Myositis Ossificans of the Breast Mimicking Breast Cancer: A Case Report.

Myositis ossificans (MO) is an uncommon tumor characterized by a rapidly growing mass following a history of local trauma. Few cases of MO affecting the breast have been reported, and some were misdiagnosed as primary osteosarcoma of the breast or metaplastic breast carcinoma. The following case report presents a patient with a growing breast lump whose core biopsy result was suspicious for breast cancer. MO was diagnosed after analysis of the mastectomy specimen. This case highlights the importance of MO as a differential diagnosis of a growing soft-tissue mass after trauma to avoid unnecessary overtreatment. Keywords: Myositis Ossificans, Osteosarcoma, Breast Cancer, Mastectomy, Heterotopic Ossification © RSNA, 2023.

Iodinated Contrast Enhancement of Breast Cancer on Prone Multidetector Computed Tomography-Preliminary Findings.

OBJECTIVES: Contrast-enhanced breast imaging has gained increasing importance in the diagnosis and management of breast cancer. The aim of this study was to assess breast cancer enhancement after contrast administration on prone multidetector computed tomography (MDCT). MATERIALS AND METHODS: This retrospective, unicentric, institutional review board-approved study included patients with newly diagnosed breast cancer who were submitted to contrast-enhanced MDCT in prone position, with image acquisition before and after nonionic iodinated contrast administration. RESULTS: Sixty breast cancer patients aged between 31 and 74 years (mean, 49 years) were included. Most patients (n = 50, 83.3%) had no special type invasive breast carcinoma and luminal subtype (n = 45, 75%). All index breast tumors were identified on prone MDCT. Forty-three cases (70.5%) presented as mass, 13 (21.3%) as nonmass enhancement and 4 (6.6%) as both mass and nonmass enhancement. Mean tumor density was 37.8 HU and 87.9 HU on precontrast and postcontrast images, respectively. Mean contrast enhancement was 50.2 HU (range, 20-109 HU). There were no statistically significant differences in tumor enhancement according to histological type, molecular subtype, nuclear grade, tumor size, or imaging presentation. CONCLUSIONS: Our results show that breast cancer usually can be identified and have significant contrast enhancement on prone MDCT images. This method could be used as an alternative when other contrast-enhanced breast imaging methods are not available.

Evaluation of pretreatment ADC values as predictors of treatment response to neoadjuvant chemotherapy in patients with breast cancer - a multicenter study.

BACKGROUND: Magnetic resonance imaging (MRI) can be used to diagnose breast cancer. Diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can reflect tumor microstructure in a non-invasive manner. The correct prediction of response of neoadjuvant chemotherapy (NAC) is crucial for clinical routine. Our aim was to compare ADC values between patients with pathological complete response (pCR) and non-responders based upon a multi-center design to improve the correct patient selection, which patient would more benefit from NAC and which patient would not. METHODS: For this study, data from 4 centers (from Japan, Brazil, Spain and United Kingdom) were retrospectively acquired. The time period was overall 2003-2019. The patient sample comprises 250 patients (all female; median age, 50.5). In every case, pretreatment breast MRI with DWI was performed. pCR was assessed by experienced pathologists in every center using the surgical specimen in the clinical routine work up. pCR was defined as no residual invasive disease in either breast or axillary lymph nodes after NAC. ADC values between the group with pCR and those with no pCR were compared using the Mann-Whitney U test (two-group comparisons). Univariable and multivariabe logistic regression analysis was performed to predict pCR status. RESULTS: Overall, 83 patients (33.2%) achieved pCR. The ADC values of the patient group with pCR were lower compared with patients without pCR (0.98 ± 0.23 × 10- 3 mm2/s versus 1.07 ± 0.24 × 10- 3 mm2/s, p = 0.02). The ADC value achieved an odds ratio of 4.65 (95% CI 1.40-15.49) in univariable analysis and of 3.0 (95% CI 0.85-10.63) in multivariable analysis (overall sample) to be associated with pCR status. The odds ratios differed in the subgroup analyses in accordance with the molecular subtype. CONCLUSIONS: The pretreatment ADC-value is associated with pathological complete response after NAC in breast cancer patients. This could aid in clinical routine to reduce treatment toxicity for patients, who would not benefit from NAC. However, this must be tested in further studies, as the overlap of the ADC values in both groups is too high for clinical prediction.

Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans.

PURPOSE: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. MATERIALS AND METHODS: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. RESULTS: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P < .001 for both; n = 250). CONCLUSION: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.Keywords: MRI, Breast, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning AlgorithmsPublished under a CC BY 4.0 license. Supplemental material is available for this article.

Prognostic value of response evaluation based on breast MRI after neoadjuvant treatment: a retrospective cohort study.

OBJECTIVE: To investigate the impact of response evaluation after neoadjuvant chemotherapy (NAC) in breast cancer patients, assessed by both magnetic resonance imaging (MRI) and pathology, on disease-free survival (DFS). METHODS: This single-center, retrospective cohort study included consecutive breast cancer patients who underwent NAC and preoperative breast MRI. Resolution of invasive carcinoma in the breast and axilla was defined as complete pathological response (pCR). Radiological complete response (rCR) was defined as the absence of abnormal enhancement in the tumor site. Kaplan-Meier estimator was used to estimate the disease-free survival on 60 months. Cox regression analysis was used to estimate hazard ratio (HR) values. RESULTS: In total, 317 patients were included with a mean age of 47.3 years and a mean tumor size of 39.8 mm. The most common immunophenotype was luminal (44.9%), followed by triple-negative (26.8%). Overall, 126 patients (39.7%) had an rCR, while 119 (37.5%) had pCR; the radiological and pathological responses agreed in 252 cases (79.5%). During follow-up, patients who had rCR and pCR had a better DFS curve compared to patients with non-rCR and non-pCR, while those who had rCR or pCR presented an intermediate curve (Log-rank p = 0.003). Multivariate analysis showed a higher risk of recurrence in patients with non-rCR and non-pCR (HR: 5,626; p = 0.020) and those who had a complete response on MRI or pathology only (HR: 4,369; p = 0.067), when compared to patients with rCR and pCR. CONCLUSIONS: The association of MRI and pathological responses after NAC might better stratify the risk of recurrence and prognosis in breast cancer patients. KEY POINTS: • Association of response evaluation after neoadjuvant chemotherapy by pathology and MRI allows better stratification of prognosis. • Complete response to neoadjuvant chemotherapy on pathology and MRI was related to better disease-free survival. • Complete response on MRI or pathology only had a greater risk of recurrence.

Is It the Era for Personalized Screening?

Breast cancer screening is a recognized tool for early detection of the disease in asymptomatic women, improving treatment efficacy and reducing the mortality rate. There is raised awareness that a "one-size-fits-all" approach cannot be applied for breast cancer screening. Currently, despite specific guidelines for a minority of women who are at very high risk of breast cancer, all other women are still treated alike. This article reviews the current recommendations for breast cancer risk assessment and breast cancer screening in average-risk and higher-than-average-risk women. Also discussed are new developments and future perspectives for personalized breast cancer screening.

Contrast-Enhanced Mammography for Screening Women after Breast Conserving Surgery.

To investigate the value of contrast-enhanced mammography (CEM) compared to full-field digital mammography (FFDM) in screening breast cancer patients after breast-conserving surgery (BCS), this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective, single-institution study included 971 CEM exams in 541 asymptomatic patients treated with BCS who underwent screening CEM between January 2013 and November 2018. Histopathology, or at least a one-year follow-up, was used as the standard of reference. Twenty-one of 541 patients (3.9%) were diagnosed with ipsi- or contralateral breast cancer: six (28.6%) cancers were seen with low-energy images (equivalent to FFDM), an additional nine (42.9%) cancers were detected only on iodine (contrast-enhanced) images, and six interval cancers were identified within 365 days of a negative screening CEM. Of the 10 ipsilateral cancers detected on CEM, four were detected on low-energy images (40%). Of the five contralateral cancers detected on CEM, two were detected on low-energy images (40%). Overall, the cancer detection rate (CDR) for CEM was 15.4/1000 (15/971), and the positive predictive value (PPV3) of the biopsies performed was 42.9% (15/35). For findings seen on low-energy images, with or without contrast, the CDR was 6.2/1000 (6/971), and the PPV3 of the biopsies performed was 37.5% (6/16). In the post-BCS screening setting, CEM has a higher CDR than FFDM.

