black dots denote the ROI for the areola, and nonareolar region, respectively. The gray dots account for the variability in areola size during the involution process and were not included in the analysis of the median DOSI parameters. The “x” denotes the data point that was excluded from the analysis due to poor probe contact during one of the measurements. (c) Example of a resulting 2-D map for the water distribution in the breast, with the same spatial dimensions as figure (b). The dashed red and black lines represent the outer boundaries of the ROIs for the areolar and nonareolar region, respectively. The areolar ROI is smaller than the actual size of the areola for this particular measurement (day 19).

Image analysis. After initial processing, DOSI data (chromophore concentrations and scattering parameters) from each session were plotted using grid coordinates and heatmap functions in MATLAB. Cubic spline interpolation was used to account for 10-mm spacing of grid points, resulting in colored 2-D maps for water, lipid, THb, ${\mathrm{StO}}_2$, scattering power, and scattering prefactor. An example for a 2-D water distribution map is given in Fig. 1(c).

After checking for normality, the median values of the optical property spectra and resulting DOSI parameters were calculated within both the areola (9 points) and nonareolar region (94 points), to evaluate their dynamics over the course of the involution process. Areola size was defined as the pigmented region that the DOSI operator recorded on the transparency sheet at day 0. Although the DOSI operator did not record the time dependency in areola size, subsurface DOSI measurements suggested that areola size decreased during the involution process. Therefore, a consistent analysis was achieved by defining conservative constant region of interest (ROI) for the areola region and nonareolar region. To account for a potential reduction in areola size, the DOSI areola ROI was defined as $\sim 2\text{-}\mathrm{cm}$ diameter less than the visually recorded areola size at day 0 [red dots, Fig. 1(b)]. The nonareolar ROI was defined as the nonareola size at day 0 [black dots, Fig. 1(b)]. Grid points outside these ROIs [gray dots, Fig. 1(b)] were not included in the analysis of the median DOSI parameters to account for possible reduction in areola size over time. One point was excluded from the nonareolar region at all time points, due to poor data quality at a single-measurement session, which was caused by poor probe contact [indicated by a the “x” in Fig. 1(b)].

2.3.

Statistical Analysis

For each DOSI parameter, descriptive statistics of measurements such as median and percentiles were calculated by measurement day and used to construct Figs. 4(a)–4(f). To evaluate whether the DOSI parameters changed significantly in time, we analyzed the relative marginal effect for each DOSI parameter at each measurement day.²⁵ This nonparametric analysis is more commonly used to determine whether a single group of homogeneous subjects experiences a change in some measurable parameter as a function of time. In this case, each “subject” is regarded as a single-spatial point on the breast. Due to the small sample size and non-normality of the DOSI parameters, this nonparametric analysis of longitudinal data in factorial experiments with repeated measures assumes that all spatial points were independent. Within each DOSI parameter, all measurements were ranked ($R_{is}$), where $i$ denotes the spatial point and $s$ denotes the day. The mean of ranks over spatial points was calculated by measurement day ($\overline{R_{.s}}$). With $p_s$ representing the relative marginal effect at day $s$, the estimated relative marginal effect at day $s$ was calculated by ${\widehat{p}}_s=\frac{1}{N}\times (\overline{R_{.s}}-\frac{1}{2})$, where $N$ is the total number of measurements. A relative marginal effect ${\widehat{p}}_s<\frac{1}{2}$ means that the experimental results at day $s$ tend to take on smaller values compared to the results at all days. The smaller the value of $p_s$, the stronger is the tendency to smaller values and vice versa.²⁵ The estimated relative marginal effects and 95% confidence intervals were used to construct Figs. 4(g)–4(l). We hypothesized that there was a measurement day effect (i.e., at least one difference in the estimated relative marginal effects across measurement days). All of the aforementioned procedures were performed in parallel and separately for both the areola and nonareolar region.

4.1.

Study Limitations

Since some of the DOSI parameters are still significantly different between day 237 and the preceding measurement at day 104 (nonareolar region), we cannot draw any definite conclusions about whether the complete involution process has been captured with this study. Nevertheless, the median water, lipid, THb, and ${\mathrm{StO}}_2$ values in the nonareolar region at day 237 are comparable to those found in healthy, premenopausal breasts.¹⁴

Hormonal fluctuations during the menstrual cycle can cause significant fluctuations in the DOSI parameters, which may explain some of the variability in the DOSI parameters versus time in Fig. 4.^31,32 The probing depth by our DOSI method inside breast tissue is $\sim 10$ to 15 mm, so a limitation of this approach is a lack of sensitivity to deeper situated tissue structures. Due to the decrease in tissue absorption during the involution process, penetration depth increases 1 to 2 mm between day 0 and day 237. As a consequence, DOSI becomes slightly more sensitive to the deeper situated tissue structures over time, but we argue that this will only be of marginal influence on the observed relative changes in tissue composition.

The nonparametric model we chose to analyze the longitudinal data requires that data collected at separate spatial points are independent. Though each position represents different tissue volumes, there is some overlap in the interrogation regions for adjacent grid points. Furthermore, there will be some interdependency due to all of the data coming from the same subject. Taking this limitation into account, as well as the fact that this is a case study, these results should not be generalized to a larger population.

Just as breast growth during pregnancy varies substantially among women,³⁰ the involution process is likely to depend on individual characteristics. In this case study, two unique characteristics of the participant were the mastectomy of the contralateral breast and the palpable duct in the investigated breast, in which the contrast disappeared gradually along with the involution of the rest of the breast. Other factors of influence on mammary involution may be the total lactation period before cessation, as well as weaning behavior, i.e., abrupt cessation of breastfeeding, versus gradual replacement by supplemental foods.¹⁹ Although the optical properties of the breast do not seem to significantly correlate with age for premenopausal women,^29,33 this factor may be of influence on the dynamics of tissue composition during the involution process. Future research with DOSI on mammary involution will enable to investigate the complete range of tissue composition changes and their dependency on these factors of influence.

4.2.

Significance

This case study demonstrates the potential of DOSI to study lactation physiology by mapping the spatiotemporal changes in tissue composition during mammary involution. Because of the safe levels of optical power and the absence of contrast agents, DOSI is an excellent technique for repeated measurements without introducing any health risks. Our data are unique in extent of investigated tissue components (water, lipid, THb, ${\mathrm{StO}}_2$, scattering power, and scattering prefactor), as well as the temporal nature of our measurements. Although Nissan et al.¹⁰ have carefully studied in vivo fibroglandular tissue changes during mammary involution with MRI, their study only compared mammary tissue composition in the lactating state to the postweaning state. As a consequence, it did not provide any information on the rate of changes in tissue composition.

Knowledge of mammary tissue composition during involution, and also during all other stages of pregnancy and lactation, is highly relevant for breast cancer diagnosis and treatment during these phases.^7–9,16 The sensitivity of DOSI to changes in THb concentrations and ${\mathrm{StO}}_2$ levels also offers unique possibilities to study mammary hemodynamics, vascular remodeling, and its relation to milk synthesis, which is a largely unexplored aspect of lactation.³⁴ Furthermore, the future study of tissue composition changes during mammary involution itself may provide important insights into its relation to pregnancy-associated breast cancer, because an imbalance in the cell-death pathways that regulate mammary involution can promote mammary tumor genesis.^35,36