Histopathology findings of non-mass cancers on breast ultrasound

Predicting Non-Mass Breast Cancer Utilizing Ultrasound and Molybdenum Target X-Ray Characteristics

Objective

The aim of this study is to investigate the influence of ultrasound and molybdenum target X-ray characteristics in predicting non-mass breast cancer.

Methods

A retrospective analysis was conducted on the clinical data of 185 patients presenting with non-mass breast lesions between September 2019 and 2021. The non-mass lesions were categorized into benign and malignant types based on ultrasonographic findings, which included lamellar hypoechoic, ductal alteration, microcalcification, and structural disorder types. Furthermore, an examination was undertaken to discern variances in molybdenum target X-ray parameters, ultrasonographic manifestations, and characteristics among individuals diagnosed with non-mass breast lesions.

Results

The ultrasonographic depiction of microcalcified lesions and the identification of suspicious malignancy through molybdenum target X-ray evaluation exhibited independent associations with non-mass breast cancer, yielding statistically significant differences (p < 0.05). Subsequently, the logistic regression model was formulated as follows: Logit (P) =-1.757+2.194* microcalcification type on ultrasound + 1.520* suspicious malignancy on molybdenum target X-ray evaluation. The respective areas under the receiver operating characteristic curves for microcalcification type on ultrasound, suspicious malignancy on molybdenum target X-ray, and the integrated diagnostic model were 0.733, 0.667, and 0.827, respectively, demonstrating discriminative capacities.

Conclusion

Using both ultrasound and molybdenum target X-ray diagnostics can increase the accuracy of non-mass breast cancer detection. The findings of this study have the potential to augment the detection rate of non-lumpy breast cancer and provide an imaging basis for enhancing the prognosis of individuals with breast cancer.

A conditional inference tree model for predicting cancer risk of non-mass lesions detected on breast ultrasound

OBJECTIVES

To generate and validate a prediction model based on imaging features for cancer risk of non-mass lesions (NMLs) detected on breast ultrasound (US).

METHODS

In this single-center study, consecutive women with 503 NMLs detected on breast US between 2012 and 2019 were retrospectively identified. The lesions were randomly assigned to the training or testing dataset with a 70/30 split. Age, symptoms, lesion size, and US features were collected. Multivariate analyses were employed to identify risk factors associated with malignancy. The predictive model was developed by using conditional inference trees (CTREE).

RESULTS

There were 498 patients (50.9 ± 13.29 years; range, 22-88 years) with 503 NMLs with histopathologic results or > 2-year follow-up, including 224 (44.5%) benign and 279 (55.5%) malignant lesions. At multivariate analysis, age (odds ratio (OR) = 1.08, 95% confidence interval (CI), 1.06-1.11, p < 0.001), NMLs with focal mass effect (OR = 3.03, 95% CI, 1.59-5.81, p = 0.001), indistinct glandular-fat interface (GFI) (OR = 4.23, 95% CI, 2.31-7.73, p < 0.001), geographic (OR = 3.47, 95% CI, 1.20-10.8, p = 0.022) and mottled (OR = 3.67, 95% CI, 1.32-10.21, p = 0.013) patterns, and calcifications (OR = 2.15, 95% CI, 1.16-4.01, p = 0.016) were associated with malignancy. The GFI status, architectural patterns, general morphology, and calcifications were consistently identified as the strongest US predictors of malignancy using CTREE analysis. Based on these factors, individuals were stratified into six risk groups. The predictive model showed an area under the curve of 0.797 in the testing dataset.

CONCLUSION

The CTREE model efficiently aids in interpreting and managing ultrasound-detected breast NMLs, overcoming BI-RADS limitations by refining cancer risk stratification.

CLINICAL RELEVANCE STATEMENT

The CTREE model allows for the reclassification of BI-RADS categories into subgroups with varying malignancy probabilities, thus providing a valuable enhancement to the BI-RADS assessment for the diagnosis of ultrasound-detected NMLs, with the potential to minimize unnecessary biopsies.

KEY POINTS

• The indistinct glandular-fat interface (GFI) status, NML with focal mass effect, geographic or mottled patterns, and calcifications are the strongest imaging predictors of malignant non-mass lesions (NMLs) detected on breast US. • A practical system has been created to categorize NMLs found in breast US; each classification is associated with a degree of diagnostic certainty. • The model may contribute to patient stratification by determining the relative likelihood of malignancy and thus support clinical decision-making and evidence-based management.

