Apparent diffusion coefficient as a potential surrogate marker for Ki-67 index in mucinous breast carcinoma

Whole-tumor histogram analysis of multiparametric breast magnetic resonance imaging to differentiate pure mucinous breast carcinomas from fibroadenomas with high-signal intensity on T2WI

PURPOSE

To investigate the utility of whole-tumor histogram analysis based on multiparametric MRI in distinguishing pure mucinous breast carcinomas (PMBCs) from fibroadenomas (FAs) with strong high-signal intensity on T2-weighted imaging (T2-SHi).

MATERIAL AND METHODS

The study included 20 patients (mean age, 55.80 ± 15.54 years) with single PBMCs and 29 patients (mean age, 42.31 ± 13.91 years) with single FAs exhibiting T2-SHi. A radiologist performed whole-tumor histogram analysis between PBMC and FA groups with T2-SHi using multiparametric MRI, including T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and the first (DCE_T1) and last (DCE_T4) phases of T1-weighted dynamic contrast-enhanced imaging (DCE) images, to extract 11 whole-tumor histogram parameters. Histogram parameters were compared between the two groups to identify significant variables using univariate analyses, and their diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis and logistic regression analyses. In addition, 15 breast lesions were randomly selected and histogram analysis was repeated by another radiologist to assess the intraclass correlation coefficient for each histogram feature. Pearson's correlation coefficients were used to analyze the correlations between histogram parameters and Ki-67 expression of PMBCs.

RESULTS

For T2WI images, mean, median, maximum, 90th percentile, variance, uniformity, and entropy significantly differed in PBMCs and FAs with T2-SHi (all P < 0.05), yielding a combined area under the curve (AUC) of 0.927. For ADC maps, entropy was significantly lower in FAs with T2-SHi than in PMBCs (P = 0.03). In both DCE_T1 and DCE_T4 sequences, FAs with T2-SHi showed significantly higher minimum values than PBMCs (P = 0.007 and 0.02, respectively). The highest AUC value of 0.956 (sensitivity, 0.862; specificity, 0.944; positive predictive value, 0.962; negative predictive value, 0.810) was obtained when all significant histogram parameters were combined.

CONCLUSIONS

Whole-tumor histogram analysis using multiparametric MRI is valuable for differentiating PBMCs from FAs with T2-SHi.

A Prospective Observational Study of Diagnostic Reliability of Semiquantitative and Quantitative High b-Value Diffusion-Weighted MRI in Distinguishing between Benign and Malignant Lung Lesions at 3 Tesla

Aim  The aim of this study was to evaluate the usefulness of high b-value diffusion-weighted imaging (DWI) to differentiate benign and malignant lung lesions in 3 Tesla magnetic resonance imaging (MRI). Materials and Methods  Thirty-one patients with lung lesions underwent a high b-value (b= 1000 s/mm 2 ) DW MRI in 3 Tesla. Thirty lesions were biopsied, followed by histopathological analysis, and one was serially followed up for 2 years. Statistical analysis was done to calculate the sensitivity, specificity, and accuracy of different DWI parameters in distinguishing benign and malignant lesions. Receiver operating characteristic (ROC) curves were used to determine the cutoff values of different parameters. Results  The qualitative assessment of signal intensity on DWI based on a 5-point rank scale had a mean score of 2.71 ± 0.75 for benign and 3. 75 ± 0.60 for malignant lesions. With a cutoff of 3.5, the sensitivity, specificity, and accuracy were 75, 86, and 77.6%, respectively. The mean ADC min (minimum apparent diffusion coefficient) value of benign and malignant lesions was 1. 49 ± 0.38 × 10-3 mm 2 /s and 1.11 ± 0.20 ×10-3 mm 2 /s, respectively. ROC curve analysis showed a cutoff value of 1.03 × 10-3 mm 2 /s; the sensitivity, specificity, and accuracy were 87.5, 71.4, and 83.3%, respectively. For lesion to spinal cord ratio and lesion to spinal cord ADC ratio with a cutoff value of 1.08 and 1.38, the sensitivity, specificity, and accuracy were 83.3 and 87.5%, 71.4 and 71.4%, and 80.6 and 83.8%, respectively. The exponential ADC showed a low accuracy rate. Conclusion  The semiquantitative and quantitative parameters of high b-value DW 3 Tesla MRI can differentiate benign from malignant lesions with high accuracy and make it a reliable nonionizing modality for characterizing lung lesions.

Correlation between dynamic contrast-enhanced MRI characteristics and apparent diffusion coefficient with Ki-67-positive expression in non-mass enhancement of breast cancer

As a remarkably specific characteristic of breast cancer observed on magnetic resonance imaging (MRI), the association between the NME type breast cancer and prognosis, including Ki-67, necessitates comprehensive exploration. To investigate the correlation between dynamic contrast-enhanced MRI (DCE-MRI) characteristics and apparent diffusion coefficient (ADC) values with Ki-67-positive expression in NME type breast cancer. A total of 63 NME type breast cancer patients were retrospectively reviewed. Malignancies were confirmed by surgical pathology. All patients underwent DCE and diffusion-weighted imaging (DWI) before surgery. DCE-MRI characteristics, including tumor distribution, internal enhancement pattern, axillary adenopathy, and time-intensity curve types were observed. ADC values and lesion sizes were also measured. The correlation between these features and Ki-67 expression were assessed using Chi-square test, Fisher's exact test, and Spearman rank analysis. The receiver operating characteristic curve and area under the curve (AUC) was used to evaluate the diagnostic performance of Ki-67-positive expression. Regional distribution, TIC type, and ipsilateral axillary lymph node enlargement were correlated with Ki-67-positive expression (χ2 = 0.397, 0.357, and 0.357, respectively; P < 0.01). ADC value and lesion size were positively correlated with Ki-67-positive expression (rs = 0.295, 0.392; P < 0.05). The optimal threshold values for lesion size and ADC value to assess Ki-67 expression were determined to be 5.05 (AUC = 0.759) cm and 0.403 × 10-3 s/mm2 (AUC = 0.695), respectively. The best diagnosis performance was the ADC combined with lesion size (AUC = 0.791). The ADC value, lesion size, regional distribution, and TIC type in NME type breast cancer were correlated with Ki-67-positive expression. These features will aid diagnosis and treatment of NME type breast cancer.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purpose

Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated.

