加藤 扶美

PURPOSE: To evaluate the effect of model-based deep-learning reconstruction (DLR) compared with that of compressed sensing-sensitivity encoding (CS) on cine cardiac magnetic resonance (CMR). METHODS: Cine CMR images of 10 healthy volunteers were obtained with reduction factors of 2, 4, 6, and 8 and reconstructed using CS and DLR. The visual image quality scores assessed sharpness, image noise, and artifacts. Left-ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) were manually measured. LV global circumferential strain (GCS) was automatically measured using the software. The precision of EDV, ESV, SV, EF, and GCS measurements was compared between CS and DLR using Bland-Altman analysis with full-sampling data as the gold standard. RESULTS: Compared with CS, DLR significantly improved image quality with reduction factors of 6 and 8. The precision of EDV and ESV with a reduction factor of 8, and GCS with reduction factors of 6 and 8 measurements improved with DLR compared with CS, whereas those of SV and EF measurements were not different between DLR and CS. CONCLUSION: The effect of DLR on cine CMR's image quality and precision in evaluating quantitative volume and strain was equal or superior to that of CS. DLR may replace CS for cine CMR.

BACKGROUND: Conventional ultrasonography (cUS) and contrast-enhanced ultrasonography (CEUS) are used to evaluate breast cancer tumors and axillary lymph nodes (ALN), by which the treatment strategy for breast cancer is determined. A breast tumor size discrepancy on CEUS compared with cUS is often observed, for which the reasons are unclear. We hypothesized that this discrepancy reflects the metastatic potential, and this study investigated the association between size discrepancies on cUS and CEUS in relation to ALN metastasis in breast cancer. METHODS: This retrospective study enrolled 259 patients who underwent surgery for breast cancer after preoperative cUS and CEUS examinations. Patients were grouped into a DISCR (i.e., tumor size discrepancy ≥ 4.0 mm between CEUS and cUS measurements) and non-DISCR group. The primary outcome was ALN metastasis, defined by pathological evaluation. Secondary outcomes were the 5-year recurrence-free survival rates. RESULTS: There were 94 patients in the DISCR and 165 in the non-DISCR groups. No tumor size differences measured by cUS were observed between two groups (p = 0.82), whereas the DISCR group had a significantly higher rate of ALN metastasis (p < 0.01). Multivariate analyses showed a discrepancy of ≥ 4.0 mm was a risk for ALN metastasis (odds ratio: 5.838, 95% confidence interval [CI]: 2.408-14.155). The 5-year recurrence-free survival rate was lower in the DISCR (0.750, 95% CI: 0.632-0.868) than in the non-DISCR (0.924, 95% CI; 0.870-0.978) group. CONCLUSION: An increase in contrast-enhanced ultrasonography tumor size is helpful for assessing axillary lymph node metastasis and prognosis.

Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully elucidated. Hence, we developed a bimodal model to predict ALN metastasis in patients with early breast cancer by integrating CEUS images with the annotated imaging features. The model adopted a light-gradient boosting machine to produce feature importance, enabling the extraction of clinically crucial imaging features. In this retrospective study, the diagnostic performance of the model was investigated using 788 CEUS images of ALNs obtained from 788 patients who underwent breast surgery between 2013 and 2021, with the ground truth defined by the pathological diagnosis. The results indicated that the test cohort had an area under the receiver operating characteristic curve (AUC) value of 0.93 (95% confidence interval: 0.88, 0.98). The model had an accuracy of 0.93, which was higher than the radiologist's diagnosis (accuracy of 0.85). The most important imaging features were heterogeneous enhancement, diffuse cortical thickening, and eccentric cortical thickening. Our model has an excellent diagnostic performance, and the extracted imaging features could be crucial for confirming ALN metastasis in clinical settings.

PURPOSE: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). METHODS: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. RESULTS: In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). CONCLUSION: Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.

We investigated the ability of echo-planar imaging with L1-regularized iterative sensitivity encoding-based diffusion-weighted imaging (DWI) to improve the image quality and reduce the scanning time in prostate magnetic resonance imaging. We retrospectively analyzed 109 cases of prostate magnetic resonance imaging. We compared variables in the quantitative and qualitative assessments among 3 imaging groups: conventional parallel imaging-based DWI (PI-DWI) with an acquisition time of 3 minutes 15 seconds; echo-planar imaging with L1-regularized iterative sensitivity encoding-based DWI (L1-DWI) with a normal acquisition time (L1-DWINEX12) of 3 minutes 15 seconds; and L1-DWI with a half acquisition time (L1-DWINEX6) of 1 minute 45 seconds. As a quantitative assessment, the signal-to-noise ratio (SNR) of DWI (SNR-DWI), the contrast-to-noise ratio (CNR) of DWI (CNR-DWI), and the CNR of apparent diffusion coefficient were measured. As a qualitative assessment, the image quality and visual detectability of prostate carcinoma were evaluated. In the quantitative analysis, L1-DWINEX12 showed significantly higher SNR-DWI than PI-DWI (P = .0058) and L1-DWINEX6 (P < .0001). In the qualitative analysis, the image quality score for L1-DWINEX12 was significantly higher than those of PI-DWI and L1-DWINEX6. A non-inferiority assessment demonstrated that L1-DWINEX6 was non-inferior to PI-DWI in terms of both quantitative CNR-DWI and qualitative grading of image quality with a <20% inferior margin. L1-DWI successfully demonstrated a reduced scanning time while maintaining good image quality.

