Development and Validation of a Preoperative Scoring System to Distinguish Between Nonadvanced and Advanced Axillary Lymph Node Metastasis in Patients With Early-stage Breast Cancer

Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics

BACKGROUND

The axillary lymph-node metastatic burden is closely associated with treatment decisions and prognosis in breast cancer patients. This study aimed to explore the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT)-based radiomics in combination with ultrasound and clinical pathological features for predicting axillary lymph-node metastatic burden in breast cancer.

METHODS

A retrospective analysis was conducted and involved 124 patients with pathologically confirmed early-stage breast cancer who had undergone 18F-FDG PET/CT examination. The ultrasound, PET/CT, and clinical pathological features of all patients were analysed, and radiomic features from PET images were extracted to establish a multi-parameter predictive model.

RESULTS

The ultrasound lymph-node positivity rate and PET lymph-node positivity rate in the high nodal burden group were significantly higher than those in the low nodal burden group (χ2 = 19.867, p < 0.001; χ2 = 33.025, p < 0.001). There was a statistically significant difference in the PET-based radiomics score (RS) for predicting axillary lymph-node burden between the high and low lymph-node burden groups. (-1.04 ± 0.41 vs. -1.47 ± 0.41, t = -4.775, p < 0.001). The ultrasound lymph-node positivity (US_LNM) (odds ratio [OR] = 3.264, 95% confidence interval [CI] = 1.022-10.423), PET lymph-node positivity (PET_LNM) (OR = 14.242, 95% CI = 2.960-68.524), and RS (OR = 5.244, 95% CI = 3.16-20.896) are all independent factors associated with high lymph-node burden (p < 0.05). The area under the curve (AUC) of the multi-parameter (MultiP) model was 0.895, which was superior to those of US_LNM, PET_LNM, and RS models (AUC = 0.703, 0.814, 0.773, respectively), with statistically significant differences (Z = 2.888, 3.208, 3.804, respectively; p = 0.004, 0.002, < 0.001, respectively). Decision curve analysis indicated that the MultiP model provided a higher net benefit for all patients.

CONCLUSION

A MultiP model based on PET-based radiomics was able to effectively predict axillary lymph-node metastatic burden in breast cancer.

TRIAL REGISTRATION

This study was registered with ClinicalTrials.gov (registration number: NCT05826197) on May 7, 2023.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients

BACKGROUND

Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer.

METHODS

The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard.

RESULTS

Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702.

CONCLUSION

Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

BACKGROUND

Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

METHODS

From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.

RESULTS

NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).

CONCLUSIONS

The ML model XGBoost can well predict NSLNM in breast cancer patients.

Clinical significance of discordances in sentinel lymph node reactivity between radioisotope and indocyanine green fluorescence in patients with cN0 breast cancer

BACKGROUND

/Objective: To evaluate the usefulness of combining radioisotopes (RI) and indocyanine green (ICG) and investigate discordances in sentinel lymph node (SN) reactivity using each tracer in cN0 breast cancer patients.

METHODS

In total, 338 cN0 primary breast cancer patients who underwent SN biopsy with RI and ICG and axillary lymph node (ALN) dissection were included. SN positivity with RI, ICG, and a combination of RI and ICG was denoted as SN(RI), SN(ICG), and SN(RI+ICG), respectively. We retrospectively estimated metastatic SN detection rates, each method's discordance rate, and the correlation of discordances in SN reactivity with postoperative N staging.

RESULTS

The combination of RI and ICG had higher metastatic SN detection rates (99.7%) than RI or ICG alone (91.7% and 96.4%, respectively; p < 0.01). The discordance rate between SN(RI) and SN(ICG) in detecting metastatic SNs was 11.3% (38/337 cases). The absence of SN(RI), cT stage (cT2-3), higher histological grade (G3), and histological type (special type) were identified as risk factors of pN2-3 disease (odds ratios: 8.64, 2.56, 1.92, and 3.28, respectively; p < 0.01).

CONCLUSION

Discordances in SN reactivity between RI and ICG helps in identifying SN metastasis. Although the absence of SN(RI) is rare, it is a significant sign of advanced ALN metastases. The findings of our study indicate that ALN dissection should be considered for accurate nodal staging in such cases.

Development and validation of a pre- and intra-operative scoring system that distinguishes between non-advanced and advanced axillary lymph node metastasis in breast cancer with positive sentinel lymph nodes: a retrospective study

Background

There are currently no scoring-type predictive models using only easily available pre- and intraoperative data developed for assessment of the risk of advanced axillary lymph node metastasis (ALNM) in patients with breast cancer with metastatic sentinel lymph nodes (SLNs). We aimed to develop and validate a scoring system using only pre- and intraoperative data to distinguish between non-advanced (≤ 3 lymph nodes) and advanced (> 3 lymph nodes) ALNM in patients with breast cancer with metastatic SLNs.

Methods

We retrospectively identified 804 patients with breast cancer (cT1-3cN0) who had metastatic SLNs and had undergone axillary lymph node dissection (ALND). We evaluated the risk factors for advanced ALNM using logistic regression analysis and developed and validated a scoring system for the prediction of ALNM using training (n = 501) and validation (n = 303) cohorts, respectively. The predictive performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration plots.