Materials and methods

Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints.

Results

Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL.

Conclusions

The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Preoperative prediction of Ki-67 status in invasive breast carcinoma using dynamic contrast-enhanced MRI, diffusion-weighted imaging and diffusion tensor imaging

Background

The Ki-67 is a beneficial marker of tumor aggressiveness. It is proliferation index that has been used to distinguish luminal B from luminal A breast cancers. By fast progress in quantitative radiology modalities, tumor biology and genetics can be assessed in a more accurate, predictive, and cost-effective method. The aim of this study was to assess the role of dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging and diffusion tensor imaging in prediction of Ki-67 status in patients with invasive breast carcinoma estimate cut off values between breast cancer with high Ki-67 status and those with low Ki-67 status.

Results

Cut off ADC (apparent diffusion co-efficient) value of 0.657 mm2/s had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off maximum enhancement value of 1715 had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off washout rate of 0.73 I/S had 60.7% sensitivity, 75% specificity and 62.5% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off time to peak value of 304 had 71.4% sensitivity, 75% specificity and 71.9% accuracy in differentiating cases with high Ki67 from those with low Ki67.

Conclusions

ADC, time to peak and maximum enhancement values had high sensitivity, specificity and accuracy in differentiating breast cancer with high Ki-67 status from those with low Ki-67 status.

Breast Magnetic Resonance Imaging Can Predict Ki67 Discordance Between Core Needle Biopsy and Surgical Samples

BACKGROUND

Disagreement in assessments of Ki67 expression based on core-needle biopsy and matched surgical samples complicates decisions in the treatment of breast cancer.

PURPOSE

To examine whether preoperative breast MRI could be useful in predicting Ki67 discordance between core-needle biopsy and surgical samples.

STUDY TYPE

Retrospective.

POPULATION

Three hundred and sixty-five breast cancer patients with MRI scans and having both core-needle biopsy and surgical samples from 2017 to 2019.

FIELD STRENGTH/SEQUENCE

3.0 T, T2-weighted iterative decomposition of water and fat with echo asymmetry and least squares estimation sequence, diffusion-weighted sequence using b-values 0/1000, dynamic contrast enhanced image by volume imaged breast assessme NT.

ASSESSMENT

We collected clinicopathologic variables and preoperative MRI features (tumor size, lesion type, shape of mass, spiculated margin, internal enhancement, peri-tumoral edema, intra-tumoral necrosis, multifocal/multicentric, apparent diffusion coefficient [ADC] minimum, ADC mean, ADC maximum, ADC difference).

STATISTICAL TESTS

K-means clustering, multivariable logistic regression, receiver operating characteristic curve.

RESULTS

Sixty-one patients showed Ki67 discordance and 304 patients show Ki67 concordance according to our definition using K-means clustering. Multivariable regression analysis showed that the following parameters were independently associated with Ki67 discordance: peri-tumoral edema, odds ratio (OR) 2.662, 95% confidence interval (CI) 1.432-4.948; ADCmin ≤ 0.829 × 10^-3  mm² /sec, OR 2.180, 95% CI 1.075-4.418; and ADCdiff > 0.317 × 10^-3  mm² /sec, OR 3.365, 95% CI 1.698-6.669. This multivariable model resulted in an AUC of 0.758 (95% CI 0.711-0.802) with sensitivity and specificity being 0.803 and 0.621, respectively.

CONCLUSION

Presence of peri-tumoral edema, smaller ADCmin and greater ADCdiff in preoperative breast MRI may indicate high risk of Ki67 discordance between core-needle biopsy and surgical samples. For patients with these MRI-based risk factors, clinicians should not rely on Ki67 assessment only from core-needle biopsy.

DWI of the Breast - Possibilities and Limitations

BACKGROUND

 The MRI of the breast is of great importance in the diagnosis of disorders of the breast. This can be stated for the primary diagnosis as well as the follow up. Of special interest is diffusion weighted imaging (DWI), which has an increasingly important role. The present review provides results regarding the diagnostic and prognostic relevance of DWI for disorders of the breast.

METHODS

 Under consideration of the recently published literature, the clinical value of DWI of the breast is discussed. Several diagnostic applications are shown, especially for the primary diagnosis of unclear tumors of the breast, the prediction of the axillary lymph node status and the possibility of a native screening. Moreover, correlations between DWI and histopathology features and treatment prediction with DWI are provided.

RESULTS

 Many studies have shown the diagnostic value of DWI for the primary diagnosis of intramammary lesions. Benign lesions of the breast have significantly higher apparent diffusion coefficients (ADC values) compared to malignant tumors. This can be clinically used to reduce unnecessary biopsies in clinical routine. However, there are inconclusive results for the prediction of the histological subtype of the breast cancer. DWI can aid in the prediction of treatment to neoadjuvant chemotherapy.

CONCLUSION

 DWI is a very promising imaging modality, which should be included in the standard protocol of the MRI of the breast. DWI can provide clinically value in the diagnosis as well as for prognosis in breast cancer.

KEY POINTS

  · DWI can aid in the discrimination between benign and malignant tumors of the breast and therefore avoiding unnecessary biopsies.. · The ADC value cannot discriminate between immunhistochemical subtypes of the breast cancer. · The ADC value of breast cancer increases under neoadjuvant chemotherapy and can by this aid in treatment prediction.. · There is definite need of standardisation for clinical translation.

CITATION FORMAT

· Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8572.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior

BACKGROUND

to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness.

METHODS

100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman's Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50-70% and >70%), using Mann-Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups.

RESULTS

ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805).

CONCLUSIONS

3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.

Total diffusion volume in MRI vs. total lesion glycolysis in PET/CT for tumor volume evaluation of multiple myeloma

OBJECTIVE

This study compared the tumor burden and prognostic impact of total diffusion volume (tDV) and total lesion glycolysis (TLG) in the same patients with newly diagnosed multiple myeloma (NDMM) simultaneously. We also examined the relationship between these imaging tumor volumes (TVs) and plasma cell (PC) TV in bone marrow (BM) specimens.