Most oncogenic human papilloma virus (HPV) genotypes stratify into two species, α-7 HPV and α-9 HPV. There are several studies that evaluate the relationship between HPV species and treatment outcomes and reports that HPV species is prognostic. The HPV genotyping was conducted using biopsy specimens which had been stored in these studies. We conducted the study using the HPV test performed by cytology specimens which is less invasive and more useful in clinical settings. This study enrolled 46 patients who received HPV genotyping before the definitive radiotherapy. The results of the HPV genotyping were classified into HPVα-7, HPVα-9 and negatives. Of the 46 patients, 10 were positive for HPVα-7, 21 positive for HPVα-9 and 15 were negative. The median follow-up period was 38 months (range 4-142). The HPVα-7, HPVα-9 and negative groups showed the 3-year overall survival (OS; 59.3%, 80.4% and 72.2% [P = 0.25]); local control (LC; 67.5%, 81% and 80% [P = 0.78]); pelvic control (PC) (50%, 81% and 72.7% [P = 0.032]); pelvic lymph node (PLN) control (78.7%, 95% and 92.3% [P = 0.012]); distant metastasis free (DMF) survival (50%, 75.4% and 42.8% [P = 0.098]); and progression free survival (PFS) rate of patients (30%, 66.7% and 38.9% [P = 0.085]), respectively. Patients with HPVα-7 showed statistically significant poorer PC than the HPVα-9 group, in multivariate analysis. This result is consistent with previous studies for HPV positive patients. The HPV negativity rate was higher in this study than in other studies and further work on this may be needed for clinical use.

We present a case of breast angiosarcoma. Although B-mode ultrasonography did not indicate a tumor, contrast-enhanced ultrasonography (CEUS) was successfully delineated it. CEUS helped identify the tumor and its extent.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

A 62-year-old woman presented with a tumor in the right kidney. A right partial nephrectomy was performed, and the tumor was diagnosed as clear cell renal cell carcinoma (RCC) on histopathological examination. A right ovarian tumor was detected on follow-up computed tomography (CT) 5 years after partial nephrectomy and pathology proved RCC metastasis. RCC rarely metastasizes to the ovaries. There is limited information on the radiological features of ovarian metastasis in RCC. In this case report, we presented the CT and magnetic resonance images of ovarian metastasis of RCC. In addition, we also presented a literature review with special emphasis on the imaging features of ovarian metastasis of RCC.

Although 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is an established method for the staging of malignancies, benign lesions (e.g, active inflammatory lesions) often show increased metabolic activity. Herpes zoster is the clinical manifestation of the activation and replication of dormant varicella-zoster virus (VZV) in individuals with decreased cell-mediated immunity. Although the diagnosis of herpes zoster is clinical, it is sometimes observed incidentally during imaging for another disease. We describe the case of a 67-year-old Japanese female patient diagnosed with cervical cancer in whom FDG-PET/CT revealed herpes zoster manifestations: hypermetabolic cutaneous lesions in the buttock and pelvic lymph node involvement. The resected lymph nodes showed no malignant lesions but revealed lymphoid follicle formation, probably related to viral infection. There has been no report comparing FDG-PET findings of lymph nodes with histologic findings; the present findings are compatible with a clinically VZV-induced inflammatory reaction in regional lymph nodes, which increased FDG accumulation. Active infection with VZV displays increased FDG uptake in regional lymph nodes and may lead to incorrect malignant disease management in oncology. Misdiagnoses can be avoided by a careful interpretation by experienced nuclear medicine physicians as well as proper clinical evaluation.

