Background
Ductal carcinoma in situ (DCIS) of the breast comprises a morphologically and biologically diverse group of cancerous lesions restricted to the breast ducts. The incidence of DCIS has seen a dramatic increase from 5.83 per 100,000 women in 1973 to 34.43 in 2014 [
1]. One of the major causes for this increase appears to be the increasing prevalence of breast screening mammography [
2], leading in turn to the discovery of these lesions at a much earlier time point. Approximately, 25% of all breast cancers in the USA are DCIS and 83% of all breast in situ cases diagnosed during 2010–2014 were DCIS, with the age- specific rate being highest in women between 65 and 75 years old (108.3 per 100,000 for 65–69 and 103.1 for 70–74) from 2010 to 2014 for carcinoma in situ [
1].
With an estimated 1 out of every 33 women in the USA expected to suffer from DCIS during her lifetime [
3], it becomes crucial to predict which of these women with DCIS might recur or progress to invasive breast cancer. Presently, the gold standard for treatment of DCIS is breast-conserving therapy, which includes a lumpectomy followed by adjuvant radiation therapy to remove the residual tumor. Hormonal therapy is also offered to patients with estrogen receptor (ER)-positive cancer. However, studies [
4,
5] have found that radiotherapy (RT) can often be omitted in low-risk DCIS by demonstrating the RT did not have significant additional benefits to those patients. Since RT is relatively expensive, time-consuming, and often carrying significantly deleterious side effects [
6], it is critical to identify DCIS patients with low recurrence risk to avoid the overtreatment.
Gene expression methods such as Oncotype DX (ODx) [
7] DCIS recurrence score have been validated in being able to identify those DCIS patients in whom post-lumpectomy RT can be safely omitted. The ODx DCIS score leverages a panel of 12 genes including seven genes purely predictive of recurrence risk along with five reference genes. The output of the ODx DCIS assay is a score, scaled between 0 and 100. Three risk categories are then defined according to the scaled score: (1) low-risk (< 39), (2) intermediate-risk (39–54), (3) high-risk (55–100). Women with a low ODx score have a lower risk of recurrence than those with a high ODx risk score and may derive a lesser benefit from adjuvant RT. However, ODx for DCIS is limited by its high cost, limited availability, and being tissue-destructive. In addition, although a study [
8] involving DCIS patients from multi-institutions has confirmed that ODx scores for risk stratification of DCIS patients provided valuable information to physicians and has effectively impacted treatment planning for DCIS patients, the actual prognostic meaning of the intermediate ODx risk category still remains unclear [
7]. In clinical practice, DCIS patients with intermediate ODx risk scores tend to be considered high risk of recurrence for the purpose of treatment planning [
9], which may potentially lead to an overtreatment.
Quantitative histomorphometry (QH) refers to the use of computerized methods and tools to quantitatively extract features of disease morphology from digitized images of tissue slides that may often be too subtle for visual discernment. QH enables an objective and reproducible measurement of the characteristics of the tumor at the sub-visual level, which is one of the ways to minimize the intra- and inter-observer variability that is often found in visual examination by pathologists [
10]. QH features have shown to be independently prognostic across different cancer types including breast [
11,
12], lung [
13,
14], and oral cancer [
15].
In this paper, we present a preliminary study to explore the potential role of quantitative nuclear histomorphometric features including nuclear shape, texture, and spatial arrangement from routine H&E-stained slide images of DCIS patients to distinguish between the high, intermediate, and low ODx DCIS risk categories. A total of N = 75 patients were retrospectively identified as having undergone surgical excision for DCIS and with a corresponding ODx DCIS score available. Using a combination of supervised classification and unsupervised clustering approaches, we sought to evaluate the ability of the features to discriminate between these DCIS patients with (1) high ODx vs. low ODx, (2) high ODx vs. intermediate ODx, (3) intermediate ODx vs. low ODx, (4) high and intermediate ODx vs. low ODx, and (5) high ODx vs. low and intermediate ODx risk categories. The prognostic value of the identified features was further evaluated on an independent validation set of 30 DCIS patients, in their abilities to distinguish the DCIS patients who progressed to invasive ductal carcinoma versus those who did not.
