Evaluating the diagnostic efficacy of whole-body MRI versus 123I-mIBG/131I-mIBG imaging in metastatic pheochromocytoma and paraganglioma.

This study aimed to compare tumor lesion detectability and diagnostic accuracy of whole-body magnetic resonance imaging (WB-MRI) and radioiodine-labeled meta-iodo-benzylguanidine (mIBG) imaging techniques in patients with metastatic pheochromocytoma and paraganglioma (PPGL). This retrospective study included 13 patients had pheochromocytoma and 5 had paraganglioma, who were all suspected of having metastatic tumors. Each patient underwent WB-MRI and 123I-mIBG as a pretreatment screening for 131I-mIBG therapy. Two expert reviewers evaluated WB-MRI, 123I-mIBG images, and post-therapy 131I-mIBG images for the presence of metastatic lesions in the lungs, bones, liver, lymph nodes, and other organs. Diagnostic measures for detecting metastatic lesions, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristics (ROC)-area under the curve (AUC), were calculated for each imaging technique. We analyzed WB-MRI images for detecting metastatic lesions, which demonstrated sensitivity, specificity, accuracy, PPV, NPV, and AUC of 82%, 97%, 90%, 96%, 86%, and 0.92, respectively. These values were 83%, 95%, 89%, 94%, 86%, and 0.90 in 123I-mIBG images and 85%, 92%, 89%, 91%, 87%, and 0.91 in post-therapy 131I-mIBG images, respectively. Our results reveal the comparable diagnostic accuracy of WB-MRI to one of the mIBG images.

Reactive lymphoid hyperplasia of the liver: A case report featuring characteristic nodular and perinodular enhancement.

A 53-year-old female with primary biliary cholangitis was referred for the evaluation of a hepatic nodule identified during routine imaging. Ultrasonography revealed a homogeneous, hypoechoic, 18 mm nodule in segment 3 of the liver. On dynamic CT and MRI, the nodule showed mild enhancement at the hepatic artery-dominant phase. On diffusion-weighted images, the nodule exhibited pronounced hyperintensity with accompanying wedge-shaped perinodular hyperintensity (comet and comet-tail appearance). The nodule showed a portal perfusion defect on CT during arterial portography, and mild enhancement on CT during hepatic arteriography (CTHA). A nodular and wedge-shaped perinodular enhancement (comet and comet-tail appearance) in the CTHA was also clearly observed. The nodule demonstrated abnormal FDG uptake on 18F-FDG-PET/CT. An excisional biopsy was performed for histopathological diagnosis, and the nodule was diagnosed as reactive lymphoid hyperplasia (RLH). Diagnosing hepatic RLH by imaging is challenging due to its imaging findings overlapping with those of various malignant tumors, especially the nodular type of lymphomas, making differentiation particularly difficult. However, radiologists should note the perinodular early enhancement and the perinodular hyperintensity on diffusion weighted images, which are thought to be key imaging findings of RLH, along with other characteristics such as a single, small, homogeneous nodule with mild early enhancement and marked restricted diffusion. We propose to name the nodular lesion with perinodular early enhancement/hyperintensity on diffusion weighted images as 'comet and comet-tail appearances'.

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients with Multiple Myeloma using Whole-body MRI.

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers. We defined the occurrence of progressive disease after MRI evaluation within 12 months as a poor prognosis and constructed a 3D CNN-based deep learning model to predict prognosis. Images from 111 cases were used as the training and internal validation data; images from 31 cases were used as the external validation data. Internal validation of the AI model with stratified 5-fold cross-validation resulted in a significant difference in progression-free survival (PFS) between good and poor prognostic cases (2-year PFS, 91.2% versus [vs.] 61.1%, P = 0.0002). The AI model clearly stratified good and poor prognostic cases in the external validation cohort (2-year PFS, 92.9% vs. 55.6%, P = 0.004), with an area under the receiver operating characteristic curve of 0.804. According to Grad-CAM, the MRI signals of the spleen and bones of the vertebrae and pelvis contributed to prognosis prediction. This study is the first to show that image analysis of whole-body MRI using a 3D CNN without any other clinical data is effective in predicting the prognosis of patients with MM.

Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer.

