Mature cystic teratomas