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). FINDINGS: The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). INTERPRETATION: The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. FUNDING: NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Ethical dilemmas in COVID-19 times: how to decide who lives and who dies?

The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study.

BACKGROUND: The aim of this study was to demonstrate the feasibility of performing multidetector computed tomography (MDCT) with a dedicated protocol for locoregional staging in breast cancer patients. METHODS: This prospective single-center study included newly diagnosed breast cancer patients submitted to contrast-enhanced chest MDCT and breast magnetic resonance imaging (MRI). MDCT was performed in prone position and using subtraction techniques. Fleiss' Kappa coefficient (K) and intraclass correlation coefficient (ICC) were used to assess agreement between MRI, MDCT, and pathology, when available. RESULTS: Thirty-three patients were included (mean age: 47 years). Breast MRI and MDCT showed at least substantial agreement for evaluation of tumor extension (k = 0.674), presence of multifocality (k = 0.669), multicentricity (k = 0.857), nipple invasion (k = 1.000), skin invasion (k = 0.872), and suspicious level I axillary lymph nodes (k = 0.613). MDCT showed higher number of suspicious axillary lymph nodes than MRI, especially on levels II and III. Both methods had similar correlation with tumor size (MRI ICC: 0.807; p = 0.008 vs. MDCT ICC: 0.750; p = 0.020) and T staging (k = 0.699) on pathology. CONCLUSIONS: MDCT with dedicated breast protocol is feasible and showed substantial agreement with MRI features in stage II or III breast cancer patients. This method could potentially allow one-step locoregional and systemic staging, reducing costs and improving logistics for these patients.

Response Evaluation of Choroidal Melanoma After Brachytherapy Using Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI): Preliminary Findings.

Purpose: To evaluate the role of diffusion-weighted magnetic resonance imaging (DW-MRI) in the assessment of therapeutic response in patients with choroidal melanoma treated with brachytherapy. Materials and Methods: We performed a prospective, unicentric study which included patients with choroidal melanoma and indication for brachytherapy. Three DW-MRI examinations were proposed for each patient, one before and two after treatment. The apparent diffusion coefficient (ADC) value was calculated on DW-MRI and compared with local tumor control assessed by ophthalmologic follow-up. Results: From 07/2018 to 06/2019, 19 patients were recruited, 13 of whom underwent follow-up examinations. Patients' ages ranged from 24 to 78 years and 52.9% were male. At the ocular ultrasound, the mean tumor thickness and diameter were 6.3 and 11.5 mm, respectively. Two patients (15.4%) showed signs of tumor progression during follow-up (7 and 9 months after treatment). There was no statistically significant difference in tumor size between MR before and after treatment, however, there was a significant reduction in mean ADC in patients with progression (p = 0.02). Conclusion: DW-MRI is a promising method for monitoring patients with choroidal melanoma; reduction in the mean ADC values between pre-treatment MRI and the first post-treatment MRI may be related to the lack of response to brachytherapy and increased risk of disease progression.

Elevated glycine detected on in vivo magnetic resonance spectroscopy in a breast cancer patient: case report and literature review.

Magnetic resonance spectroscopy (MRS) is a promising non-invasive diagnostic method that can detect and quantify endogenous tissue metabolites. High glycine levels obtained from ex vivo breast MRS have been associated with poor prognosis; however, glycine evaluation has not been reported regarding in vivo MRS. We report our finding in a breast cancer patient in whom pre-treatment but not post-treatment in vivo MRS showed elevated glycine and discuss the implications of this finding.

Functional and metabolic imaging in transthoracic biopsies guided by computed tomography.

