Non-contrast-enhanced breast MRI for evaluation of tumor volume change after neoadjuvant chemotherapy.

PURPOSE: Three-dimensional contrast-enhanced magnetic resonance imaging (3D-Ce-MRI) is a most powerful tool for evaluation of neoadjuvant chemotherapy (NAC). However, the use of contrast agent is invasive, expensive, and time consuming, Thus, contrast agent-free imaging is preferable. We aimed to investigate the tumor volume change after NAC using maximum intensity projection diffusion-weighted image (MIP-DWI) and 3D-Ce-MRI. METHOD: We finally enrolled 55 breast cancer patients who underwent NAC in 2018. All MRI analyses were performed using SYNAPSE VINCENT® medical imaging system (Fujifilm Medical, Tokyo, Japan). We evaluated the tumor volumes before, during, and after NAC. Tumor volume before NAC on 3D-Ce-MRI was termed Pre-CE and those during and after NAC were termed Post-CE. The observer raised the lower end of the window width until the tumor was clearly visible and then manually deleted the non-tumor tissues. A month thereafter, the same observer who was blinded to the 3D-Ce-MRI results randomly evaluated the tumor volumes (Pre-DWI and Post-DWI) using MIP-DWI with the same method. Tumor volume change between ΔCE (Pre-CE - Post-CE/Pre-CE) and ΔDWI (Pre-DWI - Post-DWI/Pre-DWI) and the processing time for both methods (Time-DWI and Time-CE) were compared. RESULTS: We enrolled 55 patients. Spearman's rho between ΔDWI and ΔCE for pure mass lesions, and non-mass enhancement (NME) was 0.89 (p < 0.01), 0.63(p < 0.01) respectively. Time-DWI was significantly shorter than Time-CE (41.3 ± 21.2 and 199.5 ± 98.3 respectively, p < 0.01). CONCLUSIONS: Non-contrast-enhanced Breast MRI enables appropriate and faster evaluation of tumor volume change after NAC than 3D-Ce-MRI especially for mass lesions.

Can breast MRI and adjunctive Doppler ultrasound improve the accuracy of predicting pathological complete response after neoadjuvant chemotherapy?

BACKGROUND: To examine the accuracy of MRI and Doppler ultrasound (US) for detecting residual tumor after neoadjuvant chemotherapy (NAC) for breast cancer and evaluate whether adjunctive Doppler US improves the MRI accuracy. METHODS: We reviewed 276 invasive breast cancer cases treated with NAC. Tumors were classified into four subtypes based on estrogen receptor and HER2 status. Response to NAC was evaluated using contrast-enhanced MRI and Doppler US. Residual Doppler flow was assumed to indicate a residual tumor. MRI and Doppler findings were compared with the histopathological findings of resected specimens. Pathological complete response (pCR) was defined as neither in situ nor invasive cancer left. RESULTS: Of the 276 tumors, imaging complete responses were observed using MRI and Doppler US in 62 (22%) and 111 (40%), respectively, whereas pCR was achieved in 44 (16%). MRI and Doppler US predicted residual tumor with 88% and 69% sensitivity, 80% and 91% specificity, 87% and 73% accuracy, 96% and 98% PPV, and 56% and 36% NPV, respectively. The accuracies of MRI and Doppler US were significantly higher for HER2-negative than HER2-positive tumors (p < 0.001 and p = 0.043, respectively). Seven (26%) of 27 false-negative cases identified by MRI were correctly diagnosed as positives with adjunctive Doppler US. CONCLUSIONS: Although MRI accurately detected residual tumor with 87% accuracy, this was still not sufficient to meet clinical demands and differed with tumor subtype. Adjunctive Doppler US in cases that appear to show a complete response on MRI might reduce chances of false negatives and increase the NPV of MRI for predicting residual tumor.

Is the presence of edema and necrosis on T2WI pretreatment breast MRI the key to predict pCR of triple negative breast cancer?