METHODS

We retrospectively reviewed the data of 63 patients with newly diagnosed multiple myeloma (NDMM) from April 2016 to March 2018. tDV was calculated from whole-body diffusion-weighted imaging and TLG was calculated from the average standard uptake value and the metabolic tumor volume, respectively. Cellularity of BM hematopoietic tissue and the percentage of BM PCs were used as a reference of PC volume in the BM.

RESULTS

The Spearman correlation coefficient between tDV and TLG was moderate (ɤs = 0.588, p < 0.001) when PET false-negative patients were excluded. There were positive correlations between the BM plasma cell volume (BMPCV) and the imaging TVs (ɤs = 0.505, vs. tDV; and 0.464, vs. TLG). Patients with high tDV and high TLG, as determined by the receiver operating characteristic curve, had worse survival; moreover, patients with both high tDV and high TLG showed the worst prognosis (median progression-free and overall survival: 13.2 and 28.9 months, respectively).

CONCLUSIONS

Although tDV and TLG each reflected the total TV, in several cases, tDV and TLG were discrepant due to the biological features of each MM. It is important to use both modalities for complementary assessment of total tumor burden and biological characteristics in MM.

KEY POINTS

• Total diffusion volume (tDV) and total lesion glycolysis (TLG) reflect the total tumor volume and have prognostic value in patients with multiple myeloma (MM). • tDV and TLG could assess MM from different biological perspectives and should be considered for each patient individually.

Turbo Spin-echo Diffusion-weighted Imaging Compared with Single-shot Echo-planar Diffusion-weighted Imaging: Image Quality and Diagnostic Performance When Differentiating between Ductal Carcinoma in situ and Invasive Ductal Carcinoma

Purpose:

To compare the image quality between turbo spin-echo (TSE)-diffusion weighted imaging (DWI) and single-shot echo-planar imaging (EPI)-DWI, and to verify the diagnostic performance of the apparent diffusion coefficient (ADC) parameters of the two techniques by using histogram analysis in terms of differentiation between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) lesions.

Methods:

Ninety-four women with 94 lesions diagnosed as breast cancer by surgery underwent IRB-approved preoperative magnetic resonance imaging, including TSE and EPI-DWI with b-values of 50 and 850 s/mm². Twenty lesions were identified as DCIS and 74 as IDC. Image quality [signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion] was evaluated quantitatively and compared between the TSE and EPI-DWI. A histogram analysis of the entire tumor voxel-based ADC data was performed, and the 10th, 25th, 50th, 75th, and 90th percentile values of each technique were compared between DCIS and IDC lesions.

Results:

The SNR and CNR of TSE-DWI were significantly higher than those of EPI-DWI (P < 0.0001 and < 0.0001). The geometric distortion of TSE-DWI was significantly lower than that of EPI-DWI (P < 0.0001). In TSE-DWI, the 10th, 25th, 50th, and 75th percentile values were significantly different between the DCIS and IDC lesions (P = 0.0010, 0.0004, 0.0008, and 0.0044, respectively). In EPI-DWI, the 50th and 75th percentile values were significantly different between the two groups (P = 0.0009 and 0.0093). There was no significant difference in the area under the curve of the receiver operating characteristic analysis of the 10th, 25th, 50th, and 75th percentile values of TSE-DWI, and the 50th and 75th percentile values of EPI-DWI (P = 0.29).

Conclusion:

The image quality of TSE-DWI was better than that of EPI-DWI. DCIS lesions were distinguished from IDC lesions with a wider range of percentile values in TSE-DWI than in EPI-DWI, although diagnostic performance was not significantly different between the techniques.

Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging

Background

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) technique in discriminating histological grades of rectal carcinoma.

Methods

Altogether, 49 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative 3.0 T MR scan. Histogram parameters from rFOV DWI were calculated and correlated with histological differentiation of rectal cancer. The parameters were compared between different histological grades of rectal cancer by independent Student's t-test or Man-Whitney U-test. The Spearman correlation test analyzed correlations between histological grade and histogram parameters. The diagnostic performance of individual parameters for distinguishing poorly from well-/moderately differentiated tumors was assessed by receiver operating characteristic curve (ROC) analysis.

Results

There were significant differences for ADCmean, 25^th, 50^th, 75^th, 90^th, 95^th percentiles, skewness, and kurtosis of rFOV DWI sequence between well-, moderately, and poorly differentiated rectal cancers (P<0.05). Significant correlations were noted between histological grades and the above histogram parameters (r=0.679, 0.540, 0.701, 0.730, 0.669, 0.574, -0.730, and -0.760 respectively, P<0.001). Among the individual histogram parameter, kurtosis achieved the highest AUC of 0.882 with an optimal cutoff value of 1.934 in distinguishing poorly from well-/moderately differentiated rectal cancers. The combination of ADCmean, 75^th percentile, and kurtosis yielded the highest AUC of 0.927 with a sensitivity of 88.00% and a sensitivity of 91.7% using logistic regression.

Conclusions

Quantitative whole-lesion ADC histogram analysis based on the rFOV DWI technique could help differentiate histological grades of rectal cancer. The combination of ADCmean, 75^th percentile, and kurtosis may be the best choice.

Stromal Collagen Content in Breast Tumors Correlates With In Vivo Diffusion-Weighted Imaging: A Comparison of Multi b-Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions

BACKGROUND

Increased deposition and reorientation of stromal collagen fibers are associated with breast cancer progression and invasiveness. Diffusion-weighted imaging (DWI) may be sensitive to the collagen fiber organization in the stroma and could provide important biomarkers for breast cancer characterization.

PURPOSE

To understand how collagen fibers influence water diffusion in vivo and evaluate the relationship between collagen content and the apparent diffusion coefficient (ADC) and the signal fractions of the biexponential model using a high b-value scheme.

STUDY TYPE

Prospective.

