Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma.

Objectives: Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC). Methods: We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method. Results: Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29-65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28-88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750-0.949) in the training set and 0.819 (95% CI: 0.693-0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035). Conclusions: In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging.

BACKGROUND: The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. PURPOSE: To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model. STUDY TYPE: Prospective. ANIMAL MODEL: A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC. FIELD STRENGTH/SEQUENCE: A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2 ) and T1-twist DCE acquisition series. ASSESSMENT: DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. STATISTICAL TESTS: The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant. RESULTS: The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans , r = 0.820; Kep , r = 0.683). DATA CONCLUSION: DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 4.

Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase.

OBJECTIVE: To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS AND METHODS: This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model. RESULTS: Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant. CONCLUSION: The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer. KEY POINTS: • Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

Magnetic Resonance Imaging (MRI) Phenotypes May Provide Additional Information for Risk Stratification for Encapsulated Papillary Carcinoma of the Breast.

BACKGROUND: Encapsulated papillary carcinoma (EPC) of the breast is a rare entity. EPC can be underappreciated on percutaneous biopsy, which may require additional procedures if invasion is not recognized preoperatively. We aimed to investigate the magnetic resonance imaging (MRI) phenotypes correlated with preoperative pathological risk stratification for clinical guidance. MATERIALS AND METHODS: The preoperative MRI scans of 30 patients diagnosed with 36 EPCs in multiple centers between August 2015 and February 2020 were reviewed by two breast radiologists. According to the WHO classification published in 2019, EPCs were classified into two pathological subtypes: encapsulated papillary carcinoma and encapsulated papillary carcinoma with invasion. Clinicopathological analysis of the two subtypes and MR feature analysis were performed. RESULTS: Evaluation of the MRI phenotypes and pathological subtype information revealed that not circumscribed (P=0.04) was more common in EPCs with invasion than in EPCs. There was a significant difference in the age of patients (P=0.05), and the risk increased with age. The maximum diameter of the tumor increased with tumor risk, but there was no significant difference (P=0.36). Nearly half of the EPC with invasion patients showed hyperintensity on T1WI (P=0.19). A total of 63.6% of the EPC with invasion group showed non-mass enhancement surrounding (P=0.85). In addition, 29 patients (96.7%) had no axillary lymph node metastasis, and only one patient with EPC with invasion had axillary lymph node metastasis. Further pathological information analysis of EPCs showed that higher Ki-67 levels were more common in patients with EPCs with invasion (P=0.04). A total of 29 patients (96.7%) had the luminal phenotype, and one patient with EPC with invasion had the Her-2-positive phenotype. CONCLUSION: The margin, age and Ki-67 level were the key features for EPC risk stratification. In addition, these MRI signs, including a larger tumor, non-mass enhancement surrounding and axillary lymph node metastasis, may be suggestive of a high-risk stratification. Therefore, MRI phenotypes may provide additional information for the risk stratification of EPCs.

Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer.

PURPOSE: To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer. METHODS: In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29-85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student's t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson's chi-square test or Fisher's exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests. RESULTS: A statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029). CONCLUSIONS: Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.