MRI-detectability and histological factors of prostate cancer including intraductal carcinoma and cribriform pattern.

BACKGROUND: Histopathological characteristics affecting the detectability of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) remain unclear. This study aimed to compare the histopathology between MRI-detectable and MRI-undetectable cancers, emphasizing intraductal carcinoma of the prostate (IDC-P) and predominant Gleason pattern 4 subtype. METHODS: This single-center retrospective study enrolled 153 consecutive patients with 191 lesions who underwent preoperative multiparametric MRI and subsequent radical prostatectomy. MRI/histopathological findings and area fractions of histological components (cancer cells, stroma, and luminal spaces) of MRI-detectable and MRI-undetectable cancers were compared. Data were analyzed using Fisher's exact, independent t, or Mann-Whitney U tests. RESULTS: Overall, 148 (77%) and 43 (23%) cancers were MRI-detectable and MRI-undetectable, respectively. MRI-detectable cancers were significantly larger than MRI-undetectable cancers (p = 0.03). The percentage of lesions in Grade Group 3 or higher was significantly higher among MRI-detectable cancers than among MRI-undetectable cancers (p = 0.02). MRI detectability of csPCa was associated with increases in relative area fractions of cancer cells (p < 0.001) and decreases in those of stroma (p < 0.001) and luminal spaces (p < 0.001) in prostate cancer (PCa) than the percentage of Gleason pattern 4 (p = 0.09). The percentage of lesions containing IDC-P was similar for MRI-detectable and MRI-undetectable cancers (40% vs. 33%; p = 0.48). The distribution of cribriform gland subtypes was not significantly different between MRI-detectable and MRI-undetectable Gleason pattern 4 subtype cancers (p > 0.99). Contrarily, the ratio of fused gland subtype was significantly higher in MRI-detectable than in MRI-undetectable cancers (p = 0.03). Furthermore, the ratio of poorly-formed gland subtype was significantly higher in MRI-undetectable than in MRI-detectable cancers (p = 0.01). CONCLUSIONS: MRI detectability of csPCa is strongly associated with the relative area fractions of cancer cells, stroma, and luminal spaces in PCa rather than conventional histopathological parameters. Neither the presence nor the percentage of IDC-P affected MRI detectability.

Effect of Ultra-High-Resolution CT on Pseudoenhancement in Renal Cysts: A Phantom Experiment and Clinical Study.

BACKGROUND. Ultra-high-resolution CT (UHRCT) allows acquisition using a small detector element size, in turn allowing very high spatial resolutions. The high resolution may reduce partial-volume averaging and thereby renal cyst pseudoenhancement. OBJECTIVE. The purpose of this article was to assess the impact of UHRCT on renal cyst pseudoenhancement. METHODS. A phantom was constructed that contained 7-, 15-, and 25-mm simulated cysts within compartments simulating unenhanced and nephrographic phase renal parenchyma. The phantom underwent two UHRCT acquisitions using 0.25- and 0.5-mm detector elements, with reconstruction at varying matrices and slice thicknesses. A retrospective study was performed of 36 patients (24 men, 12 women; mean age, 75.7 ± 9.4 [SD] years) with 118 renal cysts who underwent renal-mass protocol CT using UHRCT and the 0.25-mm detector element, with reconstruction at varying matrices and slice thicknesses; detector element size could not be retrospectively adjusted. ROIs were placed to measure cysts' attenuation increase from unenhanced to nephrographic phases (to reflect pseudoenhancement) and SD of unenhanced phase attenuation (to reflect image noise). RESULTS. In the phantom, attenuation increase was lower for the 0.25- than 0.5-mm detector element for the 15-mm cyst (4.6 ± 2.7 HU vs 6.8 ± 2.9 HU, p = .03) and 25-mm cyst (2.3 ± 1.4 HU vs 3.8 ± 1.2 HU, p = .02), but not the 7-mm cyst (p = .72). Attenuation increase was not different between 512 × 512 and 1024 × 1024 matrices for any cyst size in the phantom or patients (p > .05). Attenuation increase was not associated with slice thickness for any cyst size in the phantom or in patients for cysts that were between 5 mm and less than 10 mm and those that were 10 mm and larger (p > .05). For cysts smaller than 5 mm in patients, attenuation increase showed decreases with thinner slices, though there was no significant difference between 0.5-mm and 0.25-mm (3-mm slice: 23.7 ± 22.5 HU; 2-mm slice: 20.2 ± 22.7 HU; 0.5-mm slice: 11.6 ± 17.5 HU; 0.25-mm slice: 12.6 ± 19.7 HU; p < .001). Smaller detector element size, increased matrix size, and thinner slices all increased image noise for cysts of all sizes in the phantom and patients (p < .05). CONCLUSION. UHRCT may reduce renal cyst pseudoenhancement through a smaller detector element size and, for cysts smaller than 5 mm, very thin slices; however, these adjustments result in increased noise. CLINICAL IMPACT. Although requiring further clinical evaluation, UHRCT may facilitate characterization of small cystic renal lesions, thereby reducing equivocal interpretations and follow-up recommendations.

