Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1–2 breast cancer

The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1–2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR−/HER2−) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1–2 BC patients, particularly given that it can be used to adjust surgical options in the future.

Co-existent breast malignancy and contralateral primary axillary tuberculosis

Breast tuberculosis is an extremely rare entity representing less than 0.1% of all breast disease in developed countries1. Tuberculous infections within the United Kingdom have seen a steady decline with the highest rates present within North West London where infection rates reach 24.8 per 1000002. The presentation can mimic malignancy and lymphatic involvement of the breast both clinically and mammographically, with nodules within the upper outer quadrant, making accurate diagnosis challenging.3 Approximately 30% of breast TB cases present with axillary lymphadenopathy and a recent case series review of approximately 44 cases in London found that the most common presenting feature was a solitary breast lump in 87% of cases.4 We present a case of a patient presenting with primary malignancy and contralateral nodal disease highly suspicious for breast malignancy. Subsequent investigation led to the identification of synchronous localized cancer and tuberculous lymphadenitis. Synchronous presentation is uncommon and recognition and differentiation is vital as axillary lymph node metastasis is the most important factor in the staging of breast carcinoma and determining the subsequent oncological and surgical management.

Axillary metastasis from occult breast cancer and synchronous contralateral breast cancer initially suspected to be cancer with contralateral axillary metastasis: a case report

Background Initial detection of axillary metastasis without known ipsilateral breast cancer could be a challenging diagnostic problem. Four options could be considered for the primary site of the malignancy: ipsilateral occult breast cancer, contralateral breast cancer, tumors in other distant organs, and primary axillary malignancy itself. Although breast cancer is known as the most common primary cancer of axillary metastasis, both occult breast cancer and breast cancer with contralateral axillary metastasis (CAM) are rare. Case presentation A 63-year-old woman presented with palpable right axillary metastasis, and a tiny contralateral breast cancer was detected by breast magnetic resonance imaging. No lesion was found in the ipsilateral right breast and contralateral left axillary region. Both right axillary metastasis and contralateral breast cancer were positive for estrogen receptor. The diagnostic issue was to determine whether the axillary metastasis was derived from the contralateral breast cancer or not. Right axillary dissection and left breast conserving surgery were performed. The final diagnosis was occult breast cancer that presented with axillary lymph node metastasis and early-stage synchronous contralateral breast cancer, based on clinical evidence and postoperative pathologic results. After surgery, systemic treatment and whole breast irradiation were administered. No recurrence or metastasis was observed 15 months postoperatively. Conclusion For accurate diagnosis of axillary metastasis without detectable ipsilateral breast cancer, multifaceted diagnostic approach considering clinical, radiological, and pathological evidences is required.

The association between conventional ultrasound and contrast-enhanced ultrasound appearances and pathological features in small breast cancer

OBJECTIVE: To investigate the association between ultrasound appearances and pathological features in small breast cancer. MATERIALS AND METHODS: A total of 186 small breast cancers in 186 patients were analyzed in this retrospective study from January 2015 to December 2019 according to pathological results. Forty-seven cases of axillary lymph node metastasis were found. All patients underwent radical axillary surgery following conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) examinations. The association between ultrasound appearances and pathological features was analyzed using univariate distributions and multivariate analysis. Then, a logistic regression model was established using the pathological diagnosis of lymph node metastasis and biochemical indicators as the dependent variable and the ultrasound appearances as independent variables. RESULTS: In small breast cancer, risk factors of axillary lymph node metastasis were crab claw-like enhancement on CEUS and abnormal axillary lymph nodes on US. The logistic regression model was established as follows: (axillary lymph node metastasis) = 1.100×(crab claw-like enhancement of CEUS) + 2.749×(abnormal axillary lymph nodes of US) –5.790. In addition, irregular shape on CEUS and posterior echo attenuation on US were risk factors for both positive estrogen receptor and progesterone receptor expression, whereas calcification on US was a risk factor for positive Her-2 expression. A specific relationship could be found using the following logistic models: (positive ER expression) = 1.367×(irregular shape of CEUS) + 1.441×(posterior echo attenuation of US) –5.668; (positive PR expression) = 1.265×(irregular shape of CEUS) + 1.136×(posterior echo attenuation of US) –4.320; (positive Her-2 expression) = 1.658×(calcification of US) –0.896. CONCLUSION: Logistic models were established to provide significant value for the prediction of pre-operative lymph node metastasis and positive biochemical indicators, which may guide clinical treatment.

