Axillary Lymph Node Metastasis Research Articles

Cystic Hypersecretory Carcinoma of the Breast: A Rare Case Report with Review of Literature and Emphasis on Differential Diagnosis.

Karthik DhandapanCystic hypersecretory carcinoma (CHC) is a rare subset of in-situ breast carcinoma with or without associated invasive carcinoma. It is part of a spectrum of cystic hypersecretory lesions that includes cystic hypersecretory hyperplasia (CHH), CHH with atypia, CHC in situ, and CHC with invasion. Only 20 cases of CHC with invasion have been reported so far. A 60-year-old female presented with a palpable right breast mass. A core needle biopsy was carried out, which was reported as invasive breast carcinoma with areas of ductal carcinoma in situ (DCIS). Modified radical mastectomy was done post-neo-adjuvant chemotherapy; On microscopy, dilated cystic spaces filled with eosinophilic secretions (thyroid colloid-like), lining neoplastic cells with variable degrees of proliferation and atypia were seen. There were multiple foci of invasion; both skin invasion and axillary lymph node metastasis were present. Immunohistochemistry (IHC) was done with relevant markers; correlating all these findings, a diagnosis of CHC with invasion was made. CHC is a distinct form of DCIS with or without associated invasion. Awareness of this entity is required to rule out other differential diagnoses and to avoid misinterpretation. Little is known about the IHC profile, biological behavior, prognosis, and molecular profile of CHC due to its rarity.

Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. We retrospectively enrolled 196 patients with non-specific invasive breast cancer confirmed by pathology, radiomics and deep learning features were extracted from unenhanced and biphasic (arterial and venous phase) contrast-enhanced CT, and the non-linear support vector machine was used to construct the radiomics signature and the deep learning signature, respectively. Next, a DLRN was developed with independent predictors and evaluated the performance of models in terms of discrimination and clinical utility. Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and clinical n stage were independent predictors. The DLRN accurately predicted ALNM and yielded an area under the receiver operator characteristic curve of 0.893 (95% confidence interval, 0.814-0.972) in the validation set, with good calibration. Decision curve analysis confirmed that the DLRN had higher clinical utility than other predictors. The DLRN had good predictive value for ALNM in breast cancer patients and provide valuable information for individual treatment.

Systemic Sclerosis Manifesting Post-Radiotherapy: A Case Report

Morphoea is a chronic inflammatory disease of the skin and underlying tissues, characterised by fibrosis of the skin and subcutaneous tissue without systemic involvement. Radiation-induced morphoea is a rare, often unrecognised, chronically progressive form of radiation-associated localised scleroderma of the skin, infrequently progressing to systemic scleroderma. Systemic scleroderma is characterised by widespread tissue fibrosis along with systemic features, leading to an increased risk of malignancy compared to the general population. The authors present the case of a 67-year-old female, a previously diagnosed case of psoriasis developing squamous cell carcinoma on the ring finger with axillary nodal metastasis, who underwent radiotherapy, and subsequently developed symptoms of systemic sclerosis. This case is unique, as there are nominal reports on generalised morphoea converting to systemic sclerosis post-radiotherapy, along with exaggerated thickening of the skin over the side of radiotherapy. Thus, the authors report this rare side effect of radiotherapy. Treating physicians should always be vigilant in identifying these side effects after radiotherapy to prevent long-term sequelae of the disease, and minimise their impact on the patient’s quality of life.

Breast Carcinoma and Tuberculosis in Axillary Lymph Nodes: A Case Report

The coexistence of tuberculosis with axillary lymph node metastases in breast carcinoma is rare. Axillary lymph node metastasis is the most important factor in the staging of breast carcinoma, this can mimic or complicate the staging of a malignant disease. Dual organ pathology can lead to interpretation difficulties and inappropriate treatment of tuberculosis as well as breast carcinoma. We report an observation of infiltrating carcinoma of the non-specific type of the breast in woman aged 39, where tuberculosis was found in the axillary lymph nodes in addition to metastases. The possible and simultaneous occurrence of breast carcinoma and tuberculosis should be mentioned in order to avoid difficulties of interpretation and allow adequate management of axillary tuberculous lymphadenitis and breast carcinoma.

