Mammographic casting-type calcification is an independent prognostic factor in invasive breast cancer

Differentiation of histological calcification classifications in breast cancer using ultrashort echo time and chemical shift-encoded imaging MRI

Introduction

Ductal carcinoma in situ (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in de novo synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial ex vivo step towards in vivo application.

Methods

Twenty female patients, with mean age (range) of 57 (35-78) years, participated in the study. Intra- and peri-tumoural degree of calcification and peri-tumoural lipid composition were acquired on MRI using UTE and CSEI, respectively. Ex vivo imaging was conducted on the freshly excised breast tumour specimens immediately after surgery. Histopathological analysis was conducted to determine the calcification status, Nottingham Prognostic Index (NPI), and proliferative activity marker Ki-67.

Results

Intra-tumoural degree of calcification in malignant classification (1.05 ± 0.13) was significantly higher (p = 0.012) against no calcification classification (0.84 ± 0.09). Peri-tumoural degree of calcification in malignant classification (1.64 ± 0.10) was significantly higher (p = 0.033) against no calcification classification (1.41 ± 0.18). Peri-tumoural MUFA in malignant classification (0.40 ± 0.01) was significantly higher (p = 0.039) against no calcification classification (0.38 ± 0.02). Ki-67 showed significant negative correlation against peri-tumoural MUFA (p = 0.043, ρ = -0.457), significant positive correlation against SFA (p = 0.008, ρ = 0.577), and significant negative correlation against PUFA (p = 0.002, ρ = -0.653).

Conclusion

The intra- and peri-tumoural degree of calcification and peri-tumoural MUFA are sensitive to histological calcification classes supporting future investigation into DCIS prognosis.

Nomogram based on multimodal ultrasound features for evaluating breast nonmass lesions: a single center study

BACKGROUND

It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate their combined diagnostic performance.

METHODS

This retrospective analysis was conducted on 573 breast NMLs, including 309 were benign and 264 were malignant, their multimodal ultrasound features (B-mode, color Doppler and strain elastography) were assessed by two experienced radiologists. Univariate and multivariate logistic regression analysises were used to explore multimodal ultrasound features associated with malignancy, and a nomogram was developed. Diagnostic performance and clinical utility were evaluated and validated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve in the training and validation cohorts.

RESULTS

Multimodal ultrasound features including linear (odds ratio [OR] = 4.69) or segmental distribution (OR = 7.67), posterior shadowing (OR = 3.14), calcification (OR = 7.40), hypovascularity (OR = 0.38), elasticity scored 4 (OR = 7.00) and 5 (OR = 15.77) were independent factors associated with malignant breast NMLs. The nomogram based on these features exhibited diagnostic performance in the training and validation cohorts were comparable to that of experienced radiologists, with superior specificity (89.4%, 89.5% vs. 81.2%) and positive predictive value (PPV) (89.2%, 90.4% vs. 82.4%). The nomogram also demonstrated good calibration in both training and validation cohorts (all P > 0.05). Decision curve analysis indicated that interventions guided by the nomogram would be beneficial across a wide range of threshold probabilities (0.05-1 in the training cohort and 0.05-0.93 in the validation cohort).

CONCLUSIONS

The combined use of linear or segmental distribution, posterior shadowing, calcification, hypervascularity and high elasticity score, displayed as a nomogram, demonstrated satisfied diagnostic performance for malignant breast NMLs, which may contribute to the imaging interpretation and clinical management of tumors.

Initial interpretation scores of screening mammograms and cancer detection in BreastScreen Norway

PURPOSE

To explore the association between radiologists' interpretation scores, early performance measures and cumulative reading volume in mammographic screening.

METHOD

We analyzed 1,689,731 screening examinations (3,379,462 breasts) from BreastScreen Norway 2012-2020, all breasts scored 1-5 by two independent radiologists. Score 1 was considered negative/benign and score ≥2 positive in this scoring system. We performed descriptive analyses of recall, screen-detected cancer, positive predictive value (PPV) 1, mammographic features and histopathological characteristics by breast-based interpretation scores, and cumulative reading volume by examination-based interpretation scores.