Discussion
Multiple clinical trials including E5194 [
4] and RTOG9804 [
5] have shown that low-risk DCIS patients tend to receive minimal benefit from adjuvant RT. There is a clear unmet need to identify those DCIS patients with a relatively low likelihood of recurrence or progression to avoid the side effect [
6] of unnecessary adjuvant RT for those patients. Oncotype DX (ODx) for DCIS is a gene expression-based assay to assess the recurrence risk of DCIS. While the ODx-derived risk category has been validated against the outcome on a cohort comprising 670 DCIS patients from ECOG E5194, it was not perfectly correlated [
7]. In addition, the ODx test for DCIS is expensive, tissue-destructive, and requires specialized facilities. Another issue with the ODx DCIS test is the lack of true prognostic meaning and significance associated with patients assigned to the intermediate-risk category [
7]. A lot of these intermediate ODx risk category patients may end up receiving adjuvant therapy and hence potentially be over-treated [
9].
In this paper, we identified a set of image features associated with the different ODx risk categories. Additionally, the prognostic ability of these image features to predict DCIS with progression to invasive cancer versus DCIS without recurrence/progression was evaluated on a small independent test set (n = 30) of women with DCIS. Additionally, we sought to elucidate the morphologic attributes of the Oncotype DCIS intermediate category, a risk category with somewhat ambivalent prognostic significance (unlike the low and high ODx risk categories).
According to the results based on the supervised classifiers and unsupervised clustering for distinguishing high ODx vs. low ODx and high ODx vs. intermediate ODx, high ODx risk category was found to be distinguishable from low and intermediate ODx risk categories in terms of nuclear histomorphometric features. The top features identified as being discriminating of high ODx from intermediate plus low ODx risk categories included (1) mean information measure 1 of correlation between neighbor nuclei orientations, which captures the information pertaining to the disorder in the polarity of the individual nuclei; (2) the ratio of the number of existed edges to all possible edges connecting the nodes in one local cell cluster, reflecting the spatial arrangement of nuclei in locally clustered nuclei neighborhood; (3) standard deviation of pixel-wise gray-level distribution across nuclei, in turn capturing the underlying chromatin or chromosome patterns in nuclei; and (4) the average number of neighbor nuclei within a 50-pixel radius around individual nuclei, in turn reflecting the global spatial arrangement of nuclei. The top feature has previously shown to be prognostic or diagnostic for a number of other solid tumors. For instance, Lu et al. [
11] found a significant association between the nuclei orientation disorder and overall survival in early stage estrogen receptor-positive (ER+) breast cancer. Nuclear polarity has also been implicated in the diagnosis and prognosis of urothelial [
30] and papillary thyroid cancers [
31]. The second feature, relating to spatial architecture of nuclei was found to over-express in high ODx patients compared to low and intermediate ODx patients. The patterns appear to suggest a more chaotic and disordered nuclear morphology in high ODx patients compared to the low and intermediate ODx patients. Interestingly, Whitney et al. [
12] showed that these features were also discriminating of early-stage ER+ invasive breast cancer patients corresponding to high and low DCIS risk category patients. Additionally, a textural pattern within the individual nuclei was also found to be discriminating between the high and intermediate-low ODx risk categories, possibly reflecting differences in chromatin patterns. Nuclear texture has been previously found to be discriminating of malignant and benign breast lesions on histopathology [
32]. Lu et al. [
11] similarly found that differences in nuclear texture heterogeneity were associated with the overall survival for invasive breast cancer patients. Finally, the feature reflecting nuclei global spatial distribution implies that a high versus intermediate and low ODx risk category patients tended to have differences in clustering of nuclei in the proximity of necrotic regions on the slide. These findings appear to be aligned with the findings by Lagios et al. [
33], which found that a higher concentration of necrosis was found to be associated with a higher risk of local recurrence for DCIS patients.