PURPOSE: This study aimed to evaluate the performance of the commercially available artificial intelligence-based software CXR-AID for the automatic detection of pulmonary nodules on the chest radiographs of patients suspected of having lung cancer. MATERIALS AND METHODS: This retrospective study included 399 patients with clinically suspected lung cancer who underwent CT and chest radiography within 1 month between June 2020 and May 2022. The candidate areas on chest radiographs identified by CXR-AID were categorized into target (properly detected areas) and non-target (improperly detected areas) areas. The non-target areas were further divided into non-target normal areas (false positives for normal structures) and non-target abnormal areas. The visibility score, characteristics and location of the nodules, presence of overlapping structures, and background lung score and presence of pulmonary disease were manually evaluated and compared between the nodules detected or undetected by CXR-AID. The probability indices calculated by CXR-AID were compared between the target and non-target areas. RESULTS: Among the 450 nodules detected in 399 patients, 331 nodules detected in 313 patients were visible on chest radiographs during manual evaluation. CXR-AID detected 264 of these 331 nodules with a sensitivity of 0.80. The detection sensitivity increased significantly with the visibility score. No significant correlation was observed between the background lung score and sensitivity. The non-target area per image was 0.85, and the probability index of the non-target area was lower than that of the target area. The non-target normal area per image was 0.24. Larger and more solid nodules exhibited higher sensitivities, while nodules with overlapping structures demonstrated lower detection sensitivities. CONCLUSION: The nodule detection sensitivity of CXR-AID on chest radiographs was 0.80, and the non-target and non-target normal areas per image were 0.85 and 0.24, respectively. Larger, solid nodules without overlapping structures were detected more readily by CXR-AID.

Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.

Background: A reproducible and accurate automated approach to measuring cardiothoracic ratio on chest radiographs is warranted. This study aimed to develop a deep learning-based model for estimating the cardiothoracic ratio on chest radiographs without requiring self-annotation and to compare its results with those of manual measurements. Methods: The U-net architecture was designed to segment the right and left lungs and the cardiac shadow, from chest radiographs. The cardiothoracic ratio was then calculated using these labels by a mathematical algorithm. The initial model of deep learning-based cardiothoracic ratio measurement was developed using open-source 247 chest radiographs that had already been annotated. The advanced model was developed using a training dataset of 729 original chest radiographs, the labels of which were generated by the initial model and then screened. The cardiothoracic ratio of the two models was estimated in an independent test set of 120 original cases, and the results were compared to those obtained through manual measurement by four radiologists and the image-reading reports. Results: The means and standard deviations of the cardiothoracic ratio were 52.4% and 9.8% for the initial model, 51.0% and 9.3% for the advanced model, and 49.8% and 9.4% for the total of four manual measurements, respectively. The intraclass correlation coefficients (ICCs) of the cardiothoracic ratio ranged from 0.91 to 0.93 between the advanced model and the manual measurements, whereas those for the initial model and the manual measurements ranged from 0.77 to 0.82. Conclusions: Deep learning-based cardiothoracic ratio estimation on chest radiographs correlated favorably with the results obtained through manual measurements by radiologists. When the model was trained on additional local images generated by the initial model, the correlation with manual measurement improved even more than the initial model alone.

Fat fraction and R2 * values of various liver masses: Initial experience with 6-point Dixon method on a 3T MRI system.

Purpose: To assess the feasibility of the 6-point Dixon method for evaluating liver masses. We also report our initial experience with the quantitative values in various liver masses on a 3T system. Materials and methods: Of 251 consecutive patients for whom 6-point Dixon was employed in abdominal magnetic resonance imaging scans between October 2020 and October 2021, 117 nodules in 117 patients with a mass diameter of more than 1 cm were included in the study. Images for measuring the proton density fat fraction (PDFF) and R2 * values were obtained using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation-quantitative technique for liver imaging. Two radiologists independently measured PDFF (%) and R2 * (Hz). Inter-reader agreement and the differences between readers were examined using intra-class correlation coefficient (ICC) and the Bland-Altman method, respectively. PDFF and R2 * values in differentiating liver masses were examined. Results: The masses included hepatocellular carcinoma (n = 59), cyst (n = 20), metastasis (n = 14), hemangioma (n = 8), and others (n = 16). The ICCs for the region of interest (mm2), PDFF, and R2 * were 0.988 (95 % confidence interval (CI): 0.983, 0.992), 0.964 (95 % CI: 0.949, 0.975), and 0.962 (95 % CI: 0.941, 0.975), respectively. The differences of measurements between the readers showed that 5.1 % (6/117) and 6.0% (7/117) for PDFF and R2 * , respectively, were outside the 95 % CI. Conclusion: Our observation indicates that the 6-point Dixon method is applicable to liver masses.

Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location.

PURPOSE: Computer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evaluated the benefits and pitfalls of CAD in the real world. This study aimed to evaluate the independent performance of commercially available deep learning-based automatic detection (DLAD) software, EIRL Chest X-ray Lung Nodule, in a cohort that included patients with background pulmonary abnormalities often encountered in clinical situations. METHODS: Patients with clinically suspected lung cancer for whom chest radiography was performed within a month before or after CT scan between June 2020 and May 2022 in our institution were enrolled. The reference standard was created using a bounding box annotated by two radiologists with reference to the CT. The visibility score, characteristics, location of the pulmonary nodules, presence of overlapping structures or pulmonary disease, and background lung score were manually determined. RESULTS: We included 388 patients. The DLAD software detected 222 of the 322 nodules visible on manual evaluation, with a sensitivity of 0.689 and a false-positive rate of 0.168. The detectability of the DLAD software was significantly lower for small and subsolid and nodules with overlapping structures. The visibility score and sensitivity of detection by the DLAD software were positively correlated. The relationship between the background lung score and detection by the DLAD software was unclear. CONCLUSION: The standalone performance of DLAD in detecting pulmonary nodules exhibited a sensitivity of 0.689 and a false-positive rate of 0.168. Understanding the characteristics of DLAD is crucial when interpreting chest radiographs with the assistance of the DLAD.

Intraductal Tubulopapillary Neoplasm Diagnosed Before Surgery: A Case Report.

Intraductal tubulopapillary neoplasm (ITPN) is a rare disease in the pancreas with a better prognosis than pancreatic ductal adenocarcinoma (PDAC) and a different treatment strategy. Therefore, it is important to confirm its diagnosis before the surgery. However, few cases have been diagnosed preoperatively. In this report, we present a case of ITPN that was successfully diagnosed preoperatively. A 70-year-old female patient was incidentally diagnosed with a pancreatic tumor. The patient was asymptomatic, and her blood tests were all within the normal range. A dynamic computed tomography scan showed an indistinct mass with small cysts and a dilated pancreatic duct. The mass was well contrasted in the arterial phase. These findings were not enough to confirm ITPN. Therefore, endoscopic ultrasonography fine needle aspiration biopsy was performed. The specimen had no mucin and the neoplastic cells exhibited a tubulopapillary growth pattern. Moreover, the neoplastic cells were immunohistochemically positive for MUC1, CK7, and CK20, but negative for MUC2, MUC5AC, synaptophysin, and Bcl-10. Consequently, the preoperative diagnosis was confirmed as ITPN. Hence, a subtotal-stomach-preserving pancreaticoduodenectomy was performed, and the patient had a good postoperative course and was discharged after 26 days. Tegafur, gimeracil, and oteracil were administered as postoperative adjuvant chemotherapies for one year. Seventeen months after the surgery, no recurrence has been detected. ITPN and PDAC have different prognoses and treatment strategies. In this report, we experienced a case of ITPN preoperatively diagnosed and successfully treated.

Computer-Aided Detection of Subsolid Nodules on Chest Computed Tomography: Assessment of Visualization on Vessel-Suppressed Images.

OBJECTIVES: This study aimed to clarify the performance of automatic detection of subsolid nodules by commercially available software on computed tomography (CT) images of various slice thicknesses and compare it with visualization on the accompanying vessel-suppression CT (VS-CT) images. METHODS: A total of 95 subsolid nodules from 84 CT examinations of 84 patients were included. The reconstructed CT image series of each case with 3-, 2-, and 1-mm slice thicknesses were loaded into a commercially available software application (ClearRead CT) for automatic detection of subsolid nodules and generation of VS-CT images. Automatic nodule detection sensitivity was assessed for 95 nodules on each series of images acquired at 3 slice thicknesses. Four radiologists subjectively evaluated visual assessment of the nodules on VS-CT. RESULTS: ClearRead CT automatically detected 69.5% (66/95 nodules), 68.4% (65/95 nodules), and 70.5% (67/95 nodules) of all subsolid nodules in 3-, 2-, and 1-mm slices, respectively. The detection rate was higher for part-solid nodules than for pure ground-glass nodules at all slice thicknesses. In the visualization assessment on VS-CT, 3 nodules at each slice thickness (3.2%) were judged as invisible, while 26 of 29 (89.7%), 27 of 30 (90.0%), and 25 of 28 (89.3%) nodules, which were missed by computer-aided detection, were judged as visible in 3-, 2-, and 1-mm slices, respectively. CONCLUSIONS: The automatic detection rate of subsolid nodules by ClearRead CT was approximately 70% at all slice thicknesses. More than 95% of subsolid nodules were visualized on VS-CT, including nodules undetected by the automated software. Computed tomography acquisition at slices thinner than 3 mm did not confer any benefits.