SUBJECTS/SPECIMENS

Forty-five patients with benign (n = 8), malignant (n = 36), and ductal carcinoma in situ (n = 1) breast tumors. Lesions and normal fibroglandular tissue (n = 9) were analyzed using sections of formalin-fixed, paraffin-embedded tissue stained with hematoxylin, erythrosine, and saffron.

FIELD STRENGTH/SEQUENCE

MRI (3T) protocols: Protocol I: Twice-refocused spin-echo echo-planar imaging with: echo time (TE) 85 msec; repetition time (TR) 9300/11600 msec; matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm³ ; b-values: 0 and 700 s/mm² . Protocol II: Stejskal-Tanner spin-echo echo-planar imaging with: TE: 88 msec; TR: 10600/11800 msec, matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm³ ; b-values [0, 200, 600, 1200, 1800, 2400, 3000] s/mm² .

ASSESSMENT

Area fractions of cellular and collagen content in histologic sections were quantified using whole-slide image analysis and compared with the corresponding DWI parameters.

STATISTICAL TESTS

Correlations were assessed using Pearson's r. Univariate analysis of group median values was done using the Mann-Whitney U-test.

RESULTS

Collagen content correlated with the fast signal fraction (r = 0.67, P < 0.001) and ADC (r = 0.58, P < 0.001) and was lower (P < 0.05) in malignant lesions than benign and normal tissues. Cellular content correlated inversely with the fast signal fraction (r = -0.67, P < 0.001) and ADC (r = -0.61, P < 0.001) and was different (P < 0.05) between malignant, benign, and normal tissues.

DATA CONCLUSION

Our findings suggest stromal collagen content increases diffusivity observed by MRI and is associated with higher ADC and fast signal fraction of the biexponential model.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1868-1878.

Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis

Background

Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC.

Methods

Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient.

Results

ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67.

Conclusion

ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma

PURPOSE

To assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on whole-tumor in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from angiomatous meningioma (AM).

MATERIALS AND METHODS

Pathologically confirmed intracranial SFT/HPC (n = 15) and AM (n = 20) were retrospectively collected and their clinical and conventional MRI features were analyzed. Diffusion-weighted (DW) images (b = 0 and 1000 s/mm²) were processed with the mono-exponential model. Regions of interest covering the whole tumor were drawn on all slices of the ADC maps to obtain histogram parameters, including mean ADC (ADCmean), median ADC (ADCmedian), maximum ADC (ADCmax), minimum ADC (ADCmin), skewness and kurtosis, as well as the 5th, 10th, 25th, 75th, 90th and 95th percentile ADC (ADC5, ADC10, ADC25, ADC75, ADC90 and ADC95). Differences of histogram parameters between SFT/HPC and AM were compared using Mann-Whitney U test. Receiver operating characteristic (ROC) curve was used to determine the diagnostic performance.

RESULTS

The ADCmin (P = 0.001) and ADC5 (P = 0.045) were significantly lower in SFT/HPCs than in AMs, while no significant difference was found in sex, age, conventional MRI features or any other histogram parameters between the two entities (P = 0.051-1.000). ADCmin showed the best diagnostic performance (area under curve [AUC], 0.86; sensitivity, 81.3%; specificity, 83.3%) in differentiating SFT/HPC from AM with optimal cutoff value being 569.00 × 10^-6  mm²/s, followed by ADC5 (AUC, 0.72; sensitivity, 68.8%; specificity, 75%) with optimal cutoff value being 781.97 × 10^-6  mm²/s.

CONCLUSION

SFT/HPC and AM share similar conventional MR appearances. Whole-tumor histogram analysis of ADC maps may be a useful tool for differential diagnosis, with ADCmin and ADC5 being potential parameters.

Association among T2 signal intensity, necrosis, ADC and Ki-67 in estrogen receptor-positive and HER2-negative invasive ductal carcinoma

PURPOSE

To determine whether T2 signal intensity, necrosis, and ADC values are associated with Ki-67 in patients with Estrogen Receptor (ER)-positive and Human epidermal growth factor receptor type 2 (HER2)-negative invasive ductal carcinoma (IDC).

MATERIALS AND METHODS

Between March 2012 and February 2013, one hundred eighty seven women with ER-positive and HER2-negative IDC who underwent breast MRI and subsequent surgery were included. Intratumoral signal intensity was evaluated based on a combination of T2-weighted (low or equal, high, or very high) and contrast-enhanced MR images (enhancement or not). Necrosis was defined as very high T2 and no enhancement. Using the analysis of variance and pairwise t-test, a model based on intratumoral signal intensity was developed to assess Ki-67 of the surgical specimen. Inter-observer agreement for the developed model was analyzed. Conventional mean and minimum apparent diffusion coefficient (ADC) measurements were performed and correlated with Ki-67.

RESULTS

As the grade of the developed model increased (Grade I: low or equal T2, Grade II: high T2, or necrosis < 50%, Grade III: necrosis ≥ 50%), mean Ki-67 significantly increased (Grade I to III: 12.5%, 17.6%, 45.0%, respectively; P < 0.001). Good inter-observer agreement was found for the model (κ = 0.846, P < 0.001). ADC did not show significant correlations with Ki-67 (Pearson's correlation coefficient, 0.140 [P = 0.057] for mean ADC; -0.079 [P = 0.284] for minimum ADC).

CONCLUSION

Intratumoral signal intensity but not ADC was associated with Ki-67 in patients with ER-positive and HER2-negative IDC.

Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis

Background

Numerous studies have analyzed associations between apparent diffusion coefficient (ADC) and histopathological features such as Ki-67 proliferation index in breast cancer (BC), with mixed results. The purpose of this study was to perform a multicenter analysis to determine relationships between ADC and expression of Ki-67 and tumor grade in BC.

Methods

For this study, data from six centers were acquired. The sample comprises 870 patients (all female; mean age, 52.6 ± 10.8 years). In every case, breast magnetic resonance imaging with diffusion-weighted imaging was performed. The comparison of ADC values in groups was performed by Mann-Whitney U test where the p values are adjusted for multiple testing (Bonferroni correction). The association between ADC and Ki-67 values was calculated by Spearman’s rank correlation coefficient. Sensitivity, specificity, negative and positive predictive values, accuracy, and AUC were calculated for the diagnostic procedures. ADC thresholds were chosen to maximize the Youden index.

