Mammographic tumour appearance is related to clinicopathological factors and surrogate molecular breast cancer subtype

Mammographic features differ with body composition in women with breast cancer

OBJECTIVES

There are several breast cancer (BC) risk factors-many related to body composition, hormonal status, and fertility patterns. However, it is not known if risk factors in healthy women are associated with specific mammographic features at the time of BC diagnosis. Our aim was to assess the potential association between pre-diagnostic body composition and mammographic features in the diagnostic BC image.

MATERIALS AND METHODS

The prospective Malmö Diet and Cancer Study includes women with invasive BC from 1991 to 2014 (n = 1116). BC risk factors at baseline were registered (anthropometric measures, menopausal status, and parity) along with mammography data from BC diagnosis (breast density, mammographic tumor appearance, and mode of detection). We investigated associations between anthropometric measures and mammographic features via logistic regression analyses, yielding odds ratios (OR) with 95% confidence intervals (CI).

RESULTS

There was an association between high body mass index (BMI) (≥ 30) at baseline and spiculated tumor appearance (OR 1.370 (95% CI: 0.941-2.010)), primarily in women with clinically detected cancers (OR 2.240 (95% CI: 1.280-3.940)), and in postmenopausal women (OR 1.580 (95% CI: 1.030-2.440)). Furthermore, an inverse association between high BMI (≥ 30) and high breast density (OR 0.270 (95% CI: 0.166-0.438)) was found.

CONCLUSION

This study demonstrated an association between obesity and a spiculated mass on mammography-especially in women with clinically detected cancers and in postmenopausal women. These findings offer insights on the relationship between risk factors in healthy women and related mammographic features in subsequent BC.

CLINICAL RELEVANCE STATEMENT

With increasing numbers of both BC incidence and women with obesity, it is important to highlight mammographic findings in women with an unhealthy weight.

KEY POINTS

Women with obesity and BC may present with certain mammographic features. Spiculated masses were more common in women with obesity, especially postmenopausal women, and those with clinically detected BCs. Insights on the relationship between obesity and related mammographic features will aid mammographic interpretation.

Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer

BACKGROUND

The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer.

OBJECTIVES

To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images.

MATERIALS AND METHODS

Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model.

RESULTS

In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views.

CONCLUSIONS

The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.

Mammographic features at primary breast cancer diagnosis in relation to recurrence-free survival

PURPOSE

The number of women living with breast cancer (BC) is increasing, and the efficacy of surveillance programs after BC treatment is essential. Identification of links between mammographic features and recurrence could help design follow up strategies, which may lead to earlier detection of recurrence. The aim of this study was to analyze associations between mammographic features at diagnosis and their potential association with recurrence-free survival (RFS).

METHODS

Women with invasive BC in the prospective Malmö Diet and Cancer Study (n = 1116, 1991-2014) were assessed for locoregional and distant recurrences, with a median follow-up of 10.15 years. Of these, 34 women were excluded due to metastatic disease at diagnosis or missing recurrence data. Mammographic features (breast density [BI-RADS and clinical routine], tumor appearance, mode of detection) and tumor characteristics (tumor size, axillary lymph node involvement, histological grade) at diagnosis were registered. Associations were analyzed using Cox regression, yielding hazard ratios (HR) with 95 % confidence intervals (CI).

RESULTS

Of the 1082 women, 265 (24.4 %) had recurrent disease. There was an association between high mammographic breast density at diagnosis and impaired RFS (adjusted HR 1.32 (0.98-1.79). In analyses limited to screen-detected BC, this association was stronger (adjusted HR 2.12 (1.35-3.32). There was no association between mammographic tumor appearance and recurrence.

CONCLUSION

RFS was impaired in women with high breast density compared to those with low density, especially among women with screen-detected BC. This study may lead to insights on mammographic features preceding BC recurrence, which could be used to tailor follow up strategies.

Investigating the prognostic value of mammographic breast density and mammographic tumor appearance in women with invasive breast cancer: The Malmö Diet and cancer study

BACKGROUND

High breast density is a risk factor for breast cancer. However, whether density is a prognostic factor is debatable. Also, tumor appearances are related to tumor characteristics. Here we investigate the relationship between breast cancer-specific survival and mammographic breast density and mammographic tumor appearances.

METHODS

Women in the Malmö Diet and Cancer study with invasive breast cancer 1991-2014 were included (n = 1116). Mammographic information, patient and tumor characteristics, vital status, and causes of death were collected through 2018. Breast cancer-specific survival was assessed with Kaplan-Meier estimates and Cox proportional hazard models. Analyses were adjusted for established prognostic factors and stratified by detection mode.

RESULTS

High breast density did not significantly impact breast cancer-specific survival. However, there may be increased risk in women with dense breasts and screening-detected tumors (HR 1.45, CI 0.87-2.43). Neither did tumor appearance impact breast cancer-specific survival at long-term follow-up.

CONCLUSIONS

Breast cancer prognosis in women with high breast density on mammography does not seem impaired compared to women with less dense breasts, once the cancer is established. Neither does mammographic tumor appearance seem to inflict on prognosis, findings that can be of value in the management of breast cancer.

Secondary Breast Malignancy from Renal Cell Carcinoma: Challenges in Diagnosis and Treatment-Case Report

Renal cell carcinoma represents about 2% of all malignant tumours in adults. Metastases of the primary tumour in the breast make up to about 0.5-2% of the cases. Renal cell carcinoma metastases in the breast are extremely rare and have been sporadically recorded in the literature. In this paper, we present the case of a patient with breast metastasis of renal cell carcinoma 11 years after primary treatment. Case presentation: An 82-year-old female who had right nephrectomy due to renal cancer in 2010 felt a lump in her right breast in August 2021, whereby a clinical examination revealed a tumour at the junction of the upper quadrants of her right breast, about 2 cm, movable toward the base, vaguely limited, and with a rough surface. The axillae were without palpable lymph nodes. Mammography showed a circular and relatively clearly contoured lesion in the right breast. Ultrasound showed an oval lobulated lesion of 19 × 18 mm at the upper quadrants, with strong vascularisation and without posterior acoustic phenomena. A core needle biopsy was performed, and the histopathological findings and obtained immunophenotype indicated a metastatic clear cell carcinoma of renal origin. A metastasectomy was performed. Histopathologically, the tumour was without desmoplastic stroma, comprising predominantly solid-type alveolar arrangements of large moderately polymorphic cells, bright and abundant cytoplasm, and round vesicular cores with focally prominent nuclei. Immunohistochemically, tumour cells were diffusely positive for CD10, EMA, and vimentin, and negative for CK7, TTF-1, renal cell antigen, and E-cadherin. With a normal postoperative course, the patient was discharged on the third postoperative day. After 17 months, there were no new signs of the underlying disease spreading at regular follow-ups. Conclusion: Metastatic involvement of the breast is relatively rare and should be suspected in patients with a prior history of other cancers. Core needle biopsy and pathohistological analysis are required for the diagnosis of breast tumours.

Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer

BACKGROUND

Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2.

METHODS

We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity.

RESULTS

The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p < 0.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p < 0.05). The area under receiver operating characteristic curve, accuracy, sensitivity, specificity of the model to predict human epidermal growth factor receptor2+ subtype breast cancer was 0.697, 60.14%, 72.46%, 58.49% and 0.725, 72.06%, 64.71%, 72.89% in the training and test sets, respectively.

CONCLUSIONS

Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.