Histopathology of non-mass-like breast lesions on ultrasound

There have been several investigations of non-mass-like (NML) lesions on ultrasound (US) since Uematsu first described this approach, and it is a relatively new concept for breast examination. However, the results have varied, and there have been only a few studies related to the detailed histopathology of NML lesions on US. Here, we review the histopathology of NML lesions. NML lesions are pathologically benign, atypical, or malignant. There are two major findings of NML lesions on US: architectural distortion and calcifications. Architectural distortion pathologically indicates a fibrous change with ductal proliferation, invasive breast carcinoma, and carcinoma in situ. Histopathologically, microcalcifications are seen in both benign and malignant lesions, and it is important to distinguish between these lesions among NML lesions, particularly fibrocystic changes including adenosis and hyperplasia in the case of benign lesions and carcinoma in situ (ductal and lobular) in the case of malignant lesions. The differential major points may be whether NML lesions are associated with abundant hyperechoic foci, which indicate comedo necrosis on histology. They are usually high-grade carcinoma in situ that may be positive for HER2 or triple negativity. A recent report indicated that low-grade carcinoma in situ showed better survival than higher-grade carcinoma in situ, which is often accompanied by comedo necrosis on histology, reflecting visible microcalcification on US. NML lesions are considered to include a certain rate of low-grade carcinoma in situ. Therefore, more caution may be needed when detecting and managing NML lesions to avoid overdiagnosis and overtreatment as a result of this recent "low-risk ductal carcinoma in situ" concept.

Missed Breast Cancers on MRI in High-Risk Patients: A Retrospective Case–Control Study

Purpose: To determine if MRI features and molecular subtype influence the detectability of breast cancers on MRI in high-risk patients. Methods and Materials: Breast cancers in a high-risk population of 104 patients were diagnosed following MRI describing a BI-RADS 4–5 lesion. MRI characteristics at the time of diagnosis were compared with previous MRI, where a BI-RADS 1–2–3 lesion was described. Results: There were 77 false-negative MRIs. A total of 51 cancers were overlooked and 26 were misinterpreted. There was no association found between MRI characteristics, the receptor type and the frequency of missed cancers. The main factors for misinterpreted lesions were multiple breast lesions, prior biopsy/surgery and long-term stability. Lesions were mostly overlooked because of their small size and high background parenchymal enhancement. Among missed lesions, 50% of those with plateau kinetics on initial MRI changed for washout kinetics, and 65% of initially progressively enhancing lesions then showed plateau or washout kinetics. There were more basal-like tumours in BRCA1 carriers (50%) than in non-carriers (13%), p = 0.0001, OR = 6.714, 95% CI = [2.058–21.910]. The proportion of missed cancers was lower in BRCA carriers (59%) versus non-carriers (79%), p < 0.05, OR = 2.621, 95% CI = [1.02–6.74]. Conclusions: MRI characteristics or molecular subtype do not influence breast cancer detectability. Lesions in a post-surgical breast should be assessed with caution. Long-term stability does not rule out malignancy and multimodality evaluation is of added value. Lowering the biopsy threshold for lesions with an interval change in kinetics for a type 2 or 3 curve should be considered. There was a higher rate of interval cancers in BRCA 1 patients attributed to lesions more aggressive in nature.

The role of contrast-enhanced ultrasound in the diagnosis of malignant non-mass breast lesions and exploration of diagnostic criteria

OBJECTIVES

To assess the value of contrast-enhanced ultrasound (CEUS) for diagnosing malignant non-mass breast lesions (NMLs) and to explore the CEUS diagnostic criteria.

METHODS

A total of 116 patients with 119 NMLs detected by conventional US were enrolled. Histopathological results were used as the reference standard. The enhancement characteristics of NMLs in CEUS were compared between malignant and benign NMLs. The CEUS diagnostic criteria for malignant NMLs were established using independent diagnostic indicators identified by binary logistic regression analysis. The diagnostic performance of Breast Imaging Reporting and Data System-US (BI-RADS-US), CEUS, and BI-RADS-US combined with CEUS was evaluated and compared.

RESULTS

Histopathological results showed 63 and 56 benign and malignant NMLs. Enhancement degree (OR = 5.75, p = 0.003), enhancement area (OR = 4.25, p = 0.005), and radial or penetrating vessels (OR = 7.54, p = 0.003) were independent diagnostic indicators included to establish the CEUS diagnostic criteria. The sensitivity and specificity of BI-RADS-US, CEUS, and BI-RADS-US combined with CEUS were 100 and 30.2%, 80.4 and 74.6%, and 94.6 and 77.8%, respectively; the corresponding areas under the receiver operating characteristic curve (AUC) were 0.819, 0.775, and 0.885, respectively.