Discriminative Factors of Malignancy of Ipsilateral Nonmass Enhancement in Women With Newly Diagnosed Breast Cancer on Initial Staging Breast MRI.

Nonmass enhancement (NME) on breast MRI impacts surgical planning. To evaluate positive predictive values (PPVs) and identify malignancy discriminators of NME ipsilateral to breast cancer on initial staging MRI. Retrospective. Eighty-six women (median age, 48 years; range, 26-75 years) with 101 NME lesions (BI-RADS 4 and 5) ipsilateral to known cancers and confirmed histopathology. 1.5 T and 3.0 T dynamic contrast-enhanced fat-suppressed T1-weighted fast spoiled gradient-echo. Three radiologists blinded to pathology independently reviewed MRI features (distribution, internal enhancement pattern, and enhancement kinetics) of NME, locations relative to index cancers (contiguous, non-contiguous, and different quadrants), associated mammographic calcifications, lymphovascular invasion (LVI), axillary node metastasis, and radiology-pathology correlations. Clinical factors, NME features, and cancer characteristics were analyzed for associations with NME malignancy. Fisher's exact, Chi-square, Wilcoxon rank sum tests, and mixed-effect multivariable logistic regression were used. Significance threshold was set at P < 0.05. Overall NME malignancy rate was 48.5% (49/101). Contiguous NME had a significantly higher malignancy rate (86.7%) than non-contiguous NME (25.0%) and NME in different quadrants (10.7%), but no significant difference was observed by distance from cancer for non-contiguous NME, P = 0.68. All calcified NME lesions contiguous to the calcified index cancer were malignant. NME was significantly more likely malignant when index cancers were masses compared to NME (52.9% vs. 21.4%), had mammographic calcifications (63.2% vs. 39.7%), LVI (81.8% vs. 44.4%), and axillary node metastasis (70.8% vs. 41.6%). NME features with highest PPVs were segmental distribution (85.7%), clumped enhancement (66.7%), and nonpersistent kinetics (77.1%). On multivariable analysis, contiguous NME, segmental distribution, and nonpersistent kinetics were associated with malignancy. Malignancy discriminators of ipsilateral NME on staging MRI included contiguous location to index cancers, segmental distribution, and nonpersistent kinetics. 3 TECHNICAL EFFICACY: Stage 2.

Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.

This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC). This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction model in predicting axillary lymph node metastasis (ALNM) in the three cohorts. Deep learning radiomics combined with radiomics and clinical parameters was the optimal diagnostic predictor of the ALN status in the absence and presence of ALNM, with the AUC of 0.920 (95% confidence interval: 0.872 and 0.968, respectively). Additionally, this combination could also differentiate low-load ALNM [N + (1-2)] from heavy-load ALNM with ≥3 positive nodes [N + (≥3)] in the test cohort, with the AUC of 0.819 (95% confidence interval: 0.568 and 1.00, respectively). Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.

Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value—an analysis of 2473 invasive breast cancer patients

BackgroundThe value and utility of axillary lymph node (ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The aim of the current study is to test the potential of combining breast MRI and clinicopathologic factors to identify low-risk groups of ALN metastasis and improve diagnostic performance.Material and methodsPatients with primary operable invasive breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected from January 2009 to December 2021 in a single institute. The concordance of MRI and pathology of ALN status were determined, and also analyzed in different intrinsic subtypes. A stepwise strategy was designed to improve MRI-negative predictive value (NPV) on ALN metastasis.Results2473 patients were enrolled. The diagnostic performance of MRI in detecting metastatic ALN was significantly different between intrinsic subtypes (p = 0.007). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with HER-2 (MRI tumor size ≤ 2 cm), or TNBC (MRI tumor size ≤ 2 cm) were found to have MRI–ALN-NPV higher than 90%, and these false cases were limited to low axillary tumor burden.ConclusionThe diagnostic performance of MRI to predict ALN metastasis varied according to the intrinsic subtype. Combined pre-operative clinicopathologic factors and intrinsic subtypes may increase ALN MRI NPV, and further identify some groups of patients with low risks of ALN metastasis, high NPV, and low burdens of axillary disease even in false-negative cases.