PURPOSE: Given that a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is an important prognostic factor, evaluating pretreatment imaging findings is important. Outcomes for triple negative breast cancer (TNBC) vary with the histological classification, indicating that this classification is clinically significant. In this study, we focus on the most common histological subtype of TNBC, invasive carcinoma of no special type (NST), to evaluate whether intramammary edema (intra-E) and intratumoral necrosis (intra-N) on T2-weighted magnetic resonance imaging (T2WI) is a useful predictor of pCR. METHOD: We retrospectively included patients with biopsy-diagnosed TNBC-NST who received NAC between January 2014 and December 2017. Intra-E and intra-N were evaluated on T2WI before NAC. We grouped intra-E into no edema, peritumoral edema, prepectoral edema, and subcutaneous edema, and we defined intra-N as water-like signal intensity without enhancement on T2WI. We also evaluated tumor size, Ki-67 expression, and histological/nuclear grade, as well as their correlation with intra-E and intra-N. RESULTS: Fifty-seven patients with TNBC-NST were enrolled. There was no correlation with the rate of pCR and the presence of either intra-E or intra-N before NAC. Only intra-E and tumor size showed a positive correlation. CONCLUSIONS: In patients with TNBC-NST, intra-E and intra-N did not correlate with pCR, but intra-E did positively correlate with tumor size. NST may exhibit a greater response to NAC, regardless of whether intra-E or intra-N is present or not on the pretreatment MRI. KEY POINTS: • Pathological complete response in TNBC-NST had no correlation with intramammary edema or intratumoral necrosis. • NAC may be justified in TNBC-NST even in the presence of edema or necrosis. • The extension of edema correlated with tumor size of TNBC-NST.

Imaging features of breast cancer with marked hemosiderin deposition: A case report.

A 63-year-old woman was referred to our hospital for breast cancer treatment. She had a large HER2-positive breast tumor on her left breast, and received neoadjuvant chemotherapy. After treatment, a shrunk spiculated mass with calcification-like high density was detected on mammography, and MRI revealed a large strong susceptibility artifact. Surgical specimen analysis attributed these imaging features to a large marked hemosiderin deposition. This case is herein reported due to its rarity and to the importance of acknowledging that this large marked hemosiderin depositions can present as a calcification-like high density on mammography and shows large susceptibility artifact on MRI imaging.

MRI-detected breast lesions: clinical implications and evaluation based on MRI/ultrasonography fusion technology.

Magnetic resonance imaging (MRI) is a highly sensitive imaging modality that frequently reveals additional breast lesions that are occult on mammography and ultrasonography (US) and are thus difficult to diagnose. It is important to investigate these MRI-detected suspicious lesions, which are associated with a fairly high rate of malignancy. In this review, we have discussed MRI/US fusion technology, a magnetic position tracking system that synchronizes real-time US and MRI to improve lesion detection and enables comparisons of MRI and US findings of the detected lesions. This combination increases the precision of second-look US. We hope that our review underscores the importance of understanding the US findings and histopathology of MRI-detected breast lesions, as this will enable radiologists to perform appropriate assessments.

Prediction of high-stage liver fibrosis using ADC value on diffusion-weighted imaging and quantitative enhancement ratio at the hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI at 1.5 T.