Results

Overall, data of 870 patients were acquired for this study. The mean ADC value of the tumors was 0.98 ± 0.22 × 10^− 3 mm² s^− 1. ROC analysis showed that it is impossible to differentiate high/moderate grade tumors from grade 1 lesions using ADC values. Youden index identified a threshold ADC value of 1.03 with a sensitivity of 56.2% and specificity of 67.9%. The positive predictive value was 18.2%, and the negative predictive value was 92.4%. The level of the Ki-67 proliferation index was available for 845 patients. The mean value was 12.33 ± 21.77%. ADC correlated with weak statistical significant with expression of Ki-67 (p = − 0.202, p < 0.001). ROC analysis was performed to distinguish tumors with high proliferative potential from tumors with low expression of Ki-67 using ADC values. Youden index identified a threshold ADC value of 0.91 (sensitivity 64%, specificity 50%, positive predictive value 67.7%, negative predictive value 45.0%).

Conclusions

ADC cannot be used as a surrogate marker for proliferation activity and/or for tumor grade in breast cancer.

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki-67, HIF-1α and VEGF

Prostate cancer (PCa) is the second most common cancer in men. The Gleason score (GS) and biomarkers play important roles in the diagnosis and treatment of patients with PCa. The purpose of this study was to investigate the relationship between the apparent diffusion coefficient (ADC) and the molecular markers Ki-67, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in PCa. Thirty-nine patients with 39 lesions, who had been diagnosed with PCa, were enrolled in this study. All patients underwent diffusion-weighted magnetic resonance imaging (DW-MRI) (b = 800 s/mm² ). The expression of Ki-67, HIF-1α and VEGF was assessed by immunohistochemistry. Statistical analysis was applied to analyze the association between ADC and prostate-specific antigen (PSA), GS and the expression of Ki-67, HIF-1α and VEGF. The group differences in ADC among different grades of Ki-67, HIF-1α and VEGF were also analyzed. The mean ± standard deviation of ADC was (0.76 ± 0.27) × 10^-3  mm² /s. ADC correlated negatively with PSA and GS (p < 0.05). The Ki-67 staining index (SI), HIF-1α expression and VEGF expression in PCa were correlated inversely with ADC, controlling for age (r = -0.332, p < 0.05; r = -0.662, p < 0.0005; and r = -0.714, p < 0.0005, respectively). ADC showed a significant difference among different grades of Ki-67 (F = 9.164, p = 0.005), HIF-1α (F = 40.333, p < 0.0005) and VEGF (F = 22.048, p < 0.0005). In conclusion, ADC was correlated with PSA, GS, and Ki-67, HIF-1α and VEGF expression in patients with PCa. ADC may be used to evaluate tumor proliferation, hypoxia and angiogenesis in PCa.

ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) as an imaging biomarker for invasive ductal carcinoma (IDC) in the breast.

METHODS

Seventy-one patients undergoing 3.0 Tesla DWI were retrospectively enrolled. Correlations between the ADC values and prognostic factors were evaluated.

RESULTS

Multivariate regression analyses showed that Ki67 expression and molecular subtype were independently associated with the ADC. Discriminant analysis excluded the ADC as a good biomarker for subtype, but the mean ADC significantly distinguished Ki67-positive (low ADC) from Ki67-negative (high ADC) lesions, as observed in the in ROC curves, with a diagnostic sensitivity of 1.00 and a cut-off value of 0.97 × 10^-3 mm²/s.

CONCLUSION

The ADC may be helpful for predicting Ki67 expression in IDC preoperatively.

Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 2: ADCmin

The purpose of this part of the meta-analysis was to summarize data regarding associations between minimum apparent diffusion coefficient (ADC_min) and KI 67 in different tumors. MEDLINE library was screened for associations between ADC_min and KI 67 in different tumors up to April 2017. Overall, 23 studies with 944 patients were identified. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The pooled correlation coefficient between ADC_min and KI 67 for all included tumors was ρ = -0.47. In detail, the correlation coefficients for separate tumors were as follows: cerebral lymphoma: ρ = -0.61 (95% CI = [-0.82; -0.41]); cervical cancer: ρ = -0.56 (95% CI = [-0.68;-0.43]); pituitary adenoma: ρ = -0.55 (95% CI = [-1.31; 0.22]); glioma: ρ = -0.40 (95% CI = [-0.55; -0.24]); breast cancer: ρ = -0.37 (95% CI = [-0.74; -0.01]); meningioma, ρ = -0.15 (95% CI = [-0.38; 0.07]).

Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma

PURPOSE

To evaluate the utility of the whole-lesion histogram apparent diffusion coefficient (ADC) for characterizing the heterogeneity of mucinous breast carcinoma (MBC) and to determine which ADC metrics may help to best differentiate subtypes of MBC.

MATERIALS AND METHODS

This retrospective study involved 52 MBC patients, including 37 pure MBC (PMBC) and 15 mixed MBC (MMBC). The PMBC patients were subtyped into PMBC-A (20 cases) and PMBC-B (17 cases) groups. All patients underwent preoperative diffusion-weighted imaging (DWI) at 1.5T and the whole-lesion ADC assessments were generated. Histogram-derived ADC parameters were compared between PMBC vs. MMBC and PMBC-A vs. PMBC-B, and receiver operating characteristic (ROC) curve analysis was used to determine optimal histogram parameters for differentiating these groups.

RESULTS

The PMBC group exhibited significantly higher ADC values for the mean (P = 0.004), 25^th (P = 0.004), 50^th (P = 0.004), 75^th (P = 0.006), and 90^th percentiles (P = 0.013) and skewness (P = 0.021) than did the MMBC group. The 25^th percentile of ADC values achieved the highest area under the curve (AUC) (0.792), with a cutoff value of 1.345 × 10^-3 mm² /s, in distinguishing PMBC and MMBC. The PMBC-A group showed significantly higher ADC values for the mean (P = 0.049), 25^th (P = 0.015), and 50^th (P = 0.026) percentiles and skewness (P = 0.004) than did the PMBC-B group. The 25^th percentile of the ADC cutoff value (1.476 × 10^-3 mm² /s) demonstrated the best AUC (0.837) among the ADC values for distinguishing PMBC-A and PMBC-B.

