Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer

Non-contrast Breast MR Imaging

Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.

Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model

BACKGROUND

Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC).

PURPOSE

To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC.

MATERIAL AND METHODS

This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses.

RESULTS

WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model.

CONCLUSION

The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.

The variability of aggressiveness of grade 1 breast cancer

BACKGROUND

Grade 1 breast cancer represents the lowest grade of invasive breast cancer and is associated with a low risk of recurrence and distant metastasis. However, when grade 1 breast cancer is associated with lymph node involvement, the prognosis may be worse than that of grade 1 breast cancer without lymph node involvement.

METHOD

The study population included all patients who were managed in our institution between January 1, 2007 and December 31, 2013 for grade 1 breast cancer . We compared patients who had lymph node involvement to those who had no lymph node involvement.

RESULTS

During the study period 291 grade 1 carcinomas were included of which 23% had associated positive lymph node involvement. Overall survival did not differ significantly between patients without lymph node involvement and those with lymph node involvement, nor was there a significant difference in the risk of local recurrence free survival. However, a significant difference was found in survival without distant metastasis with a significant level of a p at 0.029.

CONCLUSION

Our findings confirm that tumor size and LVSI are strong predictors of axillary lymph node involvement, which is a key determinant of distant metastasis-free survival.

Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology

The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Objective

To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM).

Methods

A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T₂WI, DWI, and contrast-enhanced T₁WI (CE-T₁WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model.

Results

The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs.

Conclusion

A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.

Intravoxel Incoherent Motion (IVIM) MR Quantification in Locally Advanced Cervical Cancer (LACC): Preliminary Study on Assessment of Tumor Aggressiveness and Response to Neoadjuvant Chemotherapy

The aim of this study was to determine whether quantitative parameters obtained from intravoxel incoherent motion (IVIM) model at baseline magnetic resonance imaging (MRI) correlate with histological parameters and response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer (LACC). Methods: Twenty patients with biopsy-proven cervical cancer, staged as LACC on baseline MRI and addressed for neoadjuvant chemotherapy were enrolled. At treatment completion, tumor response was assessed with a follow-up MRI evaluated using the revised response evaluation criteria in solid tumors (RECIST; version 1.1), and patients were considered good responders (GR) if they had complete response or partial remission, and poor responders/non-responders (PR/NR) if they had stable or progressive disease. MRI protocol included conventional diffusion-weighted imaging (DWI; b = 0 and 1000 s/mm²) and IVIM acquisition using eight b-values (range: 0–1500 s/mm²). MR-images were analyzed using a dedicated software to obtain quantitative parameters: diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp) from the IVIM model; apparent diffusion coefficient (ADC) from conventional DWI. Histologic subtype, grading, and tumor-infiltrating lymphocytes (TILs) were assessed in each LACC. Results: D showed significantly higher values in GR patients (p = 0.001) and in moderate/high TILs (p = 0.018). Fp showed significantly higher values in squamous cell tumors (p = 0.006). Conclusions: D extracted from the IVIM model could represent a promising tool to identify tumor aggressiveness and predict response to therapy.

Correlation between Tissue Cellularity and Metabolism Represented by Diffusion-Weighted Imaging (DWI) and 18F-FDG PET/MRI in Head and Neck Cancer (HNC)

Simple Summary

We report on the correlation between the diffusion-weighted imaging (DWI) and the metabolic volume parameters derived from a PET scan, to determine the correlation between these parameters and the tumor cellularity in head and neck primary tumors. Our findings implied that there was no correlation between the information derived from the DWI and the information derived from the FDG metabolic parameters. Thus, both imaging techniques might play a complementary role in HNC diagnosis and assessment. This is significant because the treatment plan of patients with HNC should be well evaluated by using all the available diagnosis techniques, for a better understanding of how the tumor will react.

Abstract

Background: This study aimed to assess the association of 18F-Fluorodeoxyglucose positron-emission-tomography (18F-FDG/PET) and DWI imaging parameters from a primary tumor and their correlations with clinicopathological factors. Methods: We retrospectively analyzed primary tumors in 71 patients with proven HNC. Primary tumor radiological parameters: DWI and FDG, as well as pathological characteristics were analyzed. Spearman correlation coefficient was used to assess the correlation between DWI and FDG parameters, ANOVA or Kruskal–Wallis, independent sample t-test, Mann–Whitney test, and multiple regression were performed on the clinicopathological features that may affect the 18F- FDG and apparent-diffusion coefficient (ADC) of the tumor. Results: No significant correlations were observed between DWI and any of the 18F-FDG parameters (p > 0.05). SUVmax correlated with N-stages (p = 0.023), TLG and MTV correlated with T-stages (p = 0.006 and p = 0.001), and ADC correlated with tumor grades (p = 0.05). SUVmax was able to differentiate between N+ and N− groups (p = 0.004). Conclusions: Our results revealed a non-significant correlation between the FDG-PET and ADC-MR parameters. FDG-PET-based glucose metabolic and DWI-MR-derived cellularity data may represent different biological aspects of HNC.

Breast cancer preoperative 18FDG-PET, overall survival prognostic separation compared with the lymph node ratio

PURPOSE

To evaluate the overall survival prognostic value of preoperative 18F-fluorodeoxyglucose positron emission tomography (PET) in breast cancer, as compared with the lymph node ratio (LNR).

METHODS

Data were abstracted at a median follow-up 14.7 years from a retrospective cohort of 104 patients who underwent PET imaging before curative surgery. PET-Axillary|Sternal was classified as PET-positive if hypermetabolism was visualized in ipsilateral nodal axillary and/or sternal region, else as PET-negative. The differences of 15 years restricted mean survival time ∆RMST according to PET and LNR were computed from Kaplan-Meier overall survival. The effect of PET and other patients' characteristics was analyzed through rankit normalization, which provides with Cox regression the Royston-Sauerbrei D measure of separation to compare the characteristics (0 indicating no prognostic value). Multivariate analysis of the normalized characteristics used stepwise selection with the Akaike information criterion.

RESULTS

In Kaplan-Meier analysis, LNR > 0.20 versus ≤ 0.20 showed ∆RMST = 3.4 years, P = 0.003. PET-Axillary|Sternal positivity versus PET-negative showed a ∆RMST = 2.6 years, P = 0.008. In Cox univariate analyses, LNR appeared as topmost prognostic separator, D = 1.50, P < 0.001. PET ranked below but was also highly significant, D = 1.02, P = 0.009. In multivariate analyses, LNR and PET-Axillary|Sternal were colinear and mutually exclusive. PET-Axillary|Sternal improved as prognosticator in a model excluding lymph nodes, yielding a normalized hazard ratio of 2.44, P = 0.062.

CONCLUSION

Pathological lymph node assessment remains the gold standard of prognosis. However, PET appears as a valuable surrogate in univariate analysis at 15-year follow-up. There was a trend towards significance in multivariate analysis that warrants further investigation.