A new method for apparent diffusion coefficient measurement using sequential 18F-FDG PET and MRI: correlation with histological grade of invasive ductal carcinoma of the breast

Breast Tumor Classification based on MRI-US Images by Disentangling Modality Features

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US), which are two common modalities for clinical breast tumor diagnosis besides Mammograms, can provide different and complementary information for the same tumor regions. Although many machine learning methods have been proposed for breast tumor classification based on either single modality, it remains unclear how to further boost the classification performance by utilizing paired multi-modality information with different dimensions. In this paper, we propose MRI-US multi-modality network (MUM-Net) to classify breast tumor into different subtypes based on 3D MR and 2D US images. The key insight of MUM-Net is that we explicitly distill modality-agnostic features for tumor classification. Specifically, we first adopt a discrimination-adaption module to decompose features into modality-agnostic and modality-specific ones with min-max training strategies. Then, we propose a feature fusion module to increase the compactness of the modality-agnostic features by utilizing an affinity matrix with nearest neighbour selection. We build a paired MRI-US breast tumor classification dataset containing 502 cases with three clinical indicators to validate the proposed method. In three tasks including lymph node metastasis, histological grade and Ki-67 level, MUM-Net achieves AUC scores of 0.8581, 0.8965 and 0.8577, outperforming other counterparts which are based on single task or single modality by a wide margin. In addition, we find that the extracted modality-agnostic features can help the network focus on the tumor regions in both modalities.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

Diagnostic value of diffusion-weighted imaging-derived apparent diffusion coefficient and its association with histological prognostic factors in breast cancer

Diffusion-weighted imaging (DWI) has been proven to be effective in detecting breast malignancies and has been widely implemented for breast imaging. However, the exact association between certain DWI biomarkers and well-known prognostic factors remains to be fully elucidated. By studying the association between the apparent diffusion coefficient (ADC) and prognostic factors, the present study aimed to explore the diagnostic value and prognostic potential of the ADC in breast lesions. The study included 539 female subjects with histopathologically confirmed breast lesions who underwent DWI of the breast tissue. The diagnoses comprised 307 subjects with malignant breast tumors and 232 with benign breast tumors. The maximum ADC and mean ADC (ADC_mean) values of the breast lesions were calculated. For malignant tumors, the association between ADC and major prognostic factors, including histological grade, nuclear grade and lymph node status, as well as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and proliferation marker protein Ki-67.(Ki-67) status, were evaluated. The ADC_mean demonstrated the best diagnostic performance in distinguishing between malignant and benign lesions. With the optimum cut-off value at 1.30×10⁻³ mm²/sec, ADC_mean had a sensitivity and specificity of 84.1 and 90.2%, respectively. In those patients with malignant breast lesions, a decreased ADC was associated with breast lesions with high nuclear and histological grades, and lymph node-positive, ER-negative, PR-negative and HER-2-negative status, and Ki-67 ≥14%. In conclusion, the ADC is a useful imaging biomarker for differentiating between benign and malignant breast tumors. The marked association between the ADC and prognostic factors also demonstrated its value in evaluating the malignancy of breast lesions.

A multi-parametric PET/MRI study of breast cancer: Evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters

46 patients with histologically confirmed breast cancer were enrolled and imaged with a 3T hybrid PET/MRI system, at staging. Diffusion, functional and perfusion parameters (measured by Tofts and shutter speed models) were compared. Results showed a good correlation between pharmacokinetic parameters and the SUV.

Correlation of the apparent diffusion coefficient and the standardized uptake value in neoplastic lesions: a meta-analysis

OBJECTIVE

Diffusion-weighted imaging and fluorine-18-fluorodeoxyglucose PET are increasingly being recognized as feasible oncological techniques. The apparent diffusion coefficient (ADC) measured by diffusion-weighted imaging and the standardized uptake value (SUV) from fluorine-18-fluorodeoxyglucose PET have similar clinical applications. The aim of this study was to assess the correlation between these two parameters in various cancers.