RESULTS

Counting breasts and not women, 3.9 % (132,570/3,379,462) had a score of ≥2 by one or both radiologists. Of these, 84.8 % (112,440/132,570) were given a maximum score 2. Total recall rate was 1.6 % (53,735/3,379,462), 69.3 % (37,220/53,735) given maximum score 2. Among the 0.3 % (9733/3,379,462) diagnosed with screen-detected cancer, 34.6 % (3369/9733) had maximum score 3. The percentages of recall, screen-detected cancer and PPV-1 increased by increasing the sum of scores assigned by two radiologists (p < 0.001 for trend). Higher proportions of masses were observed among recalls and screen-detected cancers with low scores, and higher proportions of spiculated masses were observed for high scores (p < 0.001). Proportions of invasive carcinoma, histological grade 3 and lymph node positive tumors were higher for high versus low scores (p < 0.001). The proportion of examinations scored 1 increased by cumulative reading volume.

CONCLUSIONS

We observed higher rates of recall and screen-detected cancer and less favorable histopathological tumor characteristics for high versus low interpretation scores. However, a considerable number of recalls and screen-detected cancers had low interpretation scores.

Nomograms for predicting recurrence of HER2-positive breast cancer with different HR status based on ultrasound and clinicopathological characteristics

PURPOSE

This study aimed to identify ultrasound and clinicopathological characteristics related to recurrence in HER2-positive (HER2+) breast cancer, and to develop nomograms for predicting recurrence.

METHODS

In this dual-center study, we retrospectively enrolled 570 patients with HER2+ breast cancer. The ultrasound and clinicopathological characteristics of hormone receptor (HR)-/HER2+ patients and HR+/HER2+ patients were analyzed separately according to HR status. Eighty percent of the original samples from HR-/HER2+ and HR+/HER2+ patients were extracted by bootstrap sampling as the training cohorts, while the remaining 20% were used as the external validation cohorts. Informative characteristics were screened through univariate and multivariable Cox regression in the training cohorts and used to develop nomograms for predicting recurrence. The predictive accuracy was calculated using Harrell's C-index and calibration curves.

RESULTS

Three informative characteristics (axillary nodal status, calcification, and Adler degree) were identified in HR-/HER2+ patients, and another three (histological grade, axillary nodal status, and echogenic halo) in HR+/HER2+ patients. Based on these, two separate nomograms were constructed to assess recurrence risk. In the training cohorts, the C-index was 0.740 (95% CI: 0.667-0.811) for HR-/HER2+ nomogram, and 0.749 (95% CI: 0.679-0.820) for HR+/HER2+ nomogram. In the validation cohorts, the C-index was 0.708 (95% CI: 0.540-0.877) for HR-/HER2+ group, and 0.705 (95% CI: 0.557-0.853) for HR+/HER2+ group. The calibration curves also indicated the excellent accuracy of the nomograms.

CONCLUSIONS

Ultrasound performance of HER2+ breast cancers with different HR status was significantly different. Nomograms integrating ultrasound and clinicopathological characteristics exhibited favorable performance and have the potential to serve as a reliable method for predicting recurrence in heterogeneous breast cancer.

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.

Clinicopathological characteristics and prognostic significance of casting-type calcifications in patients with invasive breast cancer presenting with microcalcification

