Contrast enhanced computed tomography is indicative for angiogenesis pattern and display prognostic significance in breast cancer

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Vessel size as a marker of survival in estrogen receptor positive breast cancer

PURPOSE

Angiogenesis is crucial for tumor growth and is one of the hallmarks of cancer. In this study, we analyzed microvessel density, vessel median size, and perivascular a-SMA expression as prognostic biomarkers in breast cancer.

METHODS

Dual IHC staining was performed where alpha-SMA antibodies were used together with antibodies against the endothelial cell marker CD34. Digital images of stainings were analyzed to extract quantitative data on vessel density, vessel size, and perivascular alpha-SMA status.

RESULTS

The analyses in the discovery cohort (n = 108) revealed a statistically significant relationship between large vessel size and shorter disease-specific survival (p = 0.007, log-rank test; p = 0.01, HR 3.1; 95% CI 1.3-7.4, Cox-regression analyses). Subset analyses indicated that the survival association of vessel size was strengthened in ER + breast cancer. To consolidate these findings, additional analyses were performed on a validation cohort (n = 267) where an association between large vessel size and reduced survival was also detected in ER + breast cancer (p = 0.016, log-rank test; p = 0.02; HR 2.3, 95% CI 1.1-4.7, Cox-regression analyses).

CONCLUSION

Alpha-SMA/CD34 dual-IHC staining revealed breast cancer heterogeneity regarding vessel size, vessel density, and perivascular a-SMA status. Large vessel size was linked to shorter survival in ER + breast cancer.

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

BACKGROUND

Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with early-stage breast cancer.

METHODS

Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test. Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.

RESULTS

This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).

CONCLUSIONS

Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

Estrogen Receptor and Claudin-6 Might Play Vital Roles for Long-Term Prognosis in Patients With Luminal A Breast Cancer Who Underwent Neoadjuvant Chemotherapy

Purpose

It is well-known that the pathological complete response (pCR) rate in patients with luminal A cancer (LAC) is lower than those of other subtypes of breast cancer. The phenotype of cancer often alters after neoadjuvant chemotherapy (NAC) which may be related to hypoxia, and the latter might induce the drift of the estrogen receptor (ER). The phenotype drift in local advanced LAC after NAC might influence the long-term prognosis.

Methods

The oxygen concentration of cancer tissues during NAC was recorded and analyzed (n = 43). The expression of ER and claudin-6 was detected in pre- and post-NAC specimens.

Results

NAC might induce the cycling intracanceral hypoxia, and the pattern was related to NAC response. The median follow-up time was 61 months. Most of the patients (67%) with stable or increased ER and claudin-6 expression exhibited perfect prognosis (DFS = 100%, 61 months). About 20% of patients with decreased claudin-6 would undergo the poor prognosis (DFS = 22.2%, 61 months). The contrasting prognosis (100% vs. 22.2%) had nothing to do with the response of NAC in the above patients. Only 13% patients had stable claudin-6 and decreased ER, whose prognosis might relate to the response of NAC.

Conclusion

NAC might induce cycling intracanceral hypoxia to promote the phenotype drift in local advanced LAC, and the changes in ER and claudin-6 after NAC would determine the long-term prognosis.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Background

The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.

Purpose

The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.

Methods

We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).

Results

The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.

Conclusion

The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

INTRODUCTION

The purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT).

PATIENTS AND METHODS

A total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup.

RESULTS

Compared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction.

CONCLUSION

This study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.

Microvessel density in breast cancer: the impact of field area on prognostic informativeness

AIMS

Tumour microvessel density (MVD) is assessed by counting vessels in the most vascularised tumour region, the vascular hot spot. Current uncertainty regarding the prognostic role of MVD in breast cancer could, in part, be explained by variations in field area size for MVD assessment. We aimed to identify the field area size that provides the most accurate prognostic information in breast carcinoma.

METHODS

MVD was assessed in 212 tumours. von Willebrand factor positively stained vessels were counted in 10 consecutive visual fields in vascular hotspots. The 10 visual fields in the original counting sequence (MVD-Consecutive) were sorted from highest to lowest vessel count (MVD-Decreasing), and randomly (MVD-Random). After adding counts from one visual field at a time, mean MVD was calculated for each cumulative field area. The prognostic informativeness of each field area and sorting strategy were compared.

RESULTS

Median MVD decreased with increasing field size for MVD-Decreasing and MVD-Consecutive. A 0.35 mm² total field area comprising only the highest vessel counts provided the most accurate prognostic information (MVD-Decreasing, HR for breast cancer death 1.06 per 10 vessels/mm² increase, 95% CI 1.03 to 1.10). MVD-Decreasing gave more accurate prognostic information than MVD-Consecutive and MVD-Random, with decreasing prognostic informativeness with increasing field area.

CONCLUSIONS

Median MVD and its prognostic informativeness decreased with increasing field area. Assessing MVD in a carefully selected small field area of 0.35 mm² provides the most accurate prognostic information. This could facilitate the implementation of MVD assessment in breast cancer.