CONCLUSION

Whole-lesion ADC histogram analysis enables comprehensive evaluation of an MBC in its entirety and differentiating subtypes of MBC. Thus, it may be a helpful and supportive tool for conventional MRI.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:391-400.

Heterogeneity of Diffusion-Weighted Imaging in Tumours and the Surrounding Stroma for Prediction of Ki-67 Proliferation Status in Breast Cancer

Breast tissue heterogeneity is related to risk factors that lead to more aggressive tumour growth and worse prognosis, yet such heterogeneity has not been well characterized. The aim of this study is to reveal the heterogeneous signal patterns of the apparent diffusion coefficient (ADC) of a tumour and its surrounding stromal tissue and to predict the Ki-67 proliferation status in oestrogen receptor (ER)-positive breast cancer patients. A dataset of 82 patients who underwent diffusion-weighted imaging (DWI) examination was collected. The ADC map was segmented into regions comprising the tumour and the surrounding stromal shells. To reflect correlations between each region in terms of its mean ADC value, a functional graph was constructed consisting of nodes as regions and edges as interactions between two nodes. Analysis of the graph revealed a higher average degree in samples over-expressing Ki-67 than in samples with low Ki-67 expression. In the low-Ki-67 group, most of the identified edges represented correlations between adjacent regions, whereas additional edges representing correlations between non-adjacent regions were found in the high-Ki-67 group. The ADC signal in various breast stromal regions surrounding the tumour showed a discriminative pattern and would be valuable for estimating the Ki-67 proliferation status by DWI.

Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis

The purpose of this meta-analysis was to provide clinical evidence regarding relationship between ADC and cellularity in different tumors based on large patient data.

Medline library was screened for associations between ADC and cell count in different tumors up to September 2016. Only publications in English were extracted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research.

Overall, 39 publications with 1530 patients were included into the analysis. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.

The pooled correlation coefficient for all studies was ρ = -0.56 (95 % CI = [−0.62; −0.50]),. Correlation coefficients ranged from ρ =−0.25 (95 % CI = [−0.63; 0.12]) in lymphoma to ρ=−0.66 (95 % CI = [−0.85; −0.47]) in glioma. Other coefficients were as follows: ovarian cancer, ρ = −0.64 (95% CI = [−0.76; −0.52]); lung cancer, ρ = −0.63 (95 % CI = [−0.78; −0.48]); uterine cervical cancer, ρ = −0.57 (95 % CI = [−0.80; −0.34]); prostatic cancer, ρ = −0.56 (95 % CI = [−0.69; −0.42]); renal cell carcinoma, ρ = −0.53 (95 % CI = [−0.93; −0.13]); head and neck squamous cell carcinoma, ρ = −0.53 (95 % CI = [-0.74; −0.32]); breast cancer, ρ = −0.48 (95 % CI = [−0.74; −0.23]); and meningioma, ρ = -0.45 (95 % CI = [−0.73; −0.17]).

Whole-tumor MRI histogram analyses of hepatocellular carcinoma: Correlations with Ki-67 labeling index

PURPOSE

To evaluate whether whole-tumor histogram-derived parameters for an apparent diffusion coefficient (ADC) map and contrast-enhanced magnetic resonance imaging (MRI) could aid in assessing Ki-67 labeling index (LI) of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

In all, 57 patients with HCC who underwent pretreatment MRI with a 3T MR scanner were included retrospectively. Histogram parameters including mean, median, standard deviation, skewness, kurtosis, and percentiles (5^th , 25^th , 75^th , 95^th ) were derived from the ADC map and MR enhancement. Correlations between histogram parameters and Ki-67 LI were evaluated and differences between low Ki-67 (≤10%) and high Ki-67 (>10%) groups were assessed.

RESULTS

Mean, median, 5^th , 25^th , 75^th percentiles of ADC, and mean, median, 25^th , 75^th , 95^th percentiles of enhancement of arterial phase (AP) demonstrated significant inverse correlations with Ki-67 LI (rho up to -0.48 for ADC, -0.43 for AP) and showed significant differences between low and high Ki-67 groups (P < 0.001-0.04). Areas under the receiver operator characteristics (ROC) curve for identification of high Ki-67 were 0.78, 0.77, 0.79, 0.82, and 0.76 for mean, median, 5^th , 25^th , 75^th percentiles of ADC, respectively, and 0.74, 0.81, 0.76, 0.82, 0.69 for mean, median, 25^th , 75^th , 95^th percentiles of AP, respectively.

CONCLUSION

Histogram-derived parameters of ADC and AP were potentially helpful for predicting Ki-67 LI of HCC.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:383-392.

Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen-receptor-positive breast cancer

The purpose of this retrospective study was to evaluate whether tumor apparent diffusion coefficient (ADC) was correlated with pathologic biomarkers such as tumor cellularity, Ki67, tumor-infiltrating lymphocytes (TILs), and peritumoral lymphocytic infiltrate (PLI) in patients with estrogen receptor (ER)-positive breast cancer. The study was approved by the institutional review board and informed consent was waived. From July 2014 to December 2014, we reviewed 140 ER-positive tumors in 138 consecutive patients (range, 28-77 years; mean, 52 years) who underwent preoperative breast MRI and definitive surgery. All patients underwent diffusion-weighted imaging with a 3T scanner. Two radiologists drew the region of interest of the entire tumor and obtained the mean and pixel-based histogram of ADC. On pathology, two pathologists reviewed tumor cellularity, Ki67, TILs, and PLI. Multiple linear regression analysis was used to determine pathologic variables independently associated with ADC. Tumors with high tumor cellularity and high Ki67 had significantly lower ADCs than those with low tumor cellularity and low Ki67 (P < 0.05 for all). Tumors without PLI had significantly higher standard deviation than those with PLI (0.23 ± 0.08 versus 0.18 ± 0.05; P < 0.001). Median ADC was negatively correlated with tumor cellularity (r = -0.441), and Ki67 (r = -0.382). The standard deviation of ADC was also negatively correlated with the degree of PLI (r = -0.319). On multivariate linear regression analysis, tumor cellularity and Ki67 were independently associated with tumor ADC. Tumor ADC would be an MRI biomarker for the prediction of tumor aggressiveness indicators such as Ki67, tumor cellularity, and PLI in ER-positive breast cancer. Copyright © 2016 John Wiley & Sons, Ltd.

