Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Preoperative prediction of extensive intraductal component in invasive breast cancer based on intra- and peri-tumoral heterogeneity in high-resolution ultrafast DCE-MRI

Preoperatively predicting extensive intraductal component in invasive breast cancer through imaging is crucial for informed decision-making, guiding surgical planning to mitigate risks of incomplete resection or re-operation for positive margins in breast-conserving surgery. This study aimed to characterize intra- and peri-tumor heterogeneity using high-spatial resolution ultrafast DCE-MRI to predict the extensive intraductal component in invasive breast cancer (IBC-EIC) preoperatively. A retrospective analysis included invasive breast cancer patients who underwent preoperative high-spatial resolution ultrafast DCE-MRI, categorized based on intraductal component status (IBC-EIC vs. IBC without EIC). Propensity score matching (PSM) was employed to balance clinicopathological covariates between the groups. Personalized kinetic intra-tumor heterogeneity (ITHkinetic) and peri-tumor heterogeneity (PTHkinetic) scores were quantified using clustered voxels with similar enhancement patterns. An image combined model, incorporating MRI features, ITHkinetic, and PTHkinetic scores, was developed and assessed. Of 368 patients, 26.4% (97/368) had IBC-EIC. PSM yielded well-matched pairs of 97 patients each. After PSM, ITHkinetic and PTHkinetic scores were significantly higher in the IBC-EIC group (ITHkinetic: 0.68 ± 0.23; PTHkinetic: 0.58 ± 0.19) compared to IBC without EIC (ITHkinetic: 0.32 ± 0.25; PTHkinetic: 0.42 ± 0.18; p < 0.001). Before PSM, ITHkinetic (0.71 ± 0.20 vs. 0.49 ± 0.28, p < 0.001) and PTHkinetic (0.61 ± 0.18 vs. 0.50 ± 0.20, p < 0.001) scores remained higher in the IBC-EIC group. The Image Combined Model demonstrated good predictive performance for IBC-EIC, with an AUC of 0.91 (95% CI 0.86-0.95) after PSM and 0.85 (95% CI 0.81-0.90) before PSM. Inclusion of ITHkinetic and PTHkinetic scores significantly improved prediction capability. ITHkinetic and PTHkinetic characterization from high-spatial resolution ultrafast DCE-MRI kinetic curves enhances preoperative prediction of IBC-EIC, offering valuable insights for personalized breast cancer management.

Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis

PURPOSE

The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.

MATERIAL AND METHODS

Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.

RESULTS

A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.

CONCLUSION

Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.

Imaging features of breast cancer subtypes on contrast enhanced ultrasound: a feasibility study

The objective of this research was to study the contrast enhancement patterns of the different molecular subtypes of breast cancer on contrast-enhanced ultrasound (CEUS) using both qualitative and quantitative parameters. This prospective study included females with a single breast mass which was histopathologically proven carcinoma. B mode ultrasound (USG) and CEUS were performed in all patients during baseline assessment. Qualitative CEUS assessment encompassed enhancement pattern, presence of fill-in and washout. Quantitative assessment included measurement of peak enhancement, time to peak; area under the curve and mean transit time. A p-value < 0.05 was considered statistically significant for differentiating the subtypes. The included thirty masses were categorised into two subtypes-triple negative breast cancer (TNBC) (36.7%) and non-TNBC (63.3%) subtypes. With B-mode USG, a statistically significant difference was observed between the two groups with respect to their shape and margins. TNBC lesions showed an oval shape, circumscribed margins and peripheral nodular enhancement on CEUS with the absence of fill-in even in the delayed phase (p-value - 0.04). The two subtypes did not significantly differ in terms of quantitative perfusion parameters. The various subtypes of breast cancer therefore possess distinct contrast enhancement patterns. CEUS potentially allows differentiation amongst these molecular subtypes that may aid in radiology-pathology (rad-path) correlation and follow up of the patients.

A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI

OBJECTIVE

Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI.

METHODS

This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated.

RESULTS

In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs.

CONCLUSION

A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs.

CLINICAL RELEVANCE STATEMENT

Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue.

KEY POINTS

• It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.

Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear.

PURPOSE

To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics.

STUDY TYPE

Prospective.

POPULATION

A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%).

FIELD STRENGTH/SEQUENCE

A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values).

ASSESSMENT

After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression.

STATISTICAL TESTS

Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant.

RESULTS

With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90).

DATA CONCLUSION

NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Evaluation of breast cancer malignancy, prognostic factors and molecular subtypes using a continuous-time random-walk MR diffusion model

PURPOSE

To assess the continuous-time random-walk (CTRW) model's diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors.

METHOD

This retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and β) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters.

RESULTS

All parameters, ADC, Dm, α, and β were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and β) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower β compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that β produced significant correlations with ER, PR and Ki-67 expression (P < 0.05).