Retracted: MRI Diagnosis and Pathological Examination of Axillary Lymph Node Metastasis in Breast Cancer Patients.

[This retracts the article DOI: 10.1155/2022/4519982.].

Primary Cutaneous Adenoid Cystic Carcinoma Of The Chest Wall With Axillary Lymph Node Metastases And Its Management: A Case Report.

Primary cutaneous adenoid cystic carcinoma (PCACC) is a rare form of adenoid cystic carcinoma (ACC) arising commonly from the salivary gland. Less often they originate outside the head and neck region, with the scalp being the commonest cutaneous site in 40% of the cases. The presentation on the chest wall is rare, with no reports to date on axillary lymph node metastases. Here we report a case of a 65-year-old female with previously operated PCACC of the chest wall at a different center, showing uptake on positron emission tomography imaging at the site of surgical scar that w as inconclusive on needle biopsy metastasized to the axillary lymph node confirmed by needle biopsy managed with wide local excision, axillary lymph node dissection, and chest wall reconstruction with keystone island flap. The postoperative outcome was uneventful with no recurrence or axillary complications at one year's follow-up. She was advised to receive adjuvant radiotherapy; however, she refused. To conclude, though PCACC is rare, they can have an aggressive presentation, and a multidisciplinary approach is necessary for a better outcome.

Multimodal radiomics and nomogram-based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region.

To explore the optimal peri-tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM). This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra-tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri-tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination-SVM was used to evaluate the efficacy of models with different feature numbers used. The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676-0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra-tumoral US and DM and US-based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829-0.936/0.741-0.929/0.752-0.896 for training/internal validation/external validation cohort, respectively). The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram.

Clinical relevance of biomarker discordance between primary breast cancers and synchronous axillary lymph node metastases

Clinical decision-making for patients with breast cancer (BC) is still primarily based on biomarker characteristics of the primary tumor, together with the evaluation of synchronous axillary lymph node metastasis (LNM). In this study, we investigated the prevalence of discordance in the biomarkers and surrogate subtyping between the primary BC and the LNM, and whether subsequent changes would have altered clinical treatment recommendations. In this retrospective study, 94 patients treated for unifocal primary BC and synchronous LNM at Sahlgrenska UniversityHospital during 2018 were included. Estrogen (ER) and progesterone (PR) receptor, Ki67, and HER2 status were assessed in the primary tumor and LNM using immunohistochemistry. Discordances between the primary tumor and the LNM were analyzed for each individual biomarker and surrogate subtyping. The concordance between the primary tumor and the LNM for ER, PR, Ki67, and HER2 status was 98.9%, 89.4%, 72.3%, and 95.8%, respectively. Discordance in surrogate subtyping was found in 28.7% of the tumors and matched LNMs, the majority (81.5%) of which changed to a more favorable subtype in the LNM; most commonly from Luminal B to Luminal A (48.6%). No changes in surrogate subtyping were detected where ER or HER2 status changed from negativity in the BC to positivity in the LNM, thereby showing no additional value in performing immunohistochemistry on the LNM from a treatment decision-making perspective. However, large studies need to be performed that test both the primary BCs and synchronous LNMs for more accurate diagnostics.