MATERIALS AND METHODS

Several major databases were searched for eligible studies. The correlation coefficient (ρ) values were pooled in a random-effects model. Begg's test was used to analyze the existence of publication bias and the sources of heterogeneity were explored in subgroup analyses on the basis of study design, diagnostic method, scanning modality, and tumor type.

RESULTS

Thirty-five articles were accepted. The pooled ρ value of all of the accepted studies was -0.30 (95% confidence interval: -0.33 to -0.27), and notable heterogeneity was present (I=69.4%, P<0.001), which indicated a relatively weak negative correlation. The pooled ρ values were -0.26, -0.33, -0.32, and -0.33 for the SUVmax/ADCmean, SUVmax/ADCmin, SUVmean/ADCmean, and SUVmean/ADCmin relationships, respectively. The study design and diagnostic method were potential sources of heterogeneity. Lung cancer showed a stronger correlation (ρ=-0.42) than head and neck cancer (ρ=-0.27), cervical cancer (ρ=-0.21), and breast cancer (ρ=-0.23). A Begg's test indicated no significant publication bias among the accepted studies (P>0.05).

CONCLUSION

The two functional parameters of ADC and SUV showed a very weak inverse correlation, which may contribute toward a sophisticated characterization of tumor biology. However, the findings require further validation with trials with large samples and different tumor types.

Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma

The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADC_min) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADC_min value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADC_min values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADC_min values for IDC of grades I, II and III were (1.14±0.11)×10^-3, (0.99±0.12)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively. Statistically significant differences were detected in the mean ADC_min value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADC_min values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADC_min value was correlated with the IDC histological grade, and lower ADC_min values were associated with a higher histological grade and more aggressiveness. Thus, the ADC_min value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.

Prediction of tumor differentiation using sequential PET/CT and MRI in patients with breast cancer

OBJECTIVE

The aim of this study is to assess tumor differentiation using parameters from sequential positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in patients with breast cancer.

METHODS

This retrospective study included 78 patients with breast cancer. All patients underwent sequential PET/CT and MRI. For fluorodeoxyglucose (FDG)-PET image analysis, the maximum standardized uptake value (SUV_max) of FDG was assessed at both 1 and 2 h and metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The kinetic analysis of dynamic contrast-enhanced MRI parameters was performed using dynamic enhancement curves. We assessed diffusion-weighted imaging (DWI)-MRI parameters regarding apparent diffusion coefficient (ADC) values. Histologic grades 1 and 2 were classified as low-grade, and grade 3 as high-grade tumor.

RESULTS

Forty-five lesions of 78 patients were classified as histologic grade 3, while 26 and 7 lesions were grade 2 and grade 1, respectively. Patients with high-grade tumors showed significantly lower ADC-mean values than patients with low-grade tumors (0.99 ± 0.19 vs.1.12 ± 0.32, p = 0.007). With respect to SUV_max1, MTV2.5, and TLG2.5, patients with high-grade tumors showed higher values than patients with low-grade tumors: SUV_max1 (7.92 ± 4.5 vs.6.19 ± 3.05, p = 0.099), MTV2.5 (7.90 ± 9.32 vs.4.38 ± 5.10, p = 0.095), and TLG2.5 (40.83 ± 59.17 vs.19.66 ± 26.08, p = 0.082). However, other parameters did not reveal significant differences between low-grade and high-grade malignancies. In receiver-operating characteristic (ROC) curve analysis, ADC-mean values showed the highest area under the curve of 0.681 (95%CI 0.566-0.782) for assessing high-grade malignancy.

CONCLUSIONS

Lower ADC-mean values may predict the poor differentiation of breast cancer among diverse PET-MRI functional parameters.