CONCLUSIONS

The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI

Background Breast cancer tumors can be identified as different luminal molecular subtypes depending on either immunohistochemical (IHC) staining or St Gallen criteria that includes Ki-67. Purpose To characterize molecular subtypes and understand the impact of disagreement among IHC and St Gallen molecular subtype reference standards on artificial intelligence classification of luminal A and luminal B tumors with use of radiomic features extracted from dynamic contrast-enhanced (DCE) MRI scans. Materials and Methods In this retrospective study, 28 radiomic features previously extracted from DCE-MRI scans of breast tumors imaged between February 2015 and October 2017 were examined in the following groups: (a) tumors classified as luminal A by both reference standards ("agreement"), (b) tumors classified as luminal A by IHC and luminal B by St Gallen ("disagreement"), and (c) tumors classified as luminal B by both ("agreement"). Luminal A or luminal B tumor classification with use of radiomic features was conducted with use of three sets: (a) IHC molecular subtyping, (b) St Gallen molecular subtyping, and (c) agreement tumors. The Kruskal-Wallis test was followed by the Mann-Whitney U test to determine pair-wise differences of radiomic features among agreement and disagreement tumors. Fivefold cross-validation with use of stepwise feature selection and linear discriminant analysis classified tumors in each set, with performance measured with use of area under the receiver operating characteristic curve (AUC). Results A total of 877 breast cancer tumors from 872 women (mean age, 48 years [range, 19-75 years]) were analyzed. Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, volume of most enhancing voxels) were different (P ≤ .001) among agreement and disagreement tumors. AUC (median, 0.74 [95% CI: 0.68, 0.80]) was higher than when using tumors subtyped by either reference standard (IHC, 0.66 [0.60, 0.71], P = .003; St Gallen, 0.62 [0.58, 0.67], P = .001). Conclusion Differences in reference standards can hinder artificial intelligence classification performance of luminal molecular subtypes with dynamic contrast-enhanced MRI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

Objective

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.

Materials and Methods

A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.

Results

The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.

Conclusions

Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging

Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group ( $P<0.05$ ), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group ( $P<0.05$ ). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference ( $P>0.05$ ). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.

A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.

The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).

The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors

Purpose

To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients.

Methods

This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement.

Results

The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05).

Conclusion

FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.

Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis

INTRODUCTION

Breast cancer has four distinct molecular subtypes which are discriminated using gene expression profiling following biopsy. Radiogenomics is an emerging field which utilises diagnostic imaging to reveal genomic properties of disease. We aimed to perform a systematic review of the current literature to evaluate the value radiomics in differentiating breast cancers into their molecular subtypes using diagnostic imaging.

METHODS

A systematic review was performed as per PRISMA guidelines. Studies assessing radiomictumour analysis in differentiatingbreast cancer molecular subtypeswere included. Quality was assessed using the radiomics quality score (RQS). Diagnostic sensitivity and specificity of radiomic analyses were included for meta-analysis; Study specific sensitivity and specificity were retrieved and summary ROC analysis were performed to compile pooled sensitivities and specificities.

RESULTS

Forty-one studies were included. Overall, there were 10,090 female patients (mean age of 47.6 ± 11.7 years, range: 21-93) and molecular subtypewas reported in 7,693 of cases, with Luminal A (LABC), Luminal B (LBBC), Human Epidermal Growth Factor Receptor-2 overexpressing (HER2+), and Triple Negative (TNBC) breast cancers representing 51.3%, 19.9%, 12.3% and 16.3% of tumour respectively. Seven studies provided radiomic analysis to determine molecular subtypes using mammography to differentiateTNBCvs.others (sensitivity: 0.82,specificity:0.79). Thirty-five studies reported on radiomic analysis of magnetic resonance imaging (MRI); LABC versus others(sensitivity:0.78,specificity:0.83),HER2+versusothers(sensitivity:0.87,specificity:0.88), andLBBCversusTNBC (sensitivity: 0.79,specificity:0.88) respectively.

CONCLUSION

Radiomic tumour assessment of contemporary breast imaging provide a novel option in determining breast cancer molecular subtypes. However, amelioration of such techniques are required and genetic expression assessment will remain the gold standard.

Molecular Mechanism of Secondary Endocrine Resistance in Luminal Breast Cancer

OBJECTIVE

The molecular mechanism of secondary resistance in Luminal breast cancer was studied to provide new ideas for the treatment of breast cancer.

METHODS

The sensitivity of the downregulation of myeloid leukemia factor 1-interacting proteins (MLF1IP) to Tamoxifen (TAM) was tested by the Cell Counting Kit-8 (CCK-8). The apoptosis of MLF1IP-mediated resistance was analyzed by flow cytometry (FCM) with/without TAM. Western blot was used in detecting various kinds of apoptosis and the expression of the protein related to the phosphatidylinositol 3-kinase (PI3K)/AKT signaling pathway to study the molecular mechanism of secondary endocrine resistance in Luminal breast cancer.

RESULTS

The downregulation of MLF1IP could significantly increase the drug sensitivity of Michigan Cancer Foundation-7 (MCF-7) cells and also inhibit the proliferation of MCF-7 cells under the stimulation of drugs. Western blot results showed that the expression of Bcl-2-associated X (BAX), Caspase3, Caspase7, and Caspase9 proteins increased when MLF1IP was downregulated. The results of the PI3K/AKT signaling pathway revealed that the phosphatase and tensin homolog deleted on chromosome ten (PTEN) protein expression of MCF7-shRNA was higher than that of MCF7-NC cells, while the expression of p-AKT was lower than that of MCF7-NC cells.

CONCLUSIONS

(1) MLF1IP-related apoptosis resistance plays an essential role in MLF1IP-mediated secondary resistance of breast cancer cells. (2) MLF1IP promotes AKT phosphorylation by inhibiting the PTEN expression, thus activating the PI3K/AKT signaling pathway and causing the secondary resistance of Luminal breast cancer. (3) MLF1IP can be used as a factor to predict the endocrine resistance of Luminal breast cancer.

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

OBJECTIVE

To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.

MATERIALS AND METHODS

A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.

RESULTS

The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).

CONCLUSION

Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.