Pure tubular carcinoma of the breast: a case series

BackgroundPure tubular breast carcinoma is a rare and well-differentiated tumor with high survival and low local recurrence rate. Our study aims to determine the clinical, radiological, appropriate management, and prognosis of this carcinoma.Materials and methodsA review of Salah Azaiez institute registry from 2004 to 2019 was performed including seven cases of PTC of the breast.ResultsClinical-pathologic features and outcomes were analyzed. The median follow-up was 3 years. In our study, we found that the cohort presented more frequently with pT1 disease and pN0 disease. Conservative surgery was more frequently indicated (five cases). All patients had hormone-receptor positivity and Human Epidermal growth factor Receptor 2 (HER2) negativity. The majority of tumors had a molecular profile luminal A and a low-grade SBR. In one case we found axillary lymph node metastasis. Adjuvant radiotherapy was indicated in all cases of breast conservation and in only one case of radical surgery. One patient received chemotherapy. The mean follow-up was 4 years. We did not find any local or distant recurrence in our study.ConclusionPTC showed an excellent prognosis with a low SBR grade, a molecular profile luminal A, and a low incidence of recurrence.

Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI

To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC). Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N=27) and non-ALNM (n=59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method. Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio=1.33, P=0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features. A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.

Correlation of shear-wave elastography parameters with the molecular subtype and axillary lymph node status in breast cancer

PurposeTo examine correlations between shear-wave elastography (SWE) parameters with molecular subtype and axillary lymph node (LN) status of breast cancer. MethodsWe retrospectively analyzed 545 consecutive women (mean age, 52.7 ± 10.7 years; range, 26–83) with breast cancer who underwent preoperative breast ultrasound with SWE between December 2019 and January 2021. SWE parameters (Emax, Emean, and Eratio) and the histopathologic information from surgical specimens including histologic type, histologic grade, size of invasive cancer, hormone receptor and HER2 status, Ki-67 proliferation index, and axillary LN status were analyzed. The relationships between SWE parameters and histopathologic findings were analyzed using an independent sample t-test, one-way ANOVA test with Tukey's post hoc test, and logistic regression analyses. ResultsHigher stiffness values of SWE were associated with larger lesion size (>20 mm) on ultrasound, high histologic grade, larger invasive cancer size (>20 mm), high Ki-67, and axillary LN metastasis. Emax and Emean were the lowest in the luminal A-like subtype, and all three parameters were the highest in the triple-negative subtype. Lower value of Emax was independently associated with the luminal A-like subtype (P = 0.04). Higher value of Emean was independently associated with axillary LN metastasis for tumors ≤ 20 mm (P = 0.03). ConclusionIncreases in the tumor stiffness values on SWE were significantly associated with aggressive histopathologic features of breast cancer. Lower stiffness values were associated with the luminal A-like subtype, and tumors with higher stiffness values were associated with axillary LN metastasis in small breast cancers.

Surgery on Metastatic Foci is a Better Strategy for Stage IV Breast Cancer Patients with only Nonregional Lymph Node Metastasis.

Our study aims to explore whether breast cancer patients with non-regional lymph node (NRLN) metastasis benefit from surgery on distant nodes, and to determine the influencing factors affecting the prognosis of this type of patient. Information of invasive ductal carcinoma (IDC) patients from 2004 to 2016 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed by statistical methods, including multivariate Cox regression model, chi-squared test, propensity score matching (PSM), Kaplan-Meier plot, and log-rank test. A total of 4236 M1 patients met the designated criteria. Among 847 patients with only NRLN metastasis who have detailed information, only 114 patients received surgery on metastatic distant lymph nodes. The Kaplan-Meier plot for overall survival (OS) showed that the prognosis of NRLN metastatic patients was superior to visceral metastasis (P<0.0001) but similar to supraclavicular metastasis (P=0.33). In addition, NRLN metastatic patients who underwent surgery on the NRLNs were found to have superior prognoses in terms of both OS (P=0.041) and cancer-specific survival (P=0.034) compared with those who did not undergo NRLN surgery. We have also demonstrated that NRLN metastatic patients who have received radiotherapy plus chemotherapy for primary tumors gain superior survival compared with those who only received chemotherapy apart from NRLN surgery. Surgery on NRLN and radiotherapy for the primary tumor improved the prognosis of NRLN metastatic patients. Thus, the classification of NRLN, especially contralateral axillary lymph node metastasis (CAM), into the M1 breast cancer stage should be reconsidered. Different locoregional treatment strategies for metastatic foci should be recommended for patients with only NRLN and patients with visceral metastasis.

