Axillary Lymph Node Metastasis Research Articles

Predicting axillary nodal metastasis based on the side of asymmetrical cortical thickening in breast cancer: Evaluation with grayscale and microvascular imaging findings

PurposeTo investigate the relationship between sonographic findings and the axillary status, especially the side of thickening in the presence of cortical asymmetry. MethodsPatients with biopsy-proven axillary lymph node (ALN) metastasis were included in this study. The lymph nodes were divided into three groups depending on the type of cortical thickening as diffuse, closer (eccentric cortical thickening on the side near the tumor and/or breast) and distant (thickening on the further side) asymmetry. Longitudinal to transverse axis (L/T) ratio, the largest cortical thickness, cortex to hilum ratio (C/H), hilar status (normal/displaced/absent), orientation (parallel/vertical), capsular integrity (sharp/indistinct), vascularisation pattern (hilar/peripheral/penetrant/anarchic/avascular) on superb microvascular imaging (SMI) and presence of conglomeration were recorded for each lymph node. Axillary nodal status on 18F-FDG PET-CT/MRI scans was recorded, if available. Features of the breast lesions like size, laterality, nuclear grade, hormone receptor status and the level of Ki-67 expression have been added. ResultsA total of 219 metastatic ALNs [diffuse (n = 122), closer asymmetry (n = 71), distant asymmetry (n = 26)] were evaluated. By the univariate analysis, ALN metastasis was significantly associated with the presence of closer asymmetrical cortical thickening (p < 0,0001), C/H ratio (p = 0.001), cortical thickness (p = 0.001), hilar status (p < 0.005) and vascular pattern (p < 0.005). L/T ratio was only a statistically significant parameter for lymph nodes with diffuse cortical enlargement in predicting metastasis, and conglomeration was also observed only in this group (p < 0.05). By multivariate analysis, nodal metastasis was significantly associated with asymmetrical cortical thickening (p = 0.001), C/H ratio (p = 0.005) and vascular pattern (p < 0.0001). ConclusionAsymmetrical cortical enlargement on the side closer to the breast, C/H ratio and abnormal microvascular pattern are the independent predictors of axillary nodal involvement. Closer asymmetry is an eligible, easy-to-detect grayscale US finding to decide sampling that highly predicts ALN metastasis.

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound.

The aim of this study was to develop a radiomics nomogram based on grayscale ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with pathologically confirmed T1 (pT1) breast invasive ductal carcinoma (IDC). One hundred and ninety-two patients with pT1 IDC between September 2020 and August 2022 were analyzed retrospectively. Study population was randomly divided in a 7: 3 ratio into a training dataset of 134 patients (37 patients with LVI-positive) and a validation dataset of 58 patients (19 patients with LVI-positive). Clinical information and conventional US (CUS) features (called clinic_CUS features) were recorded and evaluated to predict LVI. In the training dataset, independent predictors of clinic_CUS features were obtained by univariate and multivariate logistic regression analyses and incorporated into a clinic_CUS prediction model. In addition, radiomics features were extracted from the grayscale US images, and the radiomics score (Radscore) was constructed after radiomics feature selection. Subsequent multivariate logistic regression analysis was also performed for Radscore and the independent predictors of clinic_CUS features, and a radiomics nomogram was developed. The performance of the nomogram model was evaluated via its discrimination, calibration, and clinical usefulness. The US reported axillary lymph node metastasis (LNM) (US_LNM) status and tumor margin were determined as independent risk factors, which were combined for the construction of clinic_CUS prediction model for LVI in pT1 IDC. Moreover, tumor margin, US_LNM status and Radscore were independent predictors, incorporated as the radiomics nomogram model, which achieved a superior discrimination to the clinic_CUS model in the training dataset (AUC: 0.849 vs. 0.747; P < 0.001) and validation dataset (AUC: 0.854 vs. 0.713; P = 0.001). Calibration curve for the radiomic nomogram showed good concordance between predicted and actual probability. Furthermore, decision curve analysis (DCA) confirmed that the radiomics nomogram had higher clinical net benefit than the clinic_CUS model. The US-based radiomics nomogram, incorporating tumor margin, US_LNM status and Radscore, showed a satisfactory preoperative prediction of LVI in pT1 IDC patients.

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model.

To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer. Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant. For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917). Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.