Diffusion-weighted imaging and (18) F-fluorodeoxyglucose positron emission tomography/computed tomography in breast cancer: Correlation of the apparent diffusion coefficient and maximum standardized uptake values with prognostic factors

PURPOSE

To evaluate the correlations between the apparent diffusion coefficient (ADC) value and the standardized uptake value (SUV) with prognostic factors in breast cancer.

MATERIALS AND METHODS

Seventy women with invasive breast cancer (56 cases of invasive ductal carcinoma, four of mixed ductal and lobular invasive carcinoma, three of lobular invasive carcinoma, two of micropapillary carcinoma, and one each of mixed ductal and mucinous carcinoma, mucinous carcinoma, medullary carcinoma, metaplastic carcinoma, and tubular carcinoma) were included in this study. All patients underwent presurgical breast magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) at 1.5T and whole-body (18) F-fluorodeoxyglucose ((18) F-FDG) positron emission tomography (PET) / computed tomography (CT). For all invasive breast cancers and invasive ductal carcinomas, we assessed the relationships among ADC, SUV, and pathological prognostic factors.

RESULTS

Both the median ADC value and maximum SUV (SUVmax) were significantly associated with vascular invasion (P = 0.008 and P = 0.026, respectively). SUVmax was also significantly correlated with tumor size (P = 0.001), histological grade (P = 0.001), lymph node status (P = 0.0015), estrogen receptor status (P = 0.010), and human epidermal growth factor receptor 2 status (P = 0.020), whereas ADC values were not. The correlation between the ADC and SUVmax was not significant (P = 0.356; R = -0.112). Mucinous carcinoma showed high ADC and relatively low SUVmax. Medullary carcinoma showed low ADC and high SUVmax. When we evaluated the relationships among ADC, SUVmax, and prognostic factors in the 56 invasive ductal carcinomas, our statistical results were not significantly changed, except SUVmax was also significantly associated with progesterone receptor status (P = 0.034), but not lymph node status.

CONCLUSION

SUVmax may be valuable for predicting the prognosis of breast cancer. Both ADC and SUVmax are useful to predict vascular invasion. J. Magn. Reson. Imaging 2016;43:1434-1444.

Differentiation of pancreatobiliary-type from intestinal-type periampullary carcinomas using 3.0T MRI

PURPOSE

To differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas using combined magnetic resonance cholangiopancreatography (MRCP), contrast-enhanced MRI, and diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

MRI (3.0T) results of 41 patients with pathologically confirmed periampullary carcinoma were retrospectively assessed. Two radiologists, blinded to histologic type of each tumor, evaluated image findings independently. MRCP image features, enhancement pattern, and apparent diffusion coefficient (ADC) values were analyzed. Independent-sample t-test, chi-square, or Fisher's exact test were used to determine differential image findings between the pancreatobiliary-type and the intestinal-type group. Cohen's κ statistic or interclass correlation coefficient (ICC) were used to evaluate interobserver agreement between two observers. Univariate and multiple logistic regression analysis were performed to identify MRI features with predictive values.

RESULTS

On the basis of hematoxylin-eosin staining, 27 patients were classified as having pancreatobiliary-type carcinomas, and 14 patients the intestinal type. The pancreatobiliary-type carcinomas more commonly showed progressive enhancement than the intestinal type (P = 0.003). The minimum ADC (ADCmin ) value of the pancreatobiliary-type group ([0.95 ± 0.21] × 10(-3) mm(2) /s) was significantly lower than the intestinal-type group ([1.10 ± 0.25] × 10(-3) mm(2) /s) (P = 0.047). For interobserver agreement, the κ values and ICCs for all parameters exceeded 0.8, indicating almost perfect agreement. At multiple logistic regression analysis, the enhancement pattern was the only significant independent predictor (P = 0.011, odds ratio [OR] = 0.105). When the enhancement pattern and ADCmin were used in combination, we could identify 70.4% of pancreatobiliary-type and 78.6% of intestinal-type carcinomas.

CONCLUSION

Progressive enhancement and low ADCmin values suggest a pancreatobiliary-type periampullary carcinoma.

Correlation of diffusion MRI with the Ki-67 index in non-small cell lung cancer

BACKGROUND

The primary objective of the study was to evaluate the association between the minimum apparent diffusion coefficient (ADCmin) and Ki-67, an index for cellular proliferation, in non-small cell lung cancers. Also, we aimed to assess whether ADCmin values differ between tumour subtypes and tissue sampling method.

METHODS

The patients who had diffusion weighted magnetic resonance imaging (DW-MRI) were enrolled retrospectively. The correlation between ADCmin and the Ki-67 index was evaluated.

RESULTS

Ninety three patients, with a mean age 65 ± 11 years, with histopathologically proven adenocarcinoma and squamous cell carcinoma of the lungs and had technically successful DW-MRI were included in the study. The numbers of tumour subtypes were 47 for adenocarcinoma and 46 for squamous cell carcinoma. There was a good negative correlation between ADCmin values and the Ki-67 proliferation index (r = -0.837, p < 0.001). The mean ADCmin value was higher and the mean Ki-67 index was lower in adenocarcinomas compared to squamous cell carcinoma (p < 0.0001). There was no statistical difference between tissue sampling methods.

CONCLUSIONS

Because ADCmin shows a good but negative correlation with Ki-67 index, it provides an opportunity to evaluate tumours and their aggressiveness and may be helpful in the differentiation of subtypes non-invasively.

Multi-modal imaging of tumor cellularity and Tryptophan metabolism in human Gliomas

Background

To assess gliomas using image-based estimation of cellularity, we utilized isotropic diffusion spectrum imaging (IDSI) on clinically feasible diffusion tensor imaging (DTI) and compared it with amino acid uptake measured by α[¹¹C]methyl-L-tryptophan positron emission tomography (AMT-PET).