To explore the clinicopathological characteristics and prognostic significance of casting-type calcification (CC) in patients with breast cancer presenting with microcalcification on mammography. Data on patients with invasive breast cancer who had mammographic calcification was retrospectively analyzed. The chi-square test was utilized to assess the clinicopathological characteristics of two forms of CC-related breast cancer. The examination of prognostic variables was conducted using Kaplan-Meier and Cox regression analyses. A total of 427 eligible patients were included in this study. Chi-square analysis indicated that the presence of CC was associated with estrogen receptor (ER) negativity (P = 0.005), progesterone receptor (PR) negativity (P < 0.001), and epidermal growth factor receptor 2 (HER-2) positivity (P < 0.001); among these, the association was stronger with the CC-predominant type. After a median follow-up of 82 months, those with CC had a worse 5-year recurrence-free survival (RFS) (77.1% vs. 86.9%, p = 0.036; hazard ratio [HR], 1.86; 95% confidence interval [CI] 1.04-3.31) and overall survival (OS) (84.0% vs. 94.4%, p = 0.007; HR, 2.99; 95% CI 1.34-6.65) rates. In COX regression analysis, such differences were still observed in HER-2 positive subgroups (RFS: HR: 2.45, 95% CI 1-5.97, P = 0.049; OS: HR: 4.53, 95% CI 1.17-17.52, P = 0.029). In patients with invasive breast cancer exhibiting calcifications on mammography, the presence of CC, especially the CC-predominant type, is linked to a higher frequency of hormone receptor negativity and HER-2 positivity. The presence of CC is associated with an unfavorable 5-year RFS and OS rates.

Mammographic features and risk of breast cancer death among women with invasive screen-detected cancer in BreastScreen Norway 1996-2020

OBJECTIVES

We explored associations between mammographic features and risk of breast cancer death among women with small (<15 mm) and large (≥15 mm) invasive screen-detected breast cancer.

METHODS

We included data from 17,614 women diagnosed with invasive breast cancer as a result of participation in BreastScreen Norway, 1996-2020. Data on mammographic features (mass, spiculated mass, architectural distortion, asymmetric density, density with calcification and calcification alone), tumour diameter and cause of death was obtained from the Cancer Registry of Norway. Cox regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for breast cancer death by mammographic features using spiculated mass as reference, adjusting for age, tumour diameter and lymph node status. All analyses were dichotomised by tumour diameter (small versus large).

RESULTS

Mean age at diagnosis was 60.8 (standard deviation, SD=5.8) for 10,160 women with small tumours and 60.0 (SD=5.8) years for 7454 women with large tumours. The number of breast cancer deaths was 299 and 634, respectively. Mean time from diagnosis to death was 8.7 (SD=5.0) years for women with small tumours and 7.2 (4.6) years for women with large tumours. Using spiculated mass as reference, adjusted HR for breast cancer death among women with small tumours was 2.48 (95% CI 1.67-3.68) for calcification alone, while HR for women with large tumours was 1.30 (95% CI 1.02-1.66) for density with calcification.

CONCLUSIONS

Small screen-detected invasive cancers presenting as calcification and large screen-detected cancers presenting as density with calcification were associated with the highest risk of breast cancer death.

CLINICAL RELEVANCE STATEMENT

Small tumours (<15 mm) presented as calcification alone and large tumours (≥ 15 mm) presented as density with calcification were associated with the highest risk of breast cancer death among women with screen-detected invasive breast cancer diagnosed 1996-2020.

KEY POINTS

• Women diagnosed with invasive screen-detected breast cancer 1996-2020 were analysed. • Small screen-detected cancers presenting as calcification alone resulted in the highest risk of breast cancer death. • Large screen-detected cancers presenting as density with calcification resulted in the highest risk of breast cancer death.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study

BACKGROUND

Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved.

MATERIALS AND METHODS

Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model.

RESULTS

In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group).

CONCLUSION

DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer

Background

Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors.

Methods

This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size.

Results

There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097).

Conclusions

MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

New insights into breast microcalcification for poor prognosis: NACT cohort and bone metastasis evaluation cohort

OBJECTIVES

The study aimed to analyze the poor prognosis of microcalcification in breast cancer (BC), including the pathological complete response (pCR) to neoadjuvant chemotherapy (NACT) and the risk of bone metastases.

MATERIALS AND METHODS

313 breast cancer patients received NACT to evaluate pCR and 1182 patients from a multicenter database to assess bone metastases were retrospectively included. Two groups were divided according to the presence or absence of mammography microcalcification. Clinical data, image characteristics, neoadjuvant treatment response, bone involvement, and follow-up information were recorded. The pCR and bone metastases were compared between subgroups using the Mann-Whitney and χ2 tests and logistic regression, respectively.