OBJECTIVES: CT-guided biopsy of indeterminate lung lesions sometimes provides insufficient histological results due to tumor necrosis. Functional and metabolic methods such as DWI-MR and PET-CT may help by directing sample collection to a lesion area of greater biological representativeness. The objective is to evaluate the histopathological results based on findings on ADC and SUV levels in lung lesions suspected for primary cancer. METHODS: Tissue samples were evaluated after undergoing biopsies guided by either DWI-MR or PET-CT findings. In each patient, sample collection from two lesion areas was guided by local ADC and SUV. Values were used to define areas of low vs. high suspicion for cancer. RESULTS: Patients who underwent DWI-MR had median lesion size of 78.0 mm. Areas of higher suspicion (HSA) had a median ADC of 1.1 × 10-3 mm2/s, while areas of lower suspicion (LSA) had median ADC of 1.8 × 10-3 mm2/s (p = 0.0001). All HSA samples and 71.43% of LSA samples were positive for cancer (p = 0.0184). Patients who performed PET-CT had median lesion size of 61.0 mm. Median SUV was 7.1 for HSA and 3.9 for LSA (p = 0.0002). Positivity for cancer was observed in 76.9% of samples for both HSA and LSA (p = 0.0522). CONCLUSION: Use of DWI-MR and PET-CT showed that tumors are functional and metabolically heterogeneous and that this heterogeneity has implications for histopathological diagnosis. KEY POINTS: • Lung cancer is heterogeneous regarding functional and metabolic imaging. • Tumor heterogeneity may have implications in histopathological diagnosis. • Intralesional lower levels of ADC target highly suspected areas with a significant improvement in lung cancer diagnosis.

Ethical dilemmas in COVID-19 times: how to decide who lives and who dies?

SUMMARY The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.

RESUMO A doença respiratória provocada pelo coronavírus 2019 (COVID-19) é uma pandemia que produz uma grande quantidade simultânea de doentes com sintomas graves que necessitam de cuidados hospitalares especiais, sobrecarregando a infraestrutura dos serviços de saúde. Todas essas demandas geram a necessidade de racionar equipamentos e intervenções. Diante desse desequilíbrio, como, quando e quem decide, há o impacto dos sistemas estressores dos profissionais que se encontram na linha de frente do atendimento e, em segundo plano, questões inerentes à subjetividade humana. Nesse percurso, surge ainda a ideia do uso de algoritmos da inteligência artificial para substituir o profissional de saúde nessa tomada de decisão. Nesse contexto, fica o questionamento ético de como gerenciar as demandas produzidas pela pandemia. O objetivo deste trabalho é refletir, do ponto de vista da ética médica, sobre princípios basilares das escolhas executadas pelas equipes de saúde, no enfrentamento da pandemia da COVID-19, cujos recursos são escassos e as decisões ocasionam angústia e inquietação. Os valores éticos para o racionamento de recursos de saúde em uma epidemia devem convergir para algumas propostas embasadas em valores fundamentais, como maximizar os benefícios produzidos por recursos escassos, tratar as pessoas de forma igualitária, promover e recomendar os valores instrumentais, dar prioridade para situações críticas. Naturalmente ocorrerão julgamentos diferentes em circunstâncias distintas, mas é fundamental que haja transparência para garantir a confiança pública. Desse modo, é possível elaborar diretrizes de priorização utilizando valores e recomendações éticas bem delineados para atingir procedimentos justos de alocação de recursos.

Clinical applications of breast cancer metabolomics using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS): systematic scoping review.

INTRODUCTION: Breast cancer is a heterogeneous disease with different prognoses and responses to systemic treatment depending on its molecular characteristics, which makes it imperative to develop new biomarkers for an individualized diagnosis and personalized oncological treatment. Ex vivo high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS) is the most common technique for metabolic quantification in human surgical and biopsy tissue specimens. OBJECTIVE: To perform a review of the current available literature on the clinical applications of HRMAS 1H MRS metabolic analysis in tissue samples of breast cancer patients. METHODS: This systematic scoping review included original research papers published in the English language in peer-reviewed journals. Study selection was performed independently by two reviewers and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed. RESULTS: The literature search returned 159 studies and 26 papers were included as part of this systematic review. There was considerable variation regarding tissue type, aims, and statistical analysis methods across the different studies. To facilitate the interpretation of the results, the included studies were grouped according to their aims or main outcomes into: feasibility and tumor diagnosis (n = 6); tumor heterogeneity (n = 2); correlation with proteomics/transcriptomics (n = 3); correlation with prognostic factors (n = 11); and response evaluation to NAC (n = 4). CONCLUSION: There is a lot of potential in including metabolic information of breast cancer tissue obtained with HRMAS 1H MRS. To date, studies show that metabolic concentrations quantified by this technique can be related to the diagnosis, prognosis, and treatment response in breast cancer patients.