Prognostic value of simultaneous 18F-FDG PET/MRI using a combination of metabolo-volumetric parameters and apparent diffusion coefficient in treated head and neck cancer

Background

The aim of this study was to determine the usefulness of combined positron emission tomography (PET)/magnetic resonance imaging (MRI) parameters provided by simultaneous ¹⁸F-fluorodeoxyglucose (FDG) PET/MRI for the prediction of treatment failure in surgically resected head and neck cancer. We hypothesized that PET parameters corrected by tumor cellularity (combined PET/MRI parameters) could predict the prognosis. On regional PET, maximum standardized uptake value (SUVmax) was measured as metabolic parameters. In addition, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were checked as metabolo-volumetric parameters. Mean apparent diffusion coefficient (ADCmean) of tumor was evaluated as the MRI parameter on the ADC map. Ratios between metabolic/metabolo-volumetric parameters and ADC were calculated as combined PET/MRI parameters. PET, MRI, and combined PET/MRI parameters were compared with clinicopathologic parameters in terms of treatment failure.

Results

Seventy-two patients (mean age = 55.9 ± 14.6 year, M: F = 45: 27) who underwent simultaneous ¹⁸F-FDG PET/MRI before head and neck cancer surgery were retrospectively enrolled. Twenty-two patients (30.6%) showed tumor treatment failure after head and neck cancer surgery (mean treatment failure = 13.0 ± 7.0 months). In the univariate analysis, MTV (P = 0.044) and ratios between metabolo-volumetric parameters and ADC (MTV/ADCmean, P = 0.022; TLG/ADCmean, P = 0.044) demonstrated significance among ¹⁸F-FDG PET/MRI parameters. Lymphatic invasion (P = 0.044) and perineural invasion (P = 0.046) revealed significance among clinicopathologic parameters. In the multivariate analysis, MTV (P = 0.026), MTV/ADCmean (P = 0.011), and TLG/ADCmean (P = 0.002) with lymphatic invasion (P = 0.026, 0.026, and 0.044, respectively) showed significance.

Conclusions

Combined PET/MRI parameters (PET metabolo-volumetric parameters corrected by tumor cellularity) could be effective predictors of tumor treatment failure after head and neck cancer surgery in addition to MTV and clinicopathologic parameter.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0357-9) contains supplementary material, which is available to authorized users.

Meta-Analysis of the Correlation between Apparent Diffusion Coefficient and Standardized Uptake Value in Malignant Disease

The objective of this meta-analysis is to explore the correlation between the apparent diffusion coefficient (ADC) on diffusion-weighted MR and the standard uptake value (SUV) of ¹⁸F-FDG on PET/CT in patients with cancer. Databases such as PubMed (MEDLINE included), EMBASE, and Cochrane Database of Systematic Review were searched for relevant original articles that explored the correlation between SUV and ADC in English. After applying Fisher's r-to-z transformation, correlation coefficient (r) values were extracted from each study and 95% confidence intervals (CIs) were calculated. Sensitivity and subgroup analyses based on tumor type were performed to investigate the potential heterogeneity. Forty-nine studies were eligible for the meta-analysis, comprising 1927 patients. Pooled r for all studies was −0.35 (95% CI: −0.42–0.28) and exhibited a notable heterogeneity (I² = 78.4%; P < 0.01). In terms of the cancer type subgroup analysis, combined correlation coefficients of ADC/SUV range from −0.12 (lymphoma, n = 5) to −0.59 (pancreatic cancer, n = 2). We concluded that there is an average negative correlation between ADC and SUV in patients with cancer. Higher correlations were found in the brain tumor, cervix carcinoma, and pancreas cancer. However, a larger, prospective study is warranted to validate these findings in different cancer types.

Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer

PURPOSE

Diffusion-weighted imaging (DWI) and ¹⁸ F-fluorodeoxyglucose-positron emission tomography (¹⁸ F-FDG-PET) independently correlate with malignancy in breast cancer, but the relationship between their structural and metabolic metrics is not completely understood. This study spatially correlates diffusion, perfusion, and glucose avidity in breast cancer with simultaneous PET/MR imaging and compares correlations with clinical prognostics.