In experiment 3, we showed that the image features associated with ODx risk categories for DCIS were also found to independently distinguish between patients who progressed to invasive ductal carcinoma versus those who did not.
We envision two primary ways in which the image-based signature developed in this study might be used clinically. In developing countries or regions, where molecular-based assays like ODx test might not be easily affordable or even accessible for most of the DCIS patients, the imaging signature could potentially be employed as a surrogate of ODx test to prognosticate outcome, since the image-based assay is low-cost, non-tissue-destructive needing only digitized H&E slide images. Meanwhile, in developed countries, where molecular-based prognostic and predictive companion diagnostic tests exist, the image-based test could provide complementary morphologic cues to molecular and functional measurements of the tumor. The combination of computerized morphologic image attributes with an ODx risk score might help more accurately identify patients who could truly avoid adjuvant radiotherapy. This is in line with a recent study by Verma et al. [
34] in early-stage ER+ invasive breast cancer, where the combination of an image-based morphologic predictor with the ODx assay was able to identify an additional 20% more patients who were truly low risk and could be spared adjuvant chemotherapy. Additionally, integrating ODx with our image-based assay could also provide additional improved characterization and stratification of those patients currently identified as intermediate risk by ODx.
Additionally, we also sought to evaluate the relative similarity in quantitative nuclear histomorphometric features between the intermediate ODx compared to low and high ODx risk categories. A higher AUC and a lower BC feature was obtained when grouping intermediate ODx together with low ODx as opposed to high ODx for any of the combinations of feature ranking methods and classifiers. Additionally, via the unsupervised clustering, the intermediate ODx risk category was found to be separable from high ODx risk category but not separable from low ODx risk category.
Taken in tandem, these results appear to suggest the histomorphometric features for intermediate ODx risk category patients were more similar compared to low ODx risk category patients as opposed to the high ODx risk patients. This is consistent to several recent studies in the context of invasive breast cancer [
12,
35‐
37] that suggested that intermediate ODx risk category tumors appear to be more closely aligned with the low-risk tumors compared to the high ODx risk tumors. Kamal et al. [
35] found that, based on the evaluation of traditional cancer prognosis criteria such as tumor size and tumor grade, invasive breast cancer in the high ODx risk category could be identified, but the discrimination between low and intermediate ODx risk categories could hardly be found. Also, a phase 3 clinical trial, TAILORx [
36], concluded that for most patients with early-stage invasive breast cancer in intermediate ODx risk category, no benefit from receiving adjuvant chemotherapy could be observed in terms of overall survival as well as disease-free survival. In a study of the MammaPrint (MP) test (another widely used assay for invasive breast cancer), the study investigators found that among the patients in intermediate ODx risk category, most (65%) were identified as MP low-risk category [
37], which is a category indicating low risk of recurrence for invasive breast cancer.
Our findings in light of the related previous studies [
35‐
37] appear to suggest that intermediate ODx risk category patients appear to present very much like the low-risk patients and hence could possibly follow a similar management strategy.
This study did have some limitations. First, the sample size was too small to draw the definite conclusion that the intermediate ODx is comparable to low ODx risk category in terms of prognosis. Still, this study provides preliminary evidence that there is a quantifiable histomorphometric similarity between low and intermediate ODx risk category for DCIS. Although the impact of ODx test for DCIS on the clinical radiotherapy adoption had been confirmed by a study conducted by Manders et al. [
8], the mismatch between low or high ODx risk category with the actual cancer aggressiveness still exists [
7]. While we have independently evaluated the image signature associated with ODx risk categories to discriminate between patients who progressed to invasive ductal carcinoma as compared to those who did not in D2 (
n = 30), clearly a larger multi-site cohort of DCIS patients is needed for definitive validation. Thirdly, the patients and image data were originated from a single facility, failing to take account of the tissue slide variance arisen from the slide preparation process as well as the differing patient population characteristics.
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