The impact of body mass index (BMI) on MRI diagnostic performance and surgical management for axillary lymph node in breast cancer

Effects of BMI on prognosis, disease-free survival and overall survival of breast cancer

BACKGROUND

Obese breast cancer patients have worse prognosis than normal weight patients, but the level at which obesity is prognostically unfavorable is unclear. This study aimed to investigate different effects of Body Mass Index (BMI) on prognosis disease-free survival and overall survivor of breast cancer patients.

METHOD

This retrospective cohort study analyzed the medical records of breast cancer patients who sought treatment at Namazi hospital in Shiraz, Iran between 2014 and 2019. Three groups of patients were divided according to BMI. Menopausal status, BMI status, clinicopathological characteristics, treatment, and overall survival (OS), and disease free survival (DFS) were comprehensively evaluated. The World Health Organization (WHO) BMI classification was used to categorize patients into three groups: normal weight (BMI < 25.0 kg/m2), overweight (25.0 ≤ BMI < 30.0 kg/m2), and obese (BMI ≥ 30.0 kg/m2).

RESULTS

Of the 7134 breast cancer patients, the majority (42.6%) were in 25-30 kg/m2. Menopausal status significantly were associated with obesity (P < 0 .001). The majority of patients were categorized as grade 2 and stage 2 according to the BMI categories (P = 0.12, P = 0.08, respectively). BMI categories regardless of menopausal status displayed increased 1, 3, and 5-year DFS and 5- year OS in stage 1 and increased 1, 3, and 5-year OS and 1 and 3-year DFS in stage 2. In stage 3, the risks of relapse and death were significantly decreased in all three groups of BMI patients with post-menopausal period.

CONCLUSION

Obesity leads to worse DFS and OS in patients with BC and the effects of obesity on the breast cancer prognosis seem to be clinically related to menopausal status. Once validated, these results should be considered in the development of prevention programs.

Predictive modeling of breast cancer-related lymphedema using machine learning algorithms

Background

Breast cancer-related lymphedema (BCRL) is one of the common complications after breast cancer surgery. It can easily lead to limb swelling, deformation and upper limb dysfunction, which has a serious impact on the physical and mental health and quality of life of patients. Previous studies have mostly used statistical methods such as linear regression and logistic regression to analyze the influencing factors, but all of them have certain limitations. Machine learning (ML) is an important branch of artificial intelligence, which can effectively overcome the problems of multivariate interaction and collinearity. This study aimed to explore the influencing factors for the occurrence of BCRL in breast cancer patients, and construct a predictive model with ML algorithms and validate its predictive value on this basis.

Methods

Clinical data of breast cancer patients admitted to Hainan Cancer Hospital from September 2018 to May 2024 were retrospectively collected. BCRL was considered as the outcome measurement, and the data were divided into training and validation sets in a ratio of 7:3. In the training set, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms were used to construct predictive models. The discrimination accuracy of the models was evaluated with receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, and F1 score. The calibration of the models was assessed using calibration curves and the Hosmer-Lemeshow (H-L) Chi-squared test.

Results

Two hundred and forty patients who met the inclusion criteria were screened, and they were randomly divided into a training set (168 patients) and a validation set (72 patients) in a 7:3 ratio. In the training set, 44 cases developed BCRL, while 124 did not. There were statistically significant differences (P<0.05) in hypertension history, number of dissected lymph nodes, postoperative complications, postoperative functional exercises, chemotherapy, radiotherapy, tumor node metastasis (TNM) stage, and level of axillary lymph node dissection between the BCRL and non-BCRL groups. Among the four models, the XGBoost model showed the best predictive performance, with an area under the curve (AUC) of 0.99 in the training set and 0.89 in the validation set. The XGBoost model demonstrated good calibration in both the training and validation sets, showing good consistency with the ideal model.

Conclusions

The ML-based XGBoost model for predicting BCRL exhibits excellent performance and assists healthcare professionals in rapidly and accurately assessing the risk of BCRL occurrence.

Association between postoperative pathological results and non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node-positive breast cancer

OBJECTIVE

For patients with 1-2 positive sentinel lymph nodes (SLN) identified by biopsy, the necessity of axillary lymph node dissection (ALND) remains a matter of debate. The primary aim of this study was to investigate the association between postoperative pathological factors and non-sentinel lymph node (NSLN) metastases in Chinese patients diagnosed with sentinel node-positive breast cancer.

METHODS

This research involved a total of 280 individuals with SLN-positive breast cancer. The relationship between postoperative pathological variables and non-sentinel lymph node metastases was scrutinized using univariate, multivariate, and stratified analysis.

RESULTS

Among the 280 patients with a complete count of SLN positives, 126 (45.0%) exhibited NSLN metastasis. Within this group, 45 cases (35.71%) had 1 SLN positive, while 81 cases (64.29%) demonstrated more than 1 SLN positive. Multivariate logistic regression analysis revealed that HER2 expression status (OR 2.25, 95% CI 1.10-4.60, P = 0.0269), LVI (OR 6.08, 95% CI 3.31-11.14, P < 0.0001), and the number of positive SLNs (OR 4.17, 95% CI 2.35-7.42, P < 0.0001) were positively correlated with NSLNM.

CONCLUSION

In our investigation, the risk variables for NSLN metastasis included LVI, HER2 expression, and the quantity of positive sentinel lymph nodes. However, further validation is imperative, including this institution, distinct institutions, and diverse patient populations.

Nipple-areolar complex (NAC) or skin flap ischemia necrosis post nipple-sparing mastectomy (NSM)-analysis of clinicopathologic factors and breast magnetic resonance imaging (MRI) features

BACKGROUND

The purpose of this study is to identify clinicopathologic factors and/or preoperative MRI vascular patterns in the prediction of ischemia necrosis of the nipple-areola complex (NAC) or skin flap post nipple-sparing mastectomy (NSM).

METHODS

We performed a retrospective analysis of 441 NSM procedures from January 2011 to September 2021 from the breast cancer database at our institution. The ischemia necrosis of NAC or skin flap was evaluated in correlation with clinicopathologic factors and types of skin incision. Patients who received NSM with preoperative MRI evaluation were further evaluated for the relationship between vascular pattern and the impact on ischemia necrosis of NAC or skin flap.

RESULTS

A total of 441 cases with NSM were enrolled in the current study, and the mean age of the cases was 49.1 ± 9.8 years old. A total of 41 (9.3%) NSM procedures were found to have NAC ischemia/necrosis. Risk factors were evaluated of which old age, large mastectomy specimen weight (> 450 g), and peri-areola incision were identified as predictors of NAC necrosis. Two-hundred seventy NSM procedures also received preoperative MRI, and the blood supply pattern was 18% single-vessel type and 82% double-vessel pattern. There were no correlations between MRI blood supply patterns or types of skin flap incisions with ischemia necrosis of NAC. There were also no correlations between blood loss and the pattern or size of the blood vessel.

CONCLUSION

Factors such as the type of skin incision, age, and size of mastectomy weight played an important role in determining ischemia necrosis of NAC; however, MRI vascular (single or dual vessel supply) pattern was not a significant predictive factor.

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.