RESULTS

Mammographic microcalcification was associated with a lower pCR than uncalcified BC in the NACT cohort (20.6% vs 31.6%, P = 0.029). Univariate and multivariate analysis suggested that calcification was a risk factor for poor NACT response [OR = 1.780, 95%CI (1.065-2.974), P = 0.028], [OR = 2.352, 95%CI (1.186-4.667), P = 0.014]. Microcalcification was more likely to be necrosis on MRI than those without microcalcification (53.0% vs 31.7%, P < 0.001), multivariate analysis indicated that tumor necrosis was also a risk factor for poor NACT response [OR = 2.325, 95%CI (1.100-4.911), P = 0.027]. Age, menopausal status, breast density, mass, molecular, and pathology type were not significantly associated with non-pCR risk assessment. In a multicenter cohort of 1182 patients with pathologically confirmed BC, those with microcalcifications had a higher proportion of bone metastases compared to non-calcified BC (11.6% vs 4.9%, P < 0.001). Univariate and multivariate analysis showed that microcalcification was an independent risk factor for bone metastasis [OR = 2.550, 95%CI (1.620-4.012), P < 0.001], [OR = 2.268(1.263-4.071), P = 0.006)]. Osteolytic bone metastases predominated but there was no statistical difference between the two groups (78.9% vs 60.7%, P = 0.099). Calcified BC was mainly involved in axial bone, but was more likely to involve the whole-body bone than non-calcified BC (33.8% vs 10.7%, P = 0.021).

CONCLUSION

This study provides important insights into the poor prognosis of microcalcification, not only in terms of poor response to NACT but also the risk factor of bone metastases.

Multifocal and diffusely infiltrating breast cancers are highly fatal subgroups needing further improvement in diagnostic and therapeutic strategies

Physicians treating breast cancer patients often wonder why this dreaded disease is still fatal in some women despite our best diagnostic and therapeutic efforts. Our own studies on prospectively documented cases spanning several decades have given us new insights for approaching this problem. By using imaging biomarkers to classify breast cancer subtypes according to their apparent site of origin, we found that a majority of breast cancer deaths (71%) occur in a minority of breast cancers (45%). Breast cancer deaths are significantly more likely to occur in women with multifocal acinar adenocarcinoma of the breast, AAB (13.1%), diffusely invasive breast cancers of ductal origin, DAB (24 %) and breast malignancies of mesenchymal hybrid cell origin, BCMO (33.7%) compared with women having unifocal invasive breast cancers (6.1%). Preventing more of these fatal events will require a re-evaluation of the current imperfect histopathologic terminology of breast cancer with special attention to the diffuse breast cancer subtypes, intensification of multimodality imaging and multidisciplinary management, as well as application of image guided large format histopathology.

The Prospects of Using Structural Phase Analysis of Microcalcifications in Breast Cancer Diagnostics

The detection of microcalcifications in the breast by mammography is of great importance for the early diagnostics of breast cancer. This study aimed to establish the basic morphological and crystal-chemical properties of microscopic calcifications and their impact on breast cancer tissue. During the retrospective study, 55 out of 469 breast cancer samples had microcalcifications. The expression of the estrogen and progesterone receptors and Her2-neu showed no significant difference from the non-calcified samples. An in-depth study of 60 tumor samples revealed a higher expression of osteopontin in the calcified breast cancer samples (p ˂ 0.01). The mineral deposits had a hydroxyapatite composition. Within the group of calcified breast cancer samples, we detected six cases of colocalization of oxalate microcalcifications together with biominerals of the usual "hydroxyapatite" phase composition. The simultaneous presence of calcium oxalate and hydroxyapatite was accompanied by a different spatial localization of microcalcifications. Thus, the phase compositions of microcalcifications could not be used as criteria for the differential diagnostics of breast tumors.

Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

BACKGROUND

The aim of this study was to develop a conventional ultrasound (US) features-based nomogram for the prediction of malignant nonmasslike (NML) breast lesions.