PURPOSE: This study aimed to examine MRI features and staging of neuroendocrine carcinoma (NEC) of the endometrium and evaluate survival. METHODS: Clinical data, pathological, and preoperative pelvic MRI findings in 22 patients with histologically surgery-proven endometrial NEC were retrospectively reviewed. Tumors were pure NEC (n = 10) or mixed histotype (n = 12), with 13 large and nine small cell type. RESULTS: International Federation of Gynecology and Obstetrics (FIGO) staging was I, II, III, and IV in 6, 2, 12, and 2 patients, respectively. In 13 (76.4%) of 17 patients with pathological deep myometrial invasion, MRI showed abnormal diffusely infiltrative high T2 signal intensity throughout the myometrium with loss of normal uterine architecture. All tumors had restricted diffusion (apparent diffusion coefficient map low signal intensity, diffusion weighted imaging high signal intensity). Accuracy of T staging by MRI for all cases was 81.8%, with reference to pathology staging, while patient-based sensitivity, specificity, and accuracy for detecting metastatic pelvic lymph nodes was 60.0%, 100%, and 77.8%, respectively. Two intrapelvic peritoneal dissemination cases were detected by MRI. During follow-up (mean 30.4, range 3.3-138.4 months), 16 patients (72.7%) experienced recurrence and 12 (54.5%) died of disease. Two-year disease-free and overall survival rates for FIGO I, II, III, and IV were 66.7% and 83.3%, 50% and 100%, 10% and 33.3%, and 0% and 0%, respectively. CONCLUSION: Abnormal diffusely infiltrative high T2 signal intensity throughout the myometrium with normal uterine architecture loss and obvious restricted diffusion throughout the tumor are suggestive features of endometrial NEC. Pelvic MRI is reliable for intrapelvic staging of affected patients.

OBJECTIVES: To investigate neuroendocrine carcinoma (NEC) of the uterine cervix cases for MRI features and staging, as well as pathological correlations and survival. RESULTS: FIGO was I in 42, II in 14, III in 1, and IV in 5 patients. T2-weighted MRI showed homogeneous slightly high signal intensity and obvious restricted diffusion (ADC map, low intensity; DWI, high intensity) throughout the tumor in most cases, and mild enhancement in two-thirds. In 50 patients who underwent a radical hysterectomy and lymphadenectomy without neoadjuvant chemotherapy (NAC), intrapelvic T staging by MRI overall accuracy was 88.0% with reference to pathology staging, while patient-based sensitivity, specificity, and accuracy for metastatic pelvic lymph node detection was 38.5%, 100%, and 83.3%, respectively. During a mean follow-up period of 45.6 months (range 4.3-151.0 months), 28 patients (45.2%) experienced recurrence and 24 (38.7%) died. Three-year progression-free and overall survival rates for FIGO I, II, III, and IV were 64.3% and 80.9%, 50% and 64.3%, 0% and 0%, and 0% and 0%, respectively. MATERIALS AND METHODS: Sixty-two patients with histologically surgery-proven uterine cervical NEC were enrolled. Twelve received NAC. Clinical data, pathological findings, and pretreatment pelvic MRI findings were retrospectively reviewed. Thirty-two tumors were pure NEC and 30 mixed with other histotypes. The NECs were small cell type (41), large cell type (18), or a mixture of both (3). CONCLUSIONS: Homogeneous lesion texture with obvious restricted diffusion throughout the tumor are features suggestive of cervical NEC. Our findings show that MRI is reliable for T staging of cervical NEC.

The aim of this study was to evaluate the effect of a novel 4-dimensional similarity filter (4DSF) on quantitative and qualitative parameters of low-dose dynamic myocardial computed tomography perfusion (CTP) images.In this retrospective study, medical records of 32 patients with suspected or known coronary artery disease who underwent dynamic myocardial CTP at 80 kV were included. The 4DSF reduces noise by averaging voxels that have similar dynamic behavior after adaptive iterative dose reduction 3D (AIDR3D) and deformable image registration were applied. Qualitative (artefact, contour sharpness, and myocardial homogeneity [1 = poor; 2 = intermediate; 3 = good]) and quantitative measurement (standard deviation [SD] and signal-to-noise ratio [SNR]) were compared between the 4DSF and AIDR3D. Contrast-to-noise ratio (CNR) between ischemic and normal remote myocardium was also assessed using myocardial perfusion magnetic resonance imaging as the reference standard in seven patients.The 4DSF was successfully applied to all the images. Improvement in subjective image quality yielded by 4DSF was higher than that yielded by AIDR3D (homogeneity, 1.0 [3 vs 2]; artefact, 1.5 [3 vs 1.5]; P < .001) in all patients. The 4DSF significantly decreased the SD by 59% (AIDR3D vs 4DSF: 33.5 ± 0.4 vs 13.8 ± 0.4, P < .001), increased the SNR by 134% (AIDR3D vs 4DSF: 4.4 ± 0.2 vs 10.3 ± 0.2, P < .001), and increased the CNR by 131% (AIDR3D vs 4DSF: 1.6 ± 0.2 vs 3.7 ± 0.2, P < .001).The 4DSF improved the qualitative and quantitative parameters of low-dose dynamic myocardial CTP images.