Impact of Molecular Subtypes of Breast Cancer on Axillary Lymph Node Metastasis: A Tertiary Center Experience

Background: Axillary lymph node metastasis (ALNM) is one of the important prognostic factors of breast cancer. The objective of this study was to assess the risk of ALNM in different molecular subtypes determined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (her2neu) of breast cancer. Methods: This retrospective study was conducted on patients who had undergone upfront breast conserving surgery (BCS) or modified radical mastectomy (MRM). Patients were classified as HR (hormone receptor) +/ her2neu- (ER or PR positive and her2neu negative), HR+/her2neu+ (ER or PR positive and her2neu positive), HR-/her2neu- (ER, PR and her2neu negative or triple negative or basal type), and HR-/her2neu+ (ER or PR negative and her2neu positive). The association between clinicopathological variables and ALNM was evaluated in logistic regression analyses. Results: In this study, 476 patients met the inclusion criteria, and had 67.2% ALNM at diagnosis. ALNM was statistically significantly correlated with age ≤ 40 years (p=0.026), tumor grade (p=0.007), pathological tumor size (P<0.001), estrogen receptor (P=0.045), molecular subtypes (P=0.021), LVI (P<0.001), and PNI (P<0.001). Post Hoc test revealed that HR-/her2neu+ subtypes of breast cancer had the highest and HR+/her2neu- had the lowest risk of ALNM. Conclusion: ALNM may be predicted by molecular subtypes of breast cancer. The risk of ALNM is less in TNBC although it is clinically more aggressive. These findings may play an important role in gauging the individualized axillary management in breast cancer.

Association of Histopathological Parameters and Axillary Lymphnode Metastasis in Primary Breast Carcinoma

Introduction: The most common malignancy worldwide among females is breast carcinoma and the second most common malignancy in India, next to cervical cancer. A wide range of potential prognostic features have been studied in breast cancer and are mainly divided into two groups i.e. Histopathological and Molecular. The histological features are cost-effective and provide reliable diagnostic and prognostic information in these tumors. Axillary Lymph node status is one of the most important prognostic factors and greatly affects the morbidity and mortality of the patient. Materials and Methods: All breast cancer specimens received in the Department of Pathology over a period of five years. The following histopathological parameters were carefully studied like Tumor size, Histological type, Grade, Presence of necrosis, Inflammatory cell infiltrate, Lymphatic invasion, Blood vessel invasion, Perineural invasion, and other Stromal changes were studied in detail, and association of these histopathological parameters with axillary lymph node metastasis were analyzed. Results: A total of 100 cases were studied, and most of the patients were over the age of 50. The maximum number of cases was in the T2 stage (55%). Infiltrating ductal carcinoma (88%) was the most common type of tumor encountered in the study. The majority of the cases were Grade I tumors. Skin Invasion was seen in 14% and Lymphovascular Invasion was seen in 17% of cases respectively. There was a statistically significant association between the size of the tumor, T stage, Grade of the tumor, necrosis, and inflammatory infiltrate on further analysis. Conclusion: There was a statistically significant correlation between Tumor size, pathological T stage, Grade of the tumor, Necrosis and inflammatory infiltrate with axillary Lymph node metastasis in the present study. Increased tumor size, T stage, higher grade, presence of necrosis and low inflammatory infiltrate are associated with increased axillary Lymph node metastasis.

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

ObjectivesTo develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.MethodsA total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.ResultsThe best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).ConclusionOur study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

Analysis of the Mechanism of Breast Metastasis Based on Image Recognition and Ultrasound Diagnosis

Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.