METHODS

In this Health Insurance Portability and Accountability Act-compliant prospective study, with written informed consent and approval of the institutional review board and using simultaneously acquired FDG-PET and DWI, tissue diffusion (D_t ), and perfusion fraction (f_p ) from intravoxel incoherent motion (IVIM) analysis were registered to FDG-PET within 14 locally advanced breast cancers. Lesions were analyzed using 2D histograms and correlation coefficients between D_t , f_p , and standardized uptake value (SUV). Correlations were compared with prognostics from biopsy, metastatic burden from whole-body PET, and treatment history.

RESULTS

SUV||D_t correlation coefficient significantly distinguished treated (0.11 ± 0.24) from nontreated (-0.33 ± 0.26) patients (P = 0.005). SUV||f_p correlations were on average negative for the whole cohort (-0.17 ± 0.13).

CONCLUSION

Simultaneously acquired and registered FDG-PET/DWI allowed quantifiable descriptions of breast cancer microenvironments that may provide a framework for monitoring and predicting response to treatment. Magn Reson Med 78:1147-1156, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Correlation of the SUVmax of FDG-PET and ADC values of diffusion-weighted MR imaging with pathologic prognostic factors in breast carcinoma

PURPOSE

To correlate both primary lesion maximum standardized uptake values (SUVmax) of FDG-PET/CT, and apparent diffusion coefficient (ADC) values of diffusion-weighted imaging (DWI) with clinicopathologic prognostic factors in patients with breast carcinoma.

MATERIALS AND METHODS

214 patients with 216 mass-type invasive breast carcinomas underwent whole-body FDG-PET/CT and 3-Tesla breast MRI including DWI before initial therapy. The primary tumor's SUVmax and ADC values were measured using FDG-PET/CT and DWI, respectively. Histologic analysis parameters included tumor size, expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67, nuclear grade, histology subtype, and axillary lymph node (LN) metastasis. The relationships among SUVmax, ADC values, and pathologic prognostic factors were evaluated.

RESULTS

The mean SUVmax and ADCmean were 5.63±3.79 (range, 1.2-24.17) and 894±204×10(-6)mm(2)/s (range, 452-1550×10(-6)), respectively. There was a significant but weak inverse correlation between the SUVmax and ADCmean values (correlation coefficient r=-0.30, p<0.0001). SUVmax was associated with numerous prognostic factors such as tumor size (p<0.0001), expression levels of ER (p=0.00041), PR (p=0.00028), HER2 (p=0.00021), and Ki-67 (p<0.0001), nuclear grade (p<0.0001), histology subtype (p=0.00061), axillary LN metastasis (p<0.0001), and TNM staging (p<0.0001). Meanwhile, ADCmean value was associated with tumor size (p=0.013), expression of Ki-67 (p=0.0010), histology subtype (p=0.00013), axillary LN metastasis (p=0.00059), and TNM staging (p=0.0011).

CONCLUSIONS

Primary tumor SUVmax on FDG-PET/CT has a stronger relationship with known prognostic parameters and may be a more useful for predicting the prognosis of breast carcinoma than ADC values.

Differentiated epithelial- and mesenchymal-like phenotypes in subcutaneous mouse xenografts using diffusion weighted-magnetic resonance imaging

Epithelial-mesenchymal transition (EMT) is important for tumor metastasis. Detection of EMT protein expression and observation of morphological changes are commonly used to identify EMT. Diffusion-weighted magnetic resonance imaging (DW-MRI) and measuring apparent diffusion coefficient (ADC) values are noninvasive techniques for characterizing tumor microenvironments. We investigated the difference in ADC values between epithelial- and mesenchymal-like subcutaneous mouse xenografted tumors using DW-MRI. Epithelial-like MM189 PB-Klf4 and BL322 PB-Klf4 cells were generated from tumor suppressive Kruppel-like factor 4 (Klf4)-expressing mesenchymal-like MM189 and BL322 cells. The ADC values of xenografted tumors from epithelial-like MM189 PB-Klf4 and BL322 PB-Klf4 were significantly lower than those from their mesenchymal-like counterparts (p < 0.05 and p < 0.01, respectively). Our results suggested that DW-MRI is a potential tool for observing mesenchymal- or epithelial-like characteristics of subcutaneous xenografted tumors.