Histological differences in cancer cells, stroma, and luminal spaces strongly correlate with in vivo MRI-detectability of prostate cancer.

The current study aimed to investigate the plausible histopathological factors that affect the detectability of prostate cancers on multiparametric magnetic resonance imaging (MP-MRI). This retrospective study included 59 consecutive patients who had undergone MP-MRI and subsequent radical prostatectomy. The cases were standardized according to the tumor size ranging from 10 to 20 mm on the final pathological diagnosis. Histopathological review and semi-automated imaging analysis were performed to evaluate the relative area fractions of the histological components, including cancer cells, stroma, and luminal spaces. Among the 59 prostatectomy specimens, no case showed two or more foci of cancer that matched the size criteria. Of the 59 lesions, 35 were MRI-detectable [Prostate Imaging Reporting and Data System (PIRADS) score of 3 or greater] and 24 were MRI-undetectable (PIRADS score of 2 or less). No significant differences were observed in Gleason Grade Group, percentage of Gleason pattern 4, and predominant subtype of Gleason pattern 4 between MRI-detectable and MRI-undetectable cancers. On the other hand, significantly higher mean area fraction of cancer cells (60.9% vs. 42.7%, P < 0.0001) and lower mean area fractions of stroma (33.8% vs. 45.1%, P = 0.00089) and luminal spaces (5.2% vs. 12.2%, P < 0.0001) were observed in MRI-detectable cancers than in MRI-undetectable cancers. In a multivariable analysis performed upon exclusion of area fraction of stroma due to its multicollinearity with that of cancer cells, area fractions of cancer cells (P = 0.0031) and luminal space (P = 0.0035) demonstrated strong positive and negative correlation with MRI-detectability, respectively. Changes in cancer cells, stroma, and luminal spaces, rather than conventional histological parameters, could be considered one of the best predictors to clinical, in vivo MRI-detectability of prostate cancer.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

BACKGROUND: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). METHODS: We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. RESULTS: In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. CONCLUSIONS: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.

Mild metatropic dysplasia: emphasis on the magnetic resonance imaging of articular cartilage thickening.

Metatropic dysplasia (MD) is a rare skeletal disorder characterized by short stature due to epiphyseal cartilage and growth plate abnormalities. The severity of MD varies from mild to lethal. This disorder is caused by mutations in the transient receptor potential vanilloid 4 (TRPV4) that encodes calcium-permeable, nonselective cation channels. A 33-year-old female presented at our hospital with a history of worsening knee pain diagnosed at the previous institution as a case of osteoarthritis. Radiographs of the knee showed epiphyseal irregularity without joint space narrowing. On MRI, fat-suppressed proton density-weighted imaging revealed thickened articular cartilage with a smooth surface and an abnormal signal intensity of the subchondral bone; T 1 weighted imaging demonstrated irregularity of the epiphysis. These findings and the familial history (both her children had TRPV4 mutations) led to the suspicion that her condition could be due to mosaicism for TRPV4 mutation. To the best of our knowledge, this is the first report of MRI findings focusing on articular cartilage thickening in a patient with mild MD. Bone dysplasia including MD should be considered in young patients with articular cartilage thickening and subchondral bone irregularities on MRI.

Usefulness of computed tomography for hospitalized adult patients with fever to investigate cause of fever: single-center, retrospective cohort study.