Gene expression comparison between primary aggressive luminal breast cancer and paired axillary and sentinel lymph node metastasis.

e13060 Background: More aggressive and therapy-resistant hormone receptor (HR)-positive breast cancer (hereafter luminal breast cancer, LBC) remains a great challenge. Few studies examine the genomics of axillary lymph node (ALN) metastasis in LBC. The aim of this study was to characterize and compare the mRNA expression patterns of primary breast cancers and paired ALN metastases. Methods: Aggressive pT1-2N2-3M0 LBC patients aged over 18 years who were treated at Shandong Provincial Hospital Affiliated to Shandong First Medical University and had available matched primary and metastatic tumor samples for gene expression analyses were retrospectively recruited. Bulk mRNA expression levels were determined by RNA sequencing (Illumina NovaSeq 6000 system) and calculated using featureCounts. Differential gene expression analysis was performed using DESeq2. All analyses were carried out with differentially expressed genes (DEGs) with an FDR &lt;0.05. Changes of intrinsic subtypes from primary to metastatic disease were assessed. Results: Ten LBC patients with paired tumor samples were included. Compared with the primary tumor, ALN metastatic tumor had 1509 statistically significant DEGs, including 743 upregulated genes and 766 downregulated genes. Notably, there was an upregulation of genes related to immune response-activating signal transduction, T cell receptor signaling pathway, B cell receptor signaling pathway, and cytokine-cytokine receptor interaction in the ALN metastasis. The downregulation of genes were mainly enriched in the PI3K-Akt signaling pathway, ECM-receptor interaction, Wnt signaling pathway, and TGF-beta signaling pathway. Intrinsic subtype distribution in primary tumors was different from those in ALN metastatic tumors, such as Luminal A (30% vs. 50%), Luminal B (10% vs. 20%), HER2-enriched (20% vs. 10%), Basal-like (10% vs. 10%), and Normal-like (30% vs. 10%). These results suggest that LBC is a highly heterogeneous disease. Conclusions: In aggressive pT1-2N2-3M0 LBC, ALN metastases have a distinct gene expression profile and show upregulation of immune-related genes ( CR2, BLK, PAX5, CD19, FCRL1, TNFRSF13B). Genes associated with signaling pathways are involved in the development of breast cancer ( FST, TNN, LAMC3, SFRP2, FGF18), and are downregulated when compared to autologous primary cancer. These pathways are potential targeted pathways in the treatment of breast cancer. LBC cells metastatic to lymph nodes undergo a change to metastasize and survive in the new microenvironment, which may provide insights into the metastatic process. Besides, a switch towards more aggressive intrinsic subtype from primary to advanced disease is also observed.

High Levels of SII and PIV are the Risk Factors of Axillary Lymph Node Metastases in Breast Cancer: A Retrospective Study.

To investigate the predictive value of systemic immune-inflammation-index (SII) and Pan-Immune-Inflammation-value (PIV) for axillary lymph node (ALN) metastasis in patients with breast cancer. We retrospectively collected data of 247 patients with invasive breast from the Affiliated Hospital of Jiangnan University. The state of axillary lymph node (ALN) metastasis was confirmed by pathological diagnosis. Clinicopathological data (age, ER, PR, HER2, Ki67 expression levels, diapause status, weight, histological grade, vascular invasion, and state of axillary lymph node) were compared between differences of SII and PIV groups and an association between clinical indexes and ALN metastasis was evaluated. The cut-off values of SII and PIV were 320.04 and 92.01, respectively. The significant difference between vascular invasion (P=0.023) and axillary lymph node metastases (P<0.001) in the high and low SII levels. Significant differences were observed in tumor size (p=0.024), PR expression level (P=0.033) and the status of axillary lymph node metastases (p<0.001) between the high PIV group and the low PIV group. Univariate analysis showed that vascular invasion, tumor size, Ki67 expression level, SII, and PIV were significantly correlated with axillary lymph node metastases (p<0.05). Then, multivariate analysis revealed that the vascular invasion (p<0.001), HER2 expression levels (p<0.047), SII (p<0.001) and PIV (p<0.030) were risk factors for axillary lymph node metastases. High levels of SII, PIV, LVI, and HER2 are the risk factors for axillary lymph node metastases in breast cancer patients.