Downregulated miR-367-3p, miR-548aq-5p, and miR-4710 in Human Whole Blood: Potential Biomarkers for Breast Cancer With Axillary Lymph Node Metastasis

BackgroundIncreasing studies have shown that microRNAs (miRNAs) have great diagnostic value in cancer. Axillary lymph node metastasis (ALNM) is closely related to the prognosis of breast cancer. However, it remains unknown whether miRNAs in whole blood could be promising biomarkers in breast cancer ALNM. MethodsAn miRNA microarray was used to screen potential differentially expressed miRNA candidates in whole blood of three breast cancer patients with ALNM and three without ALNM. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect candidate differentially expressed miRNAs in the whole blood of 109 breast cancer patients. Furthermore, bioinformatics analysis was carried to predict the potential targets and enriched pathway of miRNAs. ResultsQRT-PCR validated the fact that miR-367-3p, miR-548aq-5p and miR-4710 are downregulated in breast cancer with ALNM compared to it without ALNM. Receiver operating characteristic (ROC) curve analysis revealed that miR-367-3p, miR-548aq-5p and miR-4710 have good diagnostic values. Notably, the three-miRNA signature showed better predictive value, with an area under ROC curve (AUC) of 0.7414. Bioinformatics analysis revealed that the miRNAs could participate in a complex network and thus be involved in cancer-related pathways. ConclusionsOur findings support the potential of miR-367-3p, miR-548aq-5p and miR-4710 and the three-miRNA signature as biomarkers for breast cancer with ALNM.

Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study.

Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and traditional survival analysis methods are biased when predicting mortality. To predict the risk of death in patients with triple-negative breast cancer more precisely, a competing risk model was developed. The clinicopathological data of the TNBC patients from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The data were assigned into a training set and testing set at a ratio of 7:3 in a randomized pattern. Univariate and multivariate competing risk models were applied to find the independent prognostic factors. A prediction nomogram for cancer-specific mortality (CSM) risk was constructed. The accuracy and discrimination of the nomogram were assessed using receiver operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve using the training and testing sets, respectively. A total of 28,430 TNBC patients were randomly grouped into the training set (n=19,900) and the testing set (n=8,530). The median time for follow-up was 59 [1-107] months. A total of 7,014 (24.67%) patients died, among whom 4,801 (68.45%) died from breast cancer and 2,213 (31.55%) due to non-breast cancer events. The independent risk factors were primary site of tumor, grade, tumor-node-metastasis (TNM) stage, T stage, approach of surgery, chemotherapy, axillary lymph node metastases, brain metastases, and liver metastases. The prediction nomogram was constructed by using the aforementioned variables. The 1-, 3-, and 5-year AUC of CSM were predicted to be 0.856, 0.81, and 0.782, respectively, in the training set, and 0.856, 0.81, and 0.782 in the testing set, respectively. The C-index of the nomogram was 0.801 and 0.799 in the training and testing sets, respectively. As confirmed by the validation and training calibration curves, the nomogram was consistent with the results. We used competing risk models to identify risk factors for CSM and constructed a CSM risk prediction nomogram for TNBC patients, that may be utilized to predict CSM risk in TNBC patients clinically and assist in the creation of individualised clinical treatment options.

Solid Papillary Carcinoma: A Clinicopathological Evaluation of a Rare Variant of Carcinoma Breast

Background: Solid papillary carcinoma (SPC) of the breast is considered a low grade carcinoma with a favorable prognosis. We aimed to describe clinicopathologic features including immunohistochemistry expression profile and to delineate adverse prognostic features, if any.&#x0D; Methods: Patients diagnosed with SPC between the years 2016–2022 were retrospectively identified from the archives at Kailash Cancer Hospital and Research Centre, Goraj. Microscopic slides and clinical history were reviewed. Immunohistochemical stains were performed.&#x0D; Results: Of 12 SPCs cases retrieved, 11 (92%) were associated with ductal carcinoma in situ (DCIS). The median tumour size was 1.6cm (range 1.3-7.0cm). All tumors were positive for hormone receptor expression and negative for HER-2Neu. 6 cases (50%) show neuroendocrine differentiation. Lymph node involvement was identified in 4/12 (33.3%) patients. Of 12 patients with outcome data (median follow-up 30 months, range 7-72 months), none (0%) developed local recurrence. No distant metastasis or deaths were observed.&#x0D; Conclusions: SPCs has excellent prognosis with no local recurrence or distant metastasis in this study. Axillary node metastasis is found to be associated with larger tumor size, however it appears to have no impact on disease free or overall survival.

Identification and validation of DNA methylation markers to predict axillary lymph node metastasis of breast cancer.