Results

Ultrasound findings of multiple suspicious lymph nodes, SLN macrometastasis, the ratio of metastatic SLNs to the total number of SLNs removed, and the number of metastatic SLNs were significant risk factors for advanced ALNM. Clinical tumor size and invasive lobular carcinoma were of borderline significance. The scoring system based on these six variables yielded high AUCs (0.90 [training] and 0.89 [validation]). The calibration plots of frequency compared to the predicted probability showed slopes of 1.00 (training) and 0.85 (validation), with goodness-of-fit for the model. When the cutoff score was set at 4, the negative predictive values (NPVs) of excluding patients with advanced ALNM were 96.8% (training) and 96.9% (validation). The AUC for predicting advanced ALNM using our scoring system was significantly higher than that predicted by a single independent predictor, such as the number of positive SLNs or the proportion of positive SLNs. Similarly, our scoring system also showed good discrimination and calibration ability when the analysis was restricted to patients with one or two SLN metastases.

Conclusion

Our easy-to-use scoring system can exclude advanced ALNM with high NPVs. It may contribute to reducing the risk of undertreatment with adjuvant therapies in patients with metastatic SLNs, even if ALND is omitted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02779-9.

Apparent regional differences in the spectrum of BARD1 pathogenic variants in Spanish population and importance of copy number variants

Only up to 25% of the cases in which there is a familial aggregation of breast and/or ovarian cancer are explained by germline mutations in the well-known BRCA1 and BRCA2 high-risk genes. Recently, the BRCA1-associated ring domain (BARD1), that partners BRCA1 in DNA repair, has been confirmed as a moderate-risk breast cancer susceptibility gene. Taking advantage of next-generation sequencing techniques, and with the purpose of defining the whole spectrum of possible pathogenic variants (PVs) in this gene, here we have performed a comprehensive mutational analysis of BARD1 in a cohort of 1946 Spanish patients who fulfilled criteria to be tested for germline pathogenic mutations in BRCA1 and BRCA2. We identified 22 different rare germline variants, being 5 of them clearly pathogenic or likely pathogenic large deletions, which account for 0.26% of the patients tested. Our results show that the prevalence and spectrum of mutations in the BARD1 gene might vary between different regions of Spain and expose the relevance to test for copy number variations.

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference. Google Cloud AutoML Vision (Mountain View, CA) was used for AI image classification. Three experienced radiologists also classified the images and gave a level of suspicion score (1-5). To test the accuracy of AI, an external testing dataset of 64 images from 64 independent patients was evaluated by three AI models and the three readers. The diagnostic performance of AI and the humans were then quantified using receiver operating characteristics curves. In the complete set of 317 images, AutoML achieved a sensitivity of 77.1%, positive predictive value (PPV) of 77.1%, and an area under the precision recall curve of 0.78, while the three radiologists showed a sensitivity of 87.8% ± 8.5%, specificity of 50.3% ± 16.4%, PPV of 61.1% ± 5.4%, negative predictive value (NPV) of 84.1% ± 6.6%, and accuracy of 67.7% ± 5.7%. In the three external independent test sets, AI and human readers achieved sensitivity of 74.0% ± 0.14% versus 89.9% ± 0.06% (p = .25), specificity of 64.4% ± 0.11% versus 50.1 ± 0.20% (p = .22), PPV of 68.3% ± 0.04% versus 65.4 ± 0.07% (p = .50), NPV of 72.6% ± 0.11% versus 82.1% ± 0.08% (p = .33), and accuracy of 69.5% ± 0.06% versus 70.1% ± 0.07% (p = .90), respectively. These preliminary results indicate AI has comparable performance to trained radiologists and could be used to predict the presence of metastasis in ultrasound images of axillary lymph nodes.

Double heterozygosity for TP53 and BRCA1 mutations: clinical implications in populations with founder mutations

PURPOSE

The co-occurrence or double heterozygosity of pathogenic/likely pathogenic sequence variants (P/LPSVs) in major cancer susceptibility genes has rarely been reported. Such co-occurrence raises the issues of accurate genetic counseling, preferred recommended surveillance scheme, and the use of preimplantation genetic diagnosis (PGD).

METHODS

A clinical report of an Ashkenazi Jewish (AJ) family with co occurrence of two PSVs in BRCA1 and TP53 and a literature search.

RESULTS

In an AJ family with a substantial history of cancer limited to the maternal side, two siblings co-harbored TP53 (c.733C>A; p.G245S) and the predominant 5266dup BRCA1 mutation, originating from the mother and the father, respectively. PGD is ongoing. Four families were thus far reported as double heterozygotes for both BRCA1/BRCA2 and TP53. Based on the limited available data, it seems that the phenotype in double PSV heterozygotes is not more severe than in single PSV carrier in either gene.

CONCLUSIONS

This family highlights the need to genotype both parents, especially in populations with founder mutations, when a BRCA1 mutation is detected in an offspring, regardless of family history. The combination of mutations in these two genes presents a challenge for PGD since both genes are located on chromosome 17.