METHODS

Consecutive cases of adult females diagnosed with NML breast lesions via US screening in our center from June 1st, 2017, to April 17th, 2020, were retrospectively enrolled. Candidate variables included age, clinical symptoms, and the image features obtained from the conventional US. Nomograms were developed based on the results of the multiple logistic regression analysis via R language. One thousand bootstraps were used for internal validation. The area under the curve (AUC) and the bias-corrected concordance index (C-index) were calculated. Decision curve analysis (DCA) was also performed for further comparison between the nomogram and the Breast Imaging Reporting and Data System (BI-RADS). The study has not yet been registered.

RESULTS

A total of 229 patients were included in the study after exclusion and follow-up. The overall malignant rate of NML breast lesions was 31.0%. Age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification were selected to generate the nomogram according to the results of the multivariable logistic regression analysis. The bias-corrected C-index and the AUC of our nomogram were 0.790 and 0.828, respectively. The DCA showed that our model had larger net benefits in a range from 0.2 to 0.7 when compared with the BI-RADS.

CONCLUSIONS

We developed a prediction model using a combination of age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification for malignant NML breast lesion prediction that yielded adequate discrimination and calibration.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics

Objective

This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC).

Methods

Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set.

Results

A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts.

Conclusions

Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC.

Key Points

• The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC.

• Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC.

• The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08910-4.

Microcalcifications and Peritumoral Edema Predict Survival Outcome in Luminal Breast Cancer Treated with Neoadjuvant Chemotherapy

Background Little is known regarding findings at imaging associated with survival in patients with luminal breast cancer treated with neoadjuvant chemotherapy (NAC). Purpose To determine the relationship between imaging (MRI, US, and mammography) and clinical-pathologic variables in predicting distant metastasis-free survival (DMFS) and overall survival (OS) in patients with luminal breast cancer treated with NAC. Materials and Methods In this retrospective study, consecutive women with luminal breast cancer who underwent NAC followed by surgery were identified from the breast cancer registries of two hospitals. Women from one hospital between January 2003 and July 2015 were classified into the development cohort, and women from the other hospital between January 2007 and July 2015 were classified into the validation cohort. MRI scans, US scans, and mammograms before and after NAC (hereafter, referred to as pre- and post-NAC, respectively) and clinical-pathologic data were reviewed. Peritumoral edema was defined as the water-like high signal intensity surrounding the tumor on T2-weighted MRI scans. The prediction model was developed in the development cohort by using Cox regression and then tested in the validation cohort. Results The development cohort consisted of 318 women (68 distant metastases, 54 deaths) and the validation cohort consisted of 165 women (37 distant metastases, 14 deaths) (median age, 46 years in both cohorts). Post-NAC MRI peritumoral edema, age younger than 40 years, clinical N2 or N3, and lymphovascular invasion were associated with worse DMFS (all, P < .05). Pre-NAC mammographic microcalcifications, post-NAC MRI peritumoral edema, age older than 60 years, and clinical T3 or T4 were associated with worse OS (all, P < .05). The prediction model showed good discrimination ability (C index, 0.67-0.75 for DMFS and 0.70-0.77 for OS) and stratified prognosis into low-risk and high-risk groups (10-year DMFS rates, 79% vs 21%, respectively; and 10-year OS rates, 95%-96% vs 63%-67%, respectively) in the validation cohort. Conclusion MRI features and clinical-pathologic variables were identified that were associated with prolonged survival of patients with luminal breast cancer treated with neoadjuvant chemotherapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka in this issue.

A retrospective review of MRI features associated with metastasis-free survival in women with breast cancer: focusing on skin thickening and skin enhancement

OBJECTIVES

To identify associations between MRI-detected skin thickening and enhancement and metastasis-free survival (MFS) given recent reports of skin thickening on ultrasound being a poorer prognostic indicator.