Axillary lymph node metastasis (ALNM) is one of the most important prognostic factors for breast cancer patients, and DNA methylation is involved in ALNM of breast cancer. However, the methylation profile of breast cancer ALNM remains unknown. Breast cancer tissues were collected from patients with and without ALNM. We investigated the genome-wide DNA methylation profile in breast cancer with and without ALNM using reduced representation bisulfite sequencing (RRBS). Then, differentially methylated regions (DMRs) were verified by targeted bisulfite sequencing. A total of 21491 DMRs were identified between the lymph node positive group and negative group. Compared to the LN-negative breast cancer, LN-positive breast cancer had 10,920 hypermethylated DMRs and 10,571 hypomethylated DMRs. Then, 10 DMRs in the gene promoter region were detected by targeted bisulfite sequencing, these gene included HOXA5, PTOV1-AS1, RHOF, PAX6, GSTP1, RASGRF2, AKR1B1, BNIP3, CRMP1, ING5. Compared with negative lymph node, the promoter methylation levels of RASGRF2, AKR1B1 and CRMP1 increased in positive lymph node, while the promoter methylation level of RHOF decreased in positive lymph node. In addition, Cancer Genome Atlas (TCGA) data showed that RASGRF2, AKR1B1 and CRMP1 were low expressed in breast Cancer tissues, while RHOF was high expressed in breast Cancer tissues. Furthermore, in addition to highly methylated AKR1B1, RASGRF2 and CRMP1 gene promoters, BNIP3, GSTP1, HOXA5 and PAX6 gene promoters were also methylated in ER-positive and HER2-negative breast cancer with ALNM. When compared to negative lymph node breast cancer, the positive lymph node breast cancer has a differential methylation status. Promoter methylation of RASGRF2, AKR1B1, CRMP1 and RHOF in lymph node positive breast cancer tissues was significantly different from that in lymph node negative breast cancer tissues. AKR1B1, RASGRF2, CRMP1, BNIP3, GSTP1, HOXA5 and PAX6 genes were methylated in ER-positive and HER2-negative breast cancer with ALNM. The study provides an important biological base for understanding breast cancer with ALNM and developing therapeutic targets for breast cancer with ALNM.

The molecular profile of breast cancer: primary tumor versus corresponding lymph node metastases.

Breast cancer (BrCa) is the most frequent malignancy in female, and lymph node metastases (LNM) is an important prognostic and therapeutic parameter. The molecular classification is nowadays largely applied to characterize the primary tumors, but few studies focused on the comparison between the molecular profiles of the primary with corresponding LNM. In the current work, we investigated the expression of conventional markers used by molecular classification in both primary tumors and axillary LNM. A series of 156 patients with BrCa was investigated, and from these 80 cases showed LNM. After routine pathological investigation, including the histopathological form and grade, we performed additional step sections from the primary and lymph nodes for immunohistochemistry. All procedures for hormone receptors, human epidermal growth factor receptor 2 (HER2), Ki67, cytokeratin 5 (CK5), epidermal growth factor receptor (EGFR), p53, E-cadherin, and B-cell leukemia∕lymphoma-2 (Bcl-2) were performed using the standard automated procedures. We found significant differences between the primary tumors and corresponding LNM in luminal A, luminal B, and basal-like carcinoma. No phenotypical interconversions were noticed in HER2 and unclassified BrCa. Our data demonstrate that in almost 20% of the cases the molecular profile of the primary does not overlap with aspects found in the lymph nodes. Our results strongly suggest performing the molecular classification in both primary tumors and in LNM. Current data suggest that the application of this diagnostic procedure will significantly influence the therapeutic strategy.

Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer

Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images. The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared. The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic. This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.

Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases

BackgroundEarly identification of axillary lymph node metastasis (ALNM) in breast cancer (BC) is still a clinical difficulty. There is still no good method to replace sentinel lymph node biopsy (SLNB). The purpose of our study was to develop and validate a nomogram to predict the probability of ALNM preoperatively based on ultrasonography (US) and clinicopathological features of primary tumors.MethodsFrom September 2019 to April 2022, the preoperative US) and clinicopathological data of 1076 T1-T2 BC patients underwent surgical treatment were collected. Patients were divided into a training set (875 patients from September 2019 to October 2021) and a validation set (201 patients from November 2021 to April 2022). Patients were divided into positive and negative axillary lymph node (ALN) group according pathology of axillary surgery. Compared the US and clinicopathological features between the two groups. The risk factors for ALNM were determined using multivariate logistic regression analysis, and a nomogram was constructed. AUC and calibration were used to assess its performance.ResultsBy univariate and multivariate logistic regression analysis, age (p = 0.009), histologic grades (p = 0.000), molecular subtypes (p = 0.000), tumor location (p = 0.000), maximum diameter (p = 0.000), spiculated margin (p = 0.000) and distance from the skin (p = 0.000) were independent risk factors of ALNM. Then a nomogram was developed. The model was good discriminating with an AUC of 0.705 and 0.745 for the training and validation set, respectively. And the calibration curves demonstrated high agreement. However, in further predicting a heavy nodal disease burden (> 2 nodes), none of the variables were significant.ConclusionThis nomogram based on the US and clinicopathological data can predict the presence of ALNM good in T1-T2 BC patients. But it cannot effectively predict a heavy nodal disease burden (> 2 nodes).

Lymphatic metastasis-associated circRNA‒miRNA‒mRNA network for exploring the pathogenesis and therapeutic target of triple negative breast cancer based on whole-transcriptome sequencing analysis: an experimental verification study

BackgroundThe metastatic mechanisms of axillary lymph nodes (ALNs) in triple-negative breast cancer (TNBC) remain unclear. We aimed to identify the potential circRNA regulatory network in ALN metastasis.MethodsWe performed whole transcriptome sequencing (WTS) to determine the expression profiles of RNAs and screen out differentially expressed messenger RNAs (DEMs), microRNAs (DEMis), and circRNAs (DECs) between ALN-positive and ALN-negative TNBC patients. Functional enrichment analysis and Kaplan–Meier survival analysis were utilized to unearth the potential regulatory mechanisms of the DEMs. A competing endogenous RNA (ceRNA) network was constructed using computational biology. The expression levels of DECs in cell lines were confirmed by real-time polymerase chain reaction (RT‒PCR).ResultsFollowing WTS and differential expression analysis, 739 DEMs, 110 DEMis, and 206 DECs were identified between ALN-positive and ALN-negative TNBC patients. Functional analysis indicated that the DEMs mainly functioned in carcinogenesis and tumor progression-related pathways. ceRNA networks containing eight circRNAs, six miRNAs, and eighteen mRNAs were developed. In the ceRNA network, two mRNAs (RAB3D and EDARADD) that were significantly associated with better overall survival and one mRNA (GSR) that predicted favorable recurrence-free survival in TNBC patients were chosen for further analysis. Then, a survival-related ceRNA network containing two DECs (hsa_circ_0061260 and hsa_circ_0060876), two DEMis (hsa-miR-5000-3p and hsa-miR-4792), and three mRNAs (GSR, RAB3D, and EDARADD) was identified. Then, two candidate DECs were validated by real-time PCR.ConclusionOur research constructed a ceRNA network that provides novel insights into the molecular mechanism of ALN metastasis and potential therapeutic targets in TNBC.

Is Pathologic Axillary Staging Valid If Lymph Nodes Are Less than 10 with Axillary Lymph Node Dissection after Neoadjuvant Chemotherapy?

Introduction: The aim of this study was to evaluate the prognostic value of the number of lymph nodes removed in breast cancer patients who undergo axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC). Methods: We included patients who were diagnosed with invasive breast cancer and cytology with proven involved axillary node metastasis at diagnosis and treated with NAC followed by curative surgery at Samsung Medical Center between January 2007 and December 2015. The primary outcomes were disease-free survival (DFS) and overall survival (OS). Results: Among 772 patients with NAC and ALND, there were 285 ypN0, 258 ypN1, 135 ypN2, and 94 ypN3 cases. The median follow-up duration was 69.0 months. The group with less than 10 lymph nodes number (<10 nodes group) included 123 patients and the group with 10 or more lymph nodes number (≥10 nodes group) included 649 patients. There were no significant differences in DFS (p = 0.501) or OS (p = 0.883) between the two groups. In the ypN0 subgroup, the <10 nodes group had worse DFS than ≥10 nodes group (p = 0.024). In the ypN1 subgroup, there were no significant differences in DFS (p = 0.846) or OS (p = 0.774) between the two groups. In the ypN2 subgroup, the <10 nodes group had worse DFS (p = 0.025) and OS (p = 0.031) than ≥10 nodes group Conclusion: In ypN0 and ypN2 subgroups, breast cancer patients with less than 10 lymph nodes number in ALND after NAC might be considered for additional staging or closer surveillance when compared to patients with 10 or more than lymph node.