Background Recently, diffusion-weighted imaging (DWI) and quantitative enhancement ratio measured at the hepatobiliary phase (HBP) of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) has been established as an effective method for evaluating liver fibrosis. Purpose To evaluate which is a more favorable surrogate marker in predicting high-stage liver fibrosis, apparently diffusion coefficient (ADC) value or quantitative enhancement ratio measured on HBP. Material and Methods Eighty-three patients with 99 surgically resected hepatic lesions were enrolled in this study. DWI was performed with b-values of 100 and 800 s/mm2. Regions of interest were set on ADC map, and the HBP of Gd-EOB-DTPA-enhanced MRI, to calculate ADC value, liver-to-muscle ratio (LMR), liver-to-spleen ratio (LSR), and contrast enhancement index (CEI) of liver. We compared these parameters between low-stage fibrosis (F0, F1, and F2) and high-stage fibrosis (F3 and F4). Receiver operating characteristic analysis was performed to compare the diagnostic performance when distinguishing low-stage fibrosis from high-stage fibrosis. Results LMR and CEI were significantly lower at high-stage fibrosis than at the low stage ( P < 0.01 and P = 0.04, respectively), whereas LSR did not show a significant difference ( P = 0.053). No significant difference was observed in diagnostic performance between LMR and CEI ( P = 0.185). The best sensitivity and specificity, when an LMR of 2.80 or higher was considered to be low-stage fibrosis, were 82.4% and 75.6%, respectively. ADC value showed no significant differences among fibrosis grades ( P = 0.320). Conclusion LMR and CEI were both adequate surrogate parameters to distinguish high-stage fibrosis from low-stage fibrosis.

3D analysis of apparent diffusion coefficient histograms in hepatocellular carcinoma: correlation with histological grade.

BACKGROUND: To evaluate the usefulness of differentiation of histological grade in hepatocellular carcinoma (HCC) using three-dimensional (3D) analysis of apparent diffusion coefficient (ADC) histograms retrospectively. METHODS: The subjects consisted of 53 patients with 56 HCCs. The subjects included 12 well-differentiated, 35 moderately differentiated, and nine poorly differentiated HCCs. Diffusion-weighted imaging (b-values of 100 and 800 s/mm2) were obtained within 3 months before surgery. Regions of interest (ROIs) covered the entire tumor. The data acquired from each slice were summated to derive voxel-by-voxel ADCs for the entire tumor. The following parameters were derived from the ADC histogram: mean, standard deviation, minimum, maximum, mode, percentiles (5th, 10th, 25th, 50th, 75th, and 90th), skew, and kurtosis. These parameters were analyzed according to histological grade. After eliminating steatosis lesions, these parameters were re-analyzed. RESULTS: A weak correlation was observed in minimum ADC and 5th percentile for each histological grade (r = -0.340 and r = -0.268, respectively). The minimum ADCs of well, moderately, and poorly differentiated HCC were 585 ± 388, 411 ± 278, and 235 ± 102 × 10-6 mm2/s, respectively. Minimum ADC showed significant differences among tumor histological grades (P = 0.009). The minimum ADC of poorly differentiated HCC and that of combined well and moderately differentiated HCC were 236 ± 102 and 437 ± 299 × 10-6 mm2/s. The minimum ADC of poorly differentiated HCC was significantly lower than that of combined well and moderately differentiated HCC (P = 0.001). The sensitivity and specificity, when a minimum ADC of 400 × 10-6 mm2/s or lower was considered to be poorly differentiated HCC, were 100 and 54%, respectively. After exclusion of the effect of steatosis, the sensitivity and specificity did not change, although the statistical differences became strong (P < 0.0001). CONCLUSION: Minimum ADC was most useful to differentiate poorly differentiated HCC in 3D analysis of ADC histograms.

Diffusion-weighted imaging of the liver: Current applications.

Diffusion-weighted imaging (DWI) of the liver can be performed using most commercially available machines and is currently accepted in routine sequence. This sequence has some potential as an imaging biomarker for fibrosis, tumor detection/characterization, and following/predicting therapy. To improve reliability including accuracy and reproducibility, researchers have validated this new technique in terms of image acquisition, data sampling, and analysis. The added value of DWI in contrast-enhanced magnetic resonance imaging was established in the detection of malignant liver lesions. However, some limitations remain in terms of lesion characterization and fibrosis detection. Furthermore, the methodologies of image acquisition and data analysis have been inconsistent. Therefore, researchers should make every effort to not only improve accuracy and reproducibility but also standardize imaging parameters.