METHODS

Interrogation of a prospectively collected database of ultrasound-visible breast lesions showed 214 lesions with pre-treatment MRIs available for analysis in a single centre. Data on MFS was prospectively collected. Retrospective MRI review was performed blinded to outcome. Imaging factors recorded were presence of skin thickening and enhancement, non-mass-enhancement (NME) and abnormal nodes, mass characteristics, perilesional oedema and background parenchymal enhancement. Statistical analysis used chi-squared test, Kaplan-Meier survival curves, the Log-rank test and receiver-operator characteristic (ROC) curves.

RESULTS

During a median follow-up period of 5.6 years, 21 (10%) of 212 patients developed distant metastases. Skin thickening [24 of 30 (80%) vs 169 of 184 (92%), p = 0.043] and skin enhancement [15 of 20 (75%) vs 178 of 194 (92%), p = 0.016] were associated with poorer MFS. Large index lesion size [p < 0.001, AUC 0.823], large sum of masses [p < 0.001, AUC 0.813], increasing total lesion extent including NME [p < 0.001, AUC 0.749] and abnormal axillary nodes [55 of 66 (83%) vs 138 of 148 (93%), p = 0.024] were also associated with poorer MFS.

CONCLUSIONS

Skin thickening and enhancement on breast MRI are associated with poorer MFS. These findings should be taken into account when managing patients with invasive breast cancer.

ADVANCES IN KNOWLEDGE

Skin enhancement on breast MRI at diagnosis is associated with metastases development. Skin thickening on breast MRI is associated with future metastatic disease.

Mammographic features and screening outcome in a randomized controlled trial comparing digital breast tomosynthesis and digital mammography

PURPOSE

To compare the distribution of mammographic features among women recalled for further assessment after screening with digital breast tomosynthesis (DBT) versus digital mammography (DM), and to assess associations between features and final outcome of the screening, including immunohistochemical subtypes of the tumour.

METHODS

This randomized controlled trial was performed in Bergen, Norway, and included 28,749 women, of which 1015 were recalled due to mammographic findings. Mammographic features were classified according to a modified BI-RADS-scale. The distribution were compared using 95 % confidence intervals (CI).

RESULTS

Asymmetry was the most common feature of all recalls, 24.3 % (108/444) for DBT and 38.9 % (222/571) for DM. Spiculated mass was most common for breast cancer after screening with DBT (36.8 %, 35/95, 95 %CI: 27.2-47.4) while calcifications (23.0 %, 20/87, 95 %CI: 14.6-33.2) was the most frequent after DM. Among women screened with DBT, 0.13 % (95 %CI: 0.08-0.21) had benign outcome after recall due to indistinct mass while the percentage was 0.28 % (95 %CI: 0.20-0.38) for DM. The distributions were 0.70 % (95 %CI: 0.57-0.85) versus 1.46 % (95 %CI: 1.27-1.67) for asymmetry and 0.24 % (95 %CI: 0.16-0.33) versus 0.54 % (95 %CI: 0.43-0.68) for obscured mass, among women screened with DBT versus DM, respectively. Spiculated mass was the most common feature among women diagnosed with non-luminal A-like cancer after DBT and after DM.

CONCLUSIONS

Spiculated mass was the dominant feature for breast cancer among women screened with DBT while calcifications was the most frequent feature for DM. Further studies exploring the clinical relevance of mammographic features visible particularly on DBT are warranted.

The clinical value of detecting microcalcifications on a mammogram

Many breast lesions are associated with microcalcifications that are detectable by mammography. In most cases, radiologists are able to distinguish calcifications usually associated with benign diseases from those associated with malignancy. In addition to their value in the early detection of breast carcinoma and accurate radiological diagnosis, the presence of microcalcifications often affects the extent of surgical intervention. Certain types of microcalcifications are associated with negative genetic and molecular characteristics of the tumor and unfavorable prognosis. Microcalcifications localized in the larger ducts (duct-centric, casting-type microcalcifications) represent an independent negative prognostic marker compared to lesions containing other types of microcalcifications and to non-calcified lesions. In this review, we summarize the theoretical and methodological background for understanding the clinical impact and discuss the diagnostic and prognostic value of microcalcifications detected in the breast by mammography.