A Systematic Review and Meta-analysis of Touch Imprint Cytology and Frozen Section Biopsy and Their Comparison for Evaluation of Sentinel Lymph Node in Breast Cancer.

Evaluation of axillary lymph nodes after sentinel lymph node biopsy (SLNB) in breast cancer is mostly done by intra-operative frozen section biopsy (FSB) and/ or touch imprint cytology (TIC). In this systematic review and meta-analysis, we have compared the accuracy of the two modalities. PubMed, EMBASE, and Cochrane electronic databases were searched for articles comparing TIC with FSB. Articles were assessed for methodological and reporting quality. The main summary measures were pooled sensitivity, pooled specificity, and diagnostic accuracy using bivariate generalized linear mixed models using random effects. Fourteen studies were included. The pooled sensitivity, specificity, and diagnostic accuracy for FSB were 78%, 100%, and 98.57%. For TIC, the pooled sensitivity, specificity, and diagnostic accuracy were 74%, 98%, and 98.37%. For both methods, visual inspection of summary ROC curves and of forest plots did not show significant heterogeneity. TIC showed comparable sensitivity, specificity, and accuracy to FSB and hence can be used as its substitute as a rapid and economical test for the detection of axillary lymph node metastasis during SLNB especially in low-resource settings.

Patterns of regional recurrence according to molecular subtype in patients with pN2 breast cancer treated with limited field regional irradiation.

There is little evidence regarding the radiotherapy modification based on molecular subtypes in breast cancer. This study aimed to identify the risk and patterns of regional recurrence according to molecular subtype in patients with pN2 breast cancer. We identified 454 patients who underwent radical surgery for breast cancer with 4-9 axillary lymph node metastases. All patients underwent axillary lymph node dissection, adjuvant chemotherapy and limited-field regional nodal irradiation. The rates and patterns of regional recurrence were compared between the following three subgroups: luminal type (estrogen receptor- and/or progesterone receptor-positive), HER2-type (estrogen receptor- and progesterone receptor-negative and HER2-positive) and triple-negative type (estrogen receptor-, progesterone receptor- and HER2-negative). Regional recurrence occurred in 18/454 patients (4%). The risk of regional recurrence was higher in the triple-negative (hazard ratio 7.641) and HER2-type (hazard ratio 4.032) subtypes than in the luminal subtype. The predominant pattern of regional recurrence was inside the radiotherapy field in triple-negative breast cancer and outside the radiotherapy field in HER2-type and luminal-type cancers. In patients with pN2 breast cancer, the risk of regional recurrence was higher in the triple-negative and HER2-type than in the luminal type. In-field recurrence was predominant in triple-negative cancer, while out-field recurrence was frequent in luminal and HER2-type breast cancers.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy.

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Clinical Trials of Curative Partial Breast Irradiation for Early-Stage Breast Cancer Using Carbon Ion Radiotherapy

<h3>Purpose/Objective(s)</h3> Our institute conducted clinical trials of curative partial breast irradiation using carbon ion radiotherapy (CIRT) to patients with stage 0 and I breast cancer since 2013. The purpose of this report is to evaluate treatment outcomes up to now. <h3>Materials/Methods</h3> To date, we have conducted three clinical trials and one advanced medical treatment (NIRS9404: AMT). The first study (UMIN000010848: known as Breast I) was 4 fractions partial breast CIRT for stage I luminal patients aged 60 years or older. The AMT protocol was provided in parallel with Breast I in same manner for stage I breast cancer patients who was difficult to register due to minor variant or reject to participate in Breast I. In principle, these patients received maintenance hormone therapy. The next clinical trial (UMIN000029478: known as Breast II) enrolled patients aged 20 years and older without 2 mm or more metastasis in sentinel lymph node for all types of Tis-1N0M0, tumor size 2 cm or less. In this study, 4 fractions CIRT partial breast irradiations were combined with standard systemic therapy and x-ray whole-breast irradiation. And the latest clinical trial (UMIN000046859: known as Breast III) is a single dose of CIRT for luminal Tis-1N0M0 patients aged 50 years and older. All Breast III patients received maintenance hormone therapy. In all trials, Phase I was a dose-escalation study and Phase II was treated with the recommended dose determined in Phase I. <h3>Results</h3> By December 2021, 56 cases have been enrolled. A total of 19 were enrolled In Breast I, 7 in Phase I, 12 in Phase II with a recommended dose of 60.0 Gy (RBE), and total of 14 were enrolled in AMT. A total of 17 were enrolled In Breast II, 5 in Phase I, 12 in Phase II with a recommended dose of 60.0 Gy (RBE). A total of 6 were enrolled in Breast III, 3 in Phase I, 3 in Phase II with a recommended dose of 50.0 Gy (RBE). As acute adverse effects, acute radiation dermatitis (grade 2 in 1, Grade1 in 37), mastitis (grade 1 in 6) and myositis (grade 1 in 7) were observed. As late adverse effects, grade 3 pneumonia of unknown causality were observed in 1 case. The cosmetic results of breast were excellent in all cases. Tumor disappearance after CIRT took 3 to 24 months. Local recurrence with axillary lymph node metastasis was observed in one high-risk AMT patient who refused any systemic therapy, but no other recurrences or metastases have been observed. <h3>Conclusion</h3> CIRT for early-stage breast cancer is expected to be effective with mild adverse events, and it is considered meaningful to continue research.