PURPOSE: To verify that physicians' presumptive diagnosis is the most significant factor for finding any signs related to the cause of fever on computed tomography (CT) images. MATERIALS AND METHODS: This single-center retrospective cohort study included patients (age ≥ 16 years) who underwent CT to investigate the cause of fever between January 1, 2014, and August 31, 2016. Patients who underwent surgical procedures were excluded. The primary outcome was the presence of suspicious CT findings related to the cause of fever. We performed univariate and multivariate logistic regression analyses, adjusted for CT contrast agent use, quick sequential organ failure assessment score > 1, and C-reactive protein level. RESULTS: We enrolled 171 patients, of which 57 had CT findings, and 114 did not. Multivariate logistic regression analyses demonstrated a significant difference for the presence of a presumptive diagnosis by the attending physician (odds ratio, 4.99; 95% confidence interval 2.31-10.76; p < 0.01), but not for other covariates, including C-reactive protein. CONCLUSIONS: In hospitalized patients with fever, an attending physicians' presumptive diagnosis is associated with the presence of fever-related CT findings. Improving the quality of the diagnostic assessment before the CT scan may lead to more appropriate CT imaging use.

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.

Prostate cancer recurrence mimicking invasive urothelial cancer of the bladder.

A 74-year-old male patient with stage D1 prostate cancer with the initial prostate-specific antigen (PSA) level of 5570 ng/mL had received androgen deprivation therapy and the serum PSA level had decreased to 0.23 ng/mL when he developed macroscopic hematuria. MRI and cystoscopy suggested invasive urothelial cancer of the bladder, and transurethral resection was performed. The tumors were pathologically diagnosed as a Gleason score 9 prostate cancer with no PSA expression. Prostate cancer patients who develop novel symptoms should be screened for prostate cancer recurrence even if they have very low PSA levels.

Mobile Spinal Schwannoma with a Completely Cystic Appearance.

BACKGROUND Spinal schwannomas are benign tumors arising from Schwann cells. Although they have been well described, tumor movement in the spinal canal is an extremely rare finding, and entirely cystic spinal schwannomas have rarely been reported. This is the first report of a spinal schwannoma that simultaneously exhibited both these unusual features. CASE REPORT A 48-year-old female presented with dysuria and right leg pain. Initial magnetic resonance imaging (MRI) revealed a well-delineated intradural cystic lesion at the level of L4-S1 vertebrae that was isointense with cerebrospinal fluid on both T1- and T2-weighted images. A follow-up MRI 6 months later showed that the tumor had moved to the level of L2-L4; it also revealed tortuous configuration of nerve roots of the cauda equina. The tumor was resected, and a diagnosis of schwannoma with extensive cystic degeneration was pathologically confirmed. CONCLUSIONS Various possible mechanisms have been suggested for the mobility of extramedullary tumors. In the present case, MRI findings indicated the cause of the tumor movement might be attributed to the laxity of nerve roots. Besides, it is highly atypical for a schwannoma to present an entirely cystic appearance, and the combination of the 2 extraordinary features made preoperative diagnosis difficult. However, 16 out of 22 (73%) of previously reported mobile spinal tumors were schwannomas, so the differential diagnosis for a mobile spinal tumor should include schwannoma, even when the lesion seems entirely cystic on MRI. To minimize the risk of complications and additional surgical dissection, physicians should acknowledge that spinal tumors can migrate.

Synchronous neuroendocrine tumors in both the pancreas and ileum: A case report.

INTRODUCTION: Although it is well-known that in multiple endocrine neoplasia type 1 (MEN 1) disease, multiple endocrine lesions frequently occur, synchronous or metachronous neuroendocrine tumors (NETs) in non-MEN 1 patients are extremely rare. PRESENTATION OF CASE: An asymptomatic 72-year-old woman with an ileal NET was referred to our hospital. Abdominal computed tomography revealed another circular tumor within the pancreatic head. She was classified as a non-MEN 1 patient. An operative procedure was performed with a preoperative diagnosis of synchronous NET, which was confirmed by pathological examination. DISCUSSION: Both morphologic and immunophenotypic findings were different between in the ileum and pancreas. Therefore, it was reasonable to consider that both tumors were primary tumors. The synchronous occurrence of these tumors is unusual, and it may be considered as a chance occurrence. CONCLUSION: We here report the first case of synchronous pancreatic NET and ileal NET in a non-MEN 1 patient.