Ultrasound as a replacement for physical examination in clinical staging of axillary lymph nodes in breast cancer patients

Nomogram based on multiparametric analysis of early-stage breast cancer: Prediction of high burden metastatic axillary lymph nodes

BACKGROUND

The Z0011 and AMAROS trials found that axillary lymph node dissection (ALND) was no longer mandatory for early-stage breast cancer patients who had one or two metastatic axillary lymph nodes (mALNs). The aim of our study was to establish a nomogram which could be used to quantitatively predict the individual likelihood of high burden mALN (≥3 mALN).

METHODS

We retrospectively analyzed 564 women with early breast cancer who had all undergone both ultrasound (US) and magnetic resonance imaging (MRI) to examine axillary lymph nodes before radical surgery. All the patients were divided into training (n = 452) and validation (n = 112) cohorts by computer-generated random numbers. Their clinicopathological features and preoperative imaging associated with high burden mALNs were evaluated by logistic regression analysis to develop a nomogram for predicting the probability of high burden mALNs.

RESULTS

Multivariate analysis showed that high burden mALNs were significantly associated with replaced hilum and the shortest diameter >10 mm on MRI, with cortex thickness >3 mm on US (p < 0.05 each). These imaging criteria plus higher grade (grades II and III) and quadrant of breast tumor were used to develop a nomogram calculating the probability of high burden mALNs. The AUC of the nomogram was 0.853 (95% CI: 0.790-0.908) for the training set and 0.783 (95% CI: 0.638-0.929) for the validation set. Both internal and external validation evaluated the accuracy of nomogram to be good.

CONCLUSION

A well-discriminated nomogram was developed to predict the high burden mALN in early-stage breast patients, which may assist the breast surgeon in choosing the appropriate surgical approach.

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

Background

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).

Methods

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.

Results

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).

Conclusions

Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.

Axillary Imaging Following a New Invasive Breast Cancer Diagnosis-A Radiologist's Dilemma

Traditionally, patients with newly diagnosed invasive breast cancer underwent axillary US to assess for suspicious axillary lymph nodes (LNs), which were then targeted for image-guided needle biopsy to determine the presence of metastasis. Over the past decade, there has been a shift towards axillary preservation. For patients with palpable lymphadenopathy, the decision to perform axillary imaging with documentation of the number and location of abnormal LNs in preparation for image-guided LN sampling is straightforward. Since LN involvement correlates with cancer size, it is reasonable to image the axilla in patients with tumors larger than 5 cm; however, for tumors smaller than 5 cm, axillary imaging is often deferred until after the tumor molecular subtype and treatment plan are established. Over the last decade, neoadjuvant chemotherapy (NACT) is increasingly used for smaller cancers with more aggressive molecular subtypes. In most cases, detecting axillary metastasis is critical when deciding whether the patient would benefit from NACT. There is increasing evidence that abnormal axillary US findings correlates with LN metastases and reliably establishes a baseline to monitor response to NACT. Depending on hormone receptor status, practices may choose to image the axilla in the setting of clinical stage T1 and T2 cancers to evaluate nodal status and help determine further steps in care. Radiologists should understand the nuances of axillary management and the scope and challenges of LN marking techniques that significantly increase the precision of limited axillary surgery.

Clinical Value of Three Combined Ultrasonography Modalities in Predicting the Risk of Metastasis to Axillary Lymph Nodes in Breast Invasive Ductal Carcinoma

Objective

The present study aimed to assess the clinical value of conventional ultrasound (C-US), ultrasound elastography (UE), percutaneous contrast-enhanced ultrasound (P-CUES), and the combination of these three ultrasonography modalities for evaluating the risk of axillary lymph node (ALN) metastasis in breast invasive ductal carcinoma (IDC).

Methods

This retrospective analysis included 120 patients with pathologically confirmed IDC who underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Based on the gold standard of postoperative pathology, ALN pathology results were evaluated and compared with findings obtained using C-US, UE, P-CUES, and the three modalities combined.

Results

(1) There was a statistically significant difference between the histological grade of the tumor and the pathological condition of ALNs. (2) The difference between C-US parameters and UE score were statistically significant. The accuracy of P-CEUS localization of SLNs was 100% (96/96) when compared with localization guided by methylene blue. The difference in the distribution of the four SLN enhancement patterns was statistically significant. (3) The sensitivity, specificity, positive predictive value, and negative predictive value of C-US and UE were 75%, 71%, 58%, and 89%, and 71%, 72%, 50%, and 86%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of P-CUES were 91%, 82%, 78%, 92%, respectively. When all three modalities were combined, the sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 89%, 86%, and 95%, respectively. In the detection of ALN metastasis, there was a good correlation between histopathological results and evaluations based on the three combined ultrasonography modalities (kappa: 0.82, p<0.001).

Conclusions

When compared to C-US, UE, or P-CEUS alone, the combination of the three ultrasonography modalities was found to be superior in distinguishing metastatic and non-metastatic ALNs. This combined strategy may aid physicians in determining the most appropriate approach to ALN surgery as well as the prognosis of breast IDC.