CircRNA expression profiles of breast cancer and construction of a circRNA-miRNA-mRNA network

CircRNAs are a group of endogenous small noncoding RNAs that are involved in multiple diseases including cancers. At present, the functions of circRNAs in breast cancer need to be further explored. In this study, 3 pairs of breast cancer and paracancer tissues with axillary lymph node metastasis were collected for circRNA high-throughput sequencing. We have identified 17,966 distinct circRNA candidates. Significant differential expressions were found in 136 circRNAs in breast cancer tissues relative to the matched paracancer tissues. We aslo identified differentially expressed 156 miRNAs and 1105 mRNAs in breast cancer tissues and normal breast tissues from public databases. Then we constructed a regulatory ceRNA network. 12 mRNAs were associated with prognosis of breast cancer. We also constructed a circRNAs-mediated subnetwork which might be related to prognosis of breast cancer. This article provides a better understanding of circRNAs-mediated ceRNA regulatory network by which circRNAs compete for endogenous RNAs in breast cancer.

A Clinical-Radiomics Model of Preoperative Prediction IMN Status of Breast Cancer Patients

<h3>Purpose/Objective(s)</h3> To establish a prediction model based on clinicopathological factors and dynamic contrast enhanced (DCE)-MRI radiomics features, and to make a non-invasive prediction of IMN metastasis risk before operation. <h3>Materials/Methods</h3> The DCE-MR images and clinicopathological data of 124 breast cancer patients who had undergone internal mammary sentinel lymph node biopsy and / or internal mammary lymph node (IMN)dissection with IMN negative on preoperative images from January 2013 to December 2019 were obtained. All patients received plain breast scan and dynamic contrast-enhanced MR in Shandong Cancer Hospital before operation. In this study, three types of predictive models were established, which are based on clinicopathological risk factors, DCE-MRI imaging features and the combination of the two: 1) Clinical model: a logistic regression model based on clinicopathological factors was developed to predict ALN status. 2) Radiomics model: radiomics model based on radiomics score (RS) score is developed. 3) Clinical-radiomics model: combining the imaging features with the independent risk factors for independent prediction of IMN status, the multiple logistic regression method was used to establish a nomogram for predicting the risk of IMN metastasis. All models were evaluated by ROC and AUC, and clinical decision analysis curves was used to evaluate the clinical application value of the model. <h3>Results</h3> Three variables including primary tumor location, PR status and axillary lymph node metastasis status were included in the clinical model, and the AUC was 0.913. 150 imaging features were extracted from DCE-MRI images of primary tumors. Finally, 4 imaging features were selected to participate in the development of RS. Finally, the AUC of the imaging model combined with DCE-MRI imaging features of IMN was 0.831. The clinical-radiomics model with an AUC of 0.964. Delong test showed that, there was no significant difference in ROC curve between clinical model and radiomics model (p=0.1337) , but there were significant differences in ROC curve between clinical model and clinical-radiomics model, and between clinical model and clinical-radiomics model (p<0.05). DCA curve analysis shows that the application of clinical-radiomics model has the greatest net benefit, optimal predictive efficiency and better distinguishing ability, which is better than clinical model and radiomics model. <h3>Conclusion</h3> Clinical-radiomics model is helpful to evaluate the IMN metastasis status of breast cancer patients before operation.

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer.

To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer. A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors. Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature. Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis.

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported. To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer. Retrospective. One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer. Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T. The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed. Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant. The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups,and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and βmean achieved the highest performance (AUC=0.702) to discriminate the Luminal and HER2-positive subtypes. Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer. 4 TECHNICAL EFFICACY: Stage 2.