Desmoid-type fibromatosis: imaging features and course.

Desmoid-type fibromatosis (DF) is a soft tissue tumor characterized by infiltrative growth and a tendency toward local recurrence, while it exhibits self-limiting behavior and shows spontaneous regression. With its unpredictable behavior, a change in treatment strategies from initial surgery to nonsurgical management has been proposed, and active surveillance is currently widely chosen as the initial treatment strategy for DF. We reviewed the imaging features of DF regarding its clinical course, focusing on regression cases, postoperative cases, and imaging changes after systemic treatment.

Renal artery-based kidney segmentation on CT for patients with renal cell carcinoma: Feasibility of segmental artery clamping simulation.

Purpose: To evaluate the feasibility of renal artery-based segmentation of kidneys with renal cell carcinoma (RCC), based on three-dimensional (3D) software for the simulation of segmental artery clamping (SAC), and to correlate it with RENAL nephrometry score. Methods: Fifty RCCs (< 4 cm) identified from a pathological database search between January 2015 and January 2018 were included retrospectively. On computed tomography (CT) images, the relevant kidney, tumor, and renal artery were annotated semi-automatically on the commercial workstation, and renal artery-based segmentation was performed using 3D Voronoi diagrams. Simulation of SAC was performed by a radiologist and urologist in consensus. The volume of the whole kidney and tumor and estimated rescued volume for possible SAC cases were calculated. The correlation between possible SAC and RENAL nephrometry score was investigated. The reproducibility of the calculation of each volume and the interrater reliability of SAC simulation were assessed. Results: In the anatomical analysis, 44 patients had a single main renal artery and six had two main renal arteries, and of these, an early division pattern was observed in 11 cases. In the 3D simulation software, 22 out of 50 cases (44 %) were determined as possible SAC. The agreement of the SAC simulation was excellent (kappa = 0.96). RENAL nephrometry score was significantly different in the anterior/posterior and exophytic/endophytic components between possible and impossible SAC groups. Conclusions: Renal artery-based segmentation of kidneys with RCC on CT images using 3D simulation software is feasible for effectively estimating the possibility of SAC with high reproducibility.

Dynamic CT and Gadoxetic Acid-enhanced MRI Characteristics of P53-mutated Hepatocellular Carcinoma.

Background Imaging markers of hepatocellular carcinoma (HCC) on the basis of molecular classification are important for predicting malignancy grade and prognosis. P53-mutated HCC is a major aggressive subtype; however, its imaging characteristics have not been clarified. Purpose To clarify the imaging characteristics of P53-mutated HCC at dynamic CT and gadoxetic acid-enhanced MRI that are correlated with its clinical features, pathologic findings, and prognosis. Materials and Methods In this retrospective single-center study, patients with surgically resected HCC between January 2015 and May 2018 in a university hospital were evaluated. HCC was classified into P53-mutated HCC and non-P53-mutated HCC using immunostaining. Dynamic CT and gadoxetic acid-enhanced MRI findings, clinical features, pathologic findings, and prognosis were compared using Mann-Whitney test, χ2 test, multivariable regression analysis, receiver operating characteristic analysis, Kaplan-Meier method, and log-rank test. Immunohistochemical expression of P53, organic anion transporting polypeptide 1B3 (OATP1B3), and CD34 were evaluated, and the correlations were analyzed using the Pearson correlation test. Results In total, 149 patients (mean age, 67 years ± 9 [SD]; 103 men) with 173 HCCs were evaluated. P53-mutated HCC (n = 28) demonstrated higher serum α-fetoprotein (median, 127.5 ng/mL vs 5.5 ng/mL; P < .001), larger size (40.4 mm ± 29.7 vs 26.4 mm ± 20.5; P = .001), and higher rates of poorly differentiated HCC (22 of 28 [79%] vs 24 of 145 [17%]; P < .001). Dilated vasculature in the arterial phase of dynamic CT (odds ratio, 14; 95% CI: 3, 80; P = .002) and a lower relative enhancement ratio in the hepatobiliary phase (odds ratio, 0.05; 95% CI: 0.01, 0.34; cutoff value, 0.69; P = .002) independently predicted P53-mutated HCC. OATP1B3 expression and P53 expression were inversely correlated (P = .002; R = -0.24). Five-year overall survival was worse for P53-mutated HCC (50.0% vs 72.6%; P = .02). Conclusion Dilated vasculature at the arterial phase of dynamic CT and a lower relative enhancement ratio at the hepatobiliary phase of gadoxetic acid-enhanced MRI were useful markers for P53-mutated hepatocellular carcinoma with poor prognosis. © RSNA, 2022 Online supplemental material is available for this article.