CONCLUSIONS

CEUS has a high specificity in malignant NML diagnosis based on the diagnostic criteria including enhancement degree, enhancement area, and radial or penetrating vessels, but with lower sensitivity than BI-RADS-US. The combination of CEUS and BI-RADS-US is an effective diagnostic tool with both high sensitivity and specificity for the diagnosis of malignant NMLs.

ADVANCES IN KNOWLEDGE

In this study, we assessed the diagnostic value of CEUS for malignant NMLs and constructed a feasible diagnostic criterion. We further revealed that the combination of CEUS and BI-RADS-US has a high diagnostic value for malignant NMLs.

Ultrasonographic features of ductal carcinoma in situ: analysis of 219 lesions

Background

The purpose of this paper is to clarify the ultrasonographic features and classification of ductal carcinoma in situ (DCIS), and to evaluate the ability of ultrasonography in the prediction of DCIS.

Methods

The clinical data, gray-scale ultrasound images and pathological results of 219 DCIS lesions that detected in 203 consecutive patients who underwent ultrasonography and surgery in our hospital from January 1, 2014 to December 31, 2019 were collected retrospectively. Ultrasonographic features and classification of DCIS were summarized, and the accuracy of ultrasonography in predicting different ultrasonographic findings of DCIS were compared.

Results

Among the 219 DCIS lesions, 91 (41.6%) presented as mass-like lesions and 128 (58.4%) were non-mass-like lesions. For the 91 mass-like DCIS lesions, 79 were hypoechoic solid masses, 12 were cystic-solid structures. For the 128 non-mass-like DCIS lesions, 114 were hypoechoic areas, 10 were ductal dilatation accompanied with intraductal solid components, and 4 were multiple punctate echogenic foci only. The diagnostic accuracy of ultrasound for the 219 DCIS lesions was 81.7% (179/219). The diagnostic accuracy of mass-like DCIS lesions was 90.1% (82/91), which was significantly higher than that in non-mass-like DCIS lesions [75.8% (97/128), P=0.007]. The diagnostic accuracy of hypoechoic solid masses was significantly higher than those of the other ultrasonographic findings (P=0.002). Ducts abnormalities were detected in 45 (20.5%) lesions and punctate echogenic foci in 134 (61.2%) lesions. The diagnostic accuracy of lesions with ducts abnormalities was 93.3% (42/45), which was significantly higher than that in lesions without ducts abnormalities [78.7% (137/174), P=0.024]. The diagnostic accuracy of lesions with punctate echogenic foci was 92.5% (124/134), which was significantly higher than that in lesions without punctate echogenic foci [64.7% (55/85), P=0.000].

Conclusions

DCIS lesions can effectively be recognized as mass-like lesions and non-mass-like lesions by ultrasound. Hypoechoic areas and hypoechoic solid masses were the most common ultrasonographic features of DCIS. Ducts abnormalities and punctate echogenic foci were helpful for the diagnosis of DCIS.

Predictive factors for the presence of invasive components in patients diagnosed with ductal carcinoma in situ based on preoperative biopsy

Background

In patients diagnosed with ductal carcinoma in situ (DCIS) with needle biopsy before surgery, invasive component (IC) is often found in the postoperative tissue, which results in altered post-surgical care. However, there are no clinically available factors to predict IC, and few MRI studies are available for the detection of IC in DCIS patients. The purpose of this study was to evaluate which risk factors can predict IC preoperatively.

Methods

Patients with a DCIS diagnosis based on preoperative biopsy, who underwent breast surgery Kangbuk Samsung Hospital between Jan 2005 and June 2018, were retrospectively evaluated. Clinico-pathological and breast MRI factors were compared between DCIS and DCIS with IC in postsurgical specimens.

Results

Of the 431 patients with a preoperative diagnosis of DCIS, 34 (7.9%) showed IC during the postoperative pathological investigations, and 217 (50.3%) underwent breast MRI. Among MRI-related factors, Mass-like enhancement on MRI was the sole but significant predictor of IC (HR = 0.26, C.I. = 0.07–0.93, p = 0.038), while nipple-areolar complex invasion, enhancement peak and pattern were not statistically significant. Nuclear grade was the only significant predictor of IC in the analysis of other clinico-pathological factors (HR = 2.39, C.I. = 1.05–5.42, p = 0.038 in univariate analysis, HR = 2.86, C.I. = 1.14–7.14, p = 0.025 in multivariate analysis).

Conclusions

Mass-like enhancement on MRI and high nuclear grade were associated with IC in patients with preoperative diagnosis of DCIS. Considering the high sensitivity of breast MRI for IC, further evaluation of the predictive value of MRI in preoperative DCIS patients is desirable.