Ultrasonographic Algorithm for the Assessment of Sentinel Lymph Nodes That Drain the Mammary Carcinomas in Female Dogs

The status of sentinel lymph nodes (SLNs) is decisive in staging, prognosis, and therapeutic approach. Using an ultrasonographic examination algorithm composed of B-mode, Doppler technique, contrast-enhanced ultrasound (CEUS) and elastography, this study aimed to determine the diagnostic performance of the four techniques compared to histopathological examination. 96 SLNs belonging to 71 female dogs with mammary gland carcinomas were examined. After examinations, mastectomy and lymphadenectomy were performed. Histopathological examination confirmed the presence of metastases in 54 SLNs. The elasticity score had the highest accuracy-89.71%, identifying metastases in SLNs with 88.9.9% sensitivity (SE) and 90.5% specificity (SP), ROC analysis providing excellent results. The S/L (short axis/long axis) ratio showed 83.3% SE and 78.6% SP as a predictor of the presence of metastases in SLN having a good accuracy of 81.2%. On Doppler examination, the resistivity index(RI) showed good accuracy of 80% in characterizing lymph nodes with metastases versus unaffected ones; the same results being obtained by CEUS examination. By assigning to each ultrasonographic parameter a score (0 or 1) and summing up the scores of the four techniques, we obtained the best diagnostic performance in identifying lymph node metastases with 92.2% accuracy. In conclusion, the use of the presented algorithm provides the best identification of metastases in SLNs, helping in mammary carcinoma staging and appropriate therapeutic management.

A meta-analysis of contrast-enhanced spectral mammography versus MRI in the diagnosis of breast cancer

Background

To identify the performance of contrast‐enhanced spectral mammography (CESM) and magnetic resonance imaging (MRI) for breast cancer diagnosis by pooling the open published data.

Methods

A systematic review of studies relevant to CESM and MRI in the diagnosis of breast cancer were screened in the electronic databases of Pubmed, EMBASE, the Cochrane Library, Web of Science, Google scholar and CNKI. The methodical quality of the included publications was evaluated by the quality assessment of diagnostic accuracy studies‐2 (QUADAS‐2). The diagnostic sensitivity, specificity and area under the ROC curve (AUC) were pooled and the true positive (TP), false positive (FP), false negative (FN) and true negative (TN) of the original studies were calculated.

Results

A total of 13 diagnostic publications were identified and included in the meta‐analysis. Of those included, five were retrospective studies and the remaining eight were prospective work. The combined data indicating the pooled sensitivity and specificity of CESM and MRI were 0.97 (95% CI: 0.95–0.98), 0.66 (95% CI: 0.59–0.71), 0.97 (95% CI: 0.95–0.98),and 0.52 (95% CI: 0.46–0.58), respectively. The pooled +LR and –LR for CESM were 2.70 (95% CI: 1.57–4.65), 0.06 (95% CI: 0.04–0.09), and 2.01 (95% CI: 1.78–2.26), 0.08 (95% CI: 0.05–0.11) for MRI, respectively. For the diagnostic odds ratio (DOR), the pooled results of CESM and MRI were 60.15 (95% CI: 24.72–146.37) and 31.34 (95% CI: 19.61–50.08), respectively. The AUC of the symmetric receiver operating characteristic curve (SROC) was 0.9794 and 0.9157 for CESM and MRI, respectively, calculated using the Moses model in the diagnosis of breast cancer.

Conclusions

Both CESM and MRI are effective methods for the detection of breast cancer with high diagnostic sensitivity. The diagnostic performance of CESM appears to be more effective than MRI.

Development of a nine-lncRNA signature as a novel prognostic marker of estrogen receptor-negative breast cancer

Long non-coding RNAs (lncRNAs) have been demonstrated to be aberrantly expressed in several types of tumor, and dysregulated lncRNAs are suggested to play a prognostic role in breast cancer (BC). Estrogen receptor (ER) status is a prognostic factor in patients with ER-negative BC, which is associated with poor prognosis. Thus, the present study developed a prognostic lncRNA signature specifically for ER-negative BC, in order to predict the risk of post-surgery relapse and improve patient prognosis. A gene expression profile containing 1,631 lncRNAs was obtained by investigating and integrating publicly available cohorts of BC. Subsequently, a nine-lncRNA signature was developed and validated in two independent cohorts via the Cox regression model. Using the nine-lncRNA signature, patients in the discovery cohort were divided into high- and low-risk groups, with significantly different disease-free survival [DFS; hazard ratio (HR)=2.718, 95% confidence interval (CI)=2.115-3.494, P<0.0001]. Receiver operating characteristic curve analyses demonstrated that the area under the curve reached 0.908. Similar results were obtained in the two independent cohorts (HR=1.499, 95% CI=0.950-2.365, P=0.04; HR=1.262, 95% CI=1.056-1.510, P=0.01), respectively. Furthermore, the nine lncRNAs were demonstrated to play important roles in the cell invasion and metastasis of different types of tumor. The differentially expressed genes (DEGs) identified between the high- and low-risk groups were consistently high in the discovery and validation cohorts. Functional analysis indicated that these DEGs, as well as genes co-expressed with the nine lncRNAs, were involved in cancer-associated signaling pathways, all of which provide further evidence for the predictive ability of the nine-lncRNA signature. Overall, the present study developed a novel prognostic biomarker for ER-negative BC.