Does Bladder Cancer with Inchworm Sign Indicate Better Prognosis after TURBT?

Background: Inchworm sign is considered to be a characteristic finding in non-muscle invasive bladder cancer (NMIBC). Nevertheless, pathologically diagnosed muscle invasive bladder cancers (MIBCs) are occasionally diagnosed from tissue obtained by transurethral resection of bladder tumor (TURBT) in patients with inchworm sign. Methods: We retrospectively investigated the factors related to muscle invasive status in bladder cancer associated with inchworm sign and the role of inchworm sign in tumor outcomes following TURBT. Results: Of the 109 patients with inchworm sign, 94 (86.2%) and 15 (13.8%) were NMIBC and MIBC, respectively. Non-papillary tumors (hazard ratio (HR): 9.55, 95% confidence interval (CI): 2.07−44.10; p < 0.01) and tumors located in the bladder neck (HR: 7.73, 95% CI: 1.83−32.76; p < 0.01) were significant predictors of MIBC in bladder cancer with inchworm sign. Furthermore, recurrence-free survival (RFS) and progression-free survival were compared between patients with NMIBC with and without inchworm sign; however, no significant differences were found. In patients with NMIBC with inchworm sign, positive urine cytology was a prognostic factor for RFS (HR: 1.90, 95% CI: 1.04−3.48; p = 0.04). Conclusions: In bladder cancer with inchworm sign, 86.2% were NMIBC. Even in the case of inchworm sign, the presence of a non-papillary tumor or a bladder neck tumor before TURBT should be noted because of the possibility of MIBC. In this study, the inchworm sign was not a prognostic factor in patients with NMIBC.

Comparison of Clinical Outcomes between Robot-Assisted Partial Nephrectomy and Cryoablation in Elderly Patients with Renal Cancer.

Advances in imaging technologies have increased the opportunities for treating small-diameter renal cell carcinomas (RCCs) in the elderly. This retrospective study based on real-world clinical practice compared perioperative complications, preoperative and postoperative renal function, recurrence-free survival, and overall survival in elderly patients with RCC who had undergone robot-assisted partial nephrectomy (RAPN) or percutaneous cryoablation (PCA). A total of 99 patients (aged ≥70 years), including 50 and 49 patients in the RAPN and PCA groups, respectively, were analyzed. In the entire cohort, Clavien-Dindo grade ≥3 complications occurred in only one patient who had undergone RAPN. Renal function was significantly lower in the postoperative period than in the preoperative period in both the RAPN and PCA groups. The recurrence-free survival and overall survival rates were worse in the PCA group than in the RAPN group, albeit not significantly. RAPN was considered a safe and effective method for treating RCCs in elderly patients. Moreover, although the recurrence rate was slightly higher in the PCA group than in the RAPN group, PCA was deemed to be a safe alternative, especially for treating patients in whom general anesthesia poses a high risk.

Development of a Prognostic Model of Overall Survival for Metastatic Hormone-Naïve Prostate Cancer in Japanese Men.