Clinical characteristics of interval breast cancer within 12 months and 13-24 months in Utah.

10531 Background: A breast cancer (BC) that emerges following a negative/benign screening mammogram (SMG) but before the next expected SMG is referred to as an interval breast cancer (IBC). Current guidelines vary in the recommended frequency of SMG: some guidelines support SMG every 12 months, others every 24 months, and many adapt the 12- or 24-month recommendation by age. This study compared the clinical characteristics of IBC that developed within a 3-12 month interval (12m IBC) and a 13-24 month interval (24m IBC) in a statewide population. Methods: This is a retrospective cohort study using demographic/medical records in the Utah Population Database linked to the Utah Cancer Registry, a Surveillance, Epidemiology, and End Results (SEER) registry. Index SMG refers to the SMG just prior to a diagnostic mammogram or other clinical encounter, which led to a BC diagnosis before the next, expected SMG. Women ages ≥38y who underwent SMG during 2005-2019 at Intermountain Healthcare and University of Utah Health, had no history of BC prior to first SMG, and had BI-RADS assessment of normal or benign finding at index SMG were included. Clinical characteristics of 12m IBC and 24m IBC were compared using multivariable logistic regression to provide odds ratio estimates with covariate adjustment for age at diagnosis, stage, tumor size, grade, histology, biomarkers, breast density, body mass index, family history of breast cancer, and age at first birth. Women who developed IBC during the 3-12 month interval were not counted in the 24m IBC group. Results: A total of 7776 BC were observed in this study, among which 2122 were IBC; 486 (6.3%) 12m IBC and 1636 (21.0%) 24m IBC. Women with 12m IBC and 24m IBC had similar rates of a first-degree family history of breast cancer (16% and 17%, respectively). Women 70 and older were the least likely to develop IBC in 12 months as compared to 24 months (OR 2.34, 95% CI: 1.61-3.40, p&lt; 0.01). The odds of having axillary lymph node metastases (regional disease) at the time of BC diagnosis was higher among 12m IBC than 24m IBC (OR 1.68, 95% CI: 1.29-2.19, p&lt; 0.01). A total of 41 women with IBC had distant metastatic disease at the time of diagnosis (5% 12m IBC, 1% 24m IBC). Among those, the odds that metastatic IBC would be a 12m IBC were significantly higher than 24m IBC (OR = 4.07, CI 2.03-8.29, p&lt; 0.01). The 12m IBC group was more likely to be HER2 positive (OR 2.10, 95% CI: 1.36-3.22, p&lt; 0.01) or triple negative (OR 2.08, 95% CI: 1.26-3.39, p&lt; 0.01) than the 24m IBC group. Extremely dense breast tissue at SMG increased the odds of developing 12m IBC compared to 24m IBC (OR 2.05, 95% CI: 1.13-3.80, p&lt; 0.01). Conclusions: Compared to 24m IBC, 12m IBC appeared to be more advanced and aggressive. Women with Extremely dense breast tissue were more likely to develop 12m IBC than 24m IBC. These results suggest that women with extremely dense breast tissue or age &lt; 70y may benefit from annual SMG instead of biennial SMG.

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). Conclusion: The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.

Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features

BackgroundAxillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance.MethodsA total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan–Meier curves were used to analyze the survival outcomes.ResultsClinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, Kep, Ve, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS.ConclusionPSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.