BACKGROUND: We investigated the usefulness of the minimum ADC value of primary breast lesions for predicting axillary lymph node (LN) status in luminal A-like breast cancers with clinically negative nodes in comparison with the mean ADC. METHODS: Forty-four luminal A-like breast cancers without axillary LN metastasis at preoperative clinical evaluation, surgically resected with sentinel LN biopsy, were retrospectively studied. Mean and minimum ADC values of each lesion were measured and statistically compared between LN positive (n = 12) and LN negative (n = 32) groups. An ROC curve was drawn to determine the best cutoff value to differentiate LN status. Correlations between mean and minimum ADC values and the number of metastatic axillary LNs were investigated. RESULTS: Mean and minimum ADC values of breast lesions with positive LN were significantly lower than those with negative LN (mean 839.9 ± 110.9 vs. 1022.2 ± 250.0 × 10- 6 mm2/s, p = 0.027, minimum 696.7 ± 128.0 vs. 925.0 ± 257.6 × 10- 6 mm2/s, p = 0.004). The sensitivity and NPV using the best cutoff value from ROC using both mean and minimum ADC were 100%. AUC of the minimum ADC (0.784) was higher than that of the mean ADC (0.719). Statistically significant negative correlations were observed between both mean and minimum ADCs and number of positive LNs, with stronger correlation to minimum ADC than mean ADC. CONCLUSIONS: The minimum ADC value of primary breast lesions predicts axillary LN metastasis in luminal A-like breast cancer with clinically negative nodes, with high sensitivity and high NPV.

PURPOSE: The aim of this study was to verify coronary flow velocity reserve (CFVR) on the left main trunk (LMT) in comparison with myocardial flow reserve (MFR) by 15O-labeled water positron emission tomography (PET) (MFR-PET) in both the healthy adults and the patients with coronary artery disease (CAD), and to evaluate the feasibility of CFVR to detect CAD. METHODS: Eighteen healthy adults and 13 patients with CAD were evaluated. CFVR in LMT was estimated by 3T magnetic resonance imaging (MRI) with phase contrast technique. MFR-PET in the LMT territory including anterior descending artery and circumflex artery was calculated as the ratio of myocardial blood flow (MBF)-PET at stress to MBF-PET at rest. RESULTS: There was a significant positive relationship between CFVR and MFR-PET (R = 0.45, P < 0.0001). Inter-observer calculations of CFVR showed good correlation (R2 = 0.93, P < 0.0001). The CFVR in patients with CAD was significantly lower than that in healthy adults (1.90 ± 0.61 vs. 2.77 ± 1.03, respectively, P = 0.01), which were similar to the results of MFR-PET (2.23 ± 0.84 vs. 3.96 ± 1.04, respectively, P < 0.0001). For the detection of patients with CAD, the area under the curve was 0.78 (P = 0.01). The sensitivity was 0.77 and specificity was 0.72 when a cut-off of 2.15 was used. CONCLUSION: CFVR by 3T was validated with MFR-PET. CFVR could detect the patients with CAD. This method is a simple and reliable index without radiation or contrast material.

PURPOSE: To evaluate the effect of the menstrual cycle on BPE and cancer detectability in an Asian population. MATERIAL AND METHODS: 266 premenopausal patients with regular menstrual cycles from 24 centers were included, and 176 of them were diagnosed by pathology as having breast cancer. Thirty-five patients were examined in the menstrual phase (days 1-4), 105 in the proliferative phase (days 5-14), and 126 in the secretory phase (days 15-30). Measurement of the following signal intensities (SIs) were obtained: breast tissue on the unaffected side on a pre-contrast image (SI1) and an early-phase image (SI2); the SIs of breast tissue on the affected side on a pre-contrast image (SI3) and an early-phase image (SI4); and the SIs of breast cancer on a pre-contrast image (SI5) and an early-phase image (SI6). We calculated the BPE ratio, i.e., (SI2- SI1)/SI1 and the cancer/background enhancement ratio (C/B) ratio, i.e., (SI6- SI5) / (SI4- SI3). The BPE was classified as minimal, mild, moderate, or marked, and the cancer detectability was classified as excellent, good, or poor independently by two radiologists. RESULTS: The average C/B ratio was 20.1, 15.7, and 9.1 at the menstrual, proliferative, and secretory phases (p < 0.001). BPE was determined as moderate or marked in 0% and 5.4% at the menstrual phase, 10.3% and 11.0% at the proliferative phase, and 17.5% and 21.7% at the secretory phase by the two observers, respectively (p = 0.01, p = 0.01). The detectability of breast cancer was classified as poor in 0% and 0%, 1.4% and 13.0%, and 8.0% and 22.1% at the menstrual, proliferative, and secretory phases by the two observers, respectively (p = 0.07, p = 0.02). CONCLUSION: The menstrual phase and the proliferative phase seem to be suitable for breast MRI of Asian women.