BACKGROUND: Treatment strategies have changed dramatically in recent years with the development of a variety of agents for metastatic hormone-naïve prostate cancer (mHNPC). There is a need to identify prognostic factors for the appropriate choice of treatment for patients with mHNPC, and we retrospectively examined these factors. METHODS: Patients with mHNPC treated at our institution from 2000 to 2019 were included in this study. Overall survival (OS) was estimated retrospectively using the Kaplan-Meier method, and factors associated with OS were identified using univariate and multivariate analyses. A prognostic model was then developed based on the factors identified. Follow-up was terminated on 24 October 2021. RESULTS: The median follow-up duration was 44.2 months, whereas the median OS was 85.2 months, with 88 patients succumbing to their disease. Multivariate analysis identified Gleason pattern (GP) 5 content, bone scan index (BSI) ≥ 1.5, and lactate dehydrogenase (LDH) levels ≥ 300 IU/L as prognostic factors associated with OS. We also developed a prognostic model that classified patients with mHNPC as low risk with no factor, intermediate risk with one factor, and high risk with two or three factors. CONCLUSIONS: Three prognostic factors for OS were identified in patients with mHNPC, namely GP5 inclusion, BSI ≥ 1.5, and LDH ≥ 300. Using these three factors, we developed a new prognostic model for OS that can more objectively predict patient prognosis.

Single-Center Analysis of Pegfilgrastim-induced Aortitis Using a Drug Prescription Database and CT Findings.

Background Pegfilgrastim-induced aortitis is a rare but serious adverse event in patients undergoing anticancer therapy with granulocyte colony-stimulating factor analogs. Despite previous case series and systemic reviews, the exact incidence, clinical presentation, and CT manifestations of pegfilgrastim-induced aortitis remain unclear. Purpose To clarify the incidence and clinicoradiologic characteristics of pegfilgrastim-induced aortitis. Materials and Methods Pegfilgrastim administration records from January 2015 to March 2021 were retrospectively collected from the drug prescription database of a single center and were matched with the relevant findings in the CT database. Corresponding CT images within 6 months were available for a total of 1462 doses of pegfilgrastim in 674 patients. Four radiologists reviewed the CT images for the presence of aortitis in two steps. Clinical information and the distribution of aortitis on CT images were examined for patients with a diagnosis of pegfilgrastim-induced aortitis. Results Pegfilgrastim-induced aortitis was observed in 18 of 674 patients (mean age, 62 years ± 13 [SD]; 424 men), resulting in incidence rates of 2.7% per patient (95% CI: 1.6, 4.2) and 1.2% per dose (95% CI: 0.7, 1.9). The most common original primary malignancies were esophageal cancer (n = 10, 9%), breast cancer (n = 3, 4%), and pancreatic cancer (n = 2, 2%). The most common anticancer drugs used at onset were 5-fluorouracil, cisplatin, and docetaxel. Seven cases were symptomatic, while the remaining 11 (61%) were asymptomatic. CT findings indicated that aortitis involved branches of the aortic arch in 13 cases (72%), aortic arch in 10 cases (56%), and abdominal aorta in two cases (11%). Conclusion Pegfilgrastim-induced aortitis may be more prevalent than previously reported and may be more common in patients with esophageal cancer and those who received 5-fluorouracil, cisplatin, and docetaxel as anticancer drugs. The findings also suggest that pegfilgrastim-induced aortitis is often characterized by aortic arch and proximal branch involvement at CT. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Krinsky in this issue.

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI.

PURPOSE: To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction. METHODS: Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values. RESULTS: Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10-6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736-0.949 with specific instruction and 0.349-0.919 without specific instruction. CONCLUSION: Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI.

PURPOSE: To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS: Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal 1 min - Signal pre)/Signal pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. RESULTS: The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. CONCLUSIONS: Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

Investigating the mechanism underlying urinary continence using dynamic MRI after Retzius-sparing robot-assisted radical prostatectomy.

Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) exhibits better postoperative urinary continence than conventional RARP (C-RARP) via the anterior approach. However, the reasons behind this are unknown. Herein, early postoperative urinary incontinence and anatomical differences of 51 propensity score-matched C-RARP and RS-RARP cases were compared. Dynamic magnetic resonance imaging (MRI) was performed before and after surgery to examine the pelvic anatomical changes under abdominal pressure. The median urine loss ratios in the early postoperative period after C-RARP and RS-RARP were 11.0% and 1.0%, respectively. Postoperative MRI revealed that the anterior bladder wall was fixed in a higher position after RS-RARP compared with its position after C-RARP. Dynamic MRI after C-RARP showed that cephalocaudal compression of the bladder while applying abdominal pressure caused the membranous urethra to expand and the urine to flow out. After RS-RARP, the rectum moved forward during abdominal pressure, and the membranous urethra was compressed by closure from behind. This is the first study using dynamic MRI to reveal the importance of high attachment of the anterior bladder wall for the urethral closure mechanism during abdominal pressure. RS-RARP, which can completely preserve this mechanism, is less likely to cause stress urinary incontinence compared with C-RARP.