Methods

In 10 patients with a newly-diagnosed glioma, metabolically active tumor regions were defined in both FLAIR hyperintense areas and based on increased uptake on AMT-PET. A recently developed independent component analysis with a ball and stick model was extended to perform IDSI in clinical DTI data. In tumor regions, IDSI was used to define tumor cellularity which was compared between low and high grade glioma and correlated with the glioma proliferative index.

Results

The IDSI-derived cellularity values were elevated in both FLAIR and AMT-PET-derived regions of high-grade gliomas. ROC curve analysis found that the IDSI-derived cellularity can provide good differentiation of low-grade from high-grade gliomas (accuracy/sensitivity/specificity of 0.80/0.80/0.80). . Both apparent diffusion coefficient (ADC) and IDSI-derived cellularity showed a significant correlation with the glioma proliferative index (based on Ki-67 labeling; R = 0.95, p < 0.001), which was particularly strong when the tumor regions were confined to areas with high tryptophan uptake excluding areas with peritumoral edema.

Conclusion

IDSI-MRI combined with AMT-PET may provide a multi-modal imaging tool to enhance pretreatment assessment of human gliomas by evaluating tumor cellularity and differentiate low-grade form high-grade gliomas.

Electronic supplementary material

The online version of this article (doi:10.1186/s40644-015-0045-1) contains supplementary material, which is available to authorized users.

Peritumoral apparent diffusion coefficients for prediction of lymphovascular invasion in clinically node-negative invasive breast cancer

OBJECTIVES

To evaluate whether visual assessment of T2-weighted imaging (T2WI) or an apparent diffusion coefficient (ADC) could predict lymphovascular invasion (LVI) status in cases with clinically node-negative invasive breast cancer.

MATERIALS AND METHODS

One hundred and thirty-six patients with 136 lesions underwent MRI. Visual assessment of T2WI, tumour-ADC, peritumoral maximum-ADC and the peritumour-tumour ADC ratio (the ratio between them) were compared with LVI status of surgical specimens.

RESULTS

No significant relationship was found between LVI and T2WI. Tumour-ADC was significantly lower in the LVI-positive (n = 77, 896 ± 148 × 10(-6) mm(2)/s) than the LVI-negative group (n = 59, 1002 ± 163 × 10(-6) mm(2)/s; p < 0.0001). Peritumoral maximum-ADC was significantly higher in the LVI-positive (1805 ± 355 × 10(-6) mm(2)/s) than the LVI-negative group (1625 ± 346 × 10(-6) mm(2)/s; p = 0.0003). Peritumour-tumour ADC ratio was significantly higher in the LVI-positive (2.05 ± 0.46) than the LVI-negative group (1.65 ± 0.40; p < 0.0001). Receiver operating characteristic curve analysis revealed that the area under the curve (AUC) of the peritumour-tumour ADC ratio was the highest (0.81). The most effective threshold for the peritumour-tumour ADC ratio was 1.84, and the sensitivity, specificity, positive predictive value and negative predictive value were 77% (59/77), 76% (45/59), 81% (59/73) and 71% (45/63), respectively.

CONCLUSIONS

We suggest that the peritumour-tumour ADC ratio can assist in predicting LVI status on preoperative imaging.

KEY POINTS

• Tumour ADC was significantly lower in LVI-positive than LVI-negative breast cancer. • Peritumoral maximum-ADC was significantly higher in LVI-positive than LVI-negative breast cancer. • Peritumour-tumour ADC ratio was significantly higher in LVI-positive breast cancer. • Diagnostic performance of the peritumour-tumour ADC ratio was highest for positive LVI. • Peritumour-tumour ADC ratio showed higher diagnostic ability in postmenopausal than premenopausal patients.

MR mammography using diffusion-weighted imaging in evaluating breast cancer: a correlation with proliferation index

PURPOSE

To evaluate whether the variation of the apparent diffusion coefficient (ADC) values obtained with DWI can predict elevated levels of Ki67 proliferation index and aggressive subtypes in patients with breast cancer.

MATERIALS AND METHODS

Breast MRI studies of 115 patients (mean age 57.3 years, range 36-81 years) with a biopsy-proven breast cancers were evaluated in this retrospective IRB-approved study. Examinations were performed on a 1.5 T magnet and included a Single-Shot Echoplanar DWI sequence with b values of 0 and 1000 s/mm(2). For each target lesion, ADC was measured. ADC values were compared considering Ki67 status (≥20 % or <20 %), histology, grade (G1, G2 or G3) and clinical-pathological classification (Luminal A, Luminal B HER2-positive, Luminal B HER-2 negative, HER-2 enriched and Triple Negative). Mann-Whitney U test and Kruskal-Wallis test were used for comparisons and receiver operating characteristic (ROC) curves were obtained. Inter- and intra-reader variability was evaluated in a subset of 40 patients, using interclass correlation coefficient (ICC) and Bland-Altman plots.

RESULTS

Of 115 lesions, 53 (46.1 %) were assessed as Ki67 positive and 62 (53.9 %) as Ki67 negative. ADC values were significantly (p < 0.0001) lower in Ki67-positive patients (median 0.86 × 10(-3) mm(2)/s; interquartile range 0.75-0.92) compared to Ki67-negative (median 1.03 × 10(-3) mm(2)/s; interquartile range 0.92-1.13). Median ADC was also lower in G2 and G3 cancer and in the Luminal B Her2-negative subtype (p = 0.0015). No differences were found when evaluating histology. ROC curve showed a sensitivity and specificity of 83.0 and 66.1 %, respectively, when using a cutoff of 0.95 s/mm(2) to differentiate Ki67-positive from Ki67-negative cancers. Inter- and intra-reader variability was moderate (ICC = 0.623; ICC = 0.548, respectively). No systematic differences were identified with Bland-Altman plots.

CONCLUSIONS

Lower ADC values are associated with elevated Ki67 proliferation index and more aggressive pathologic features. Moderate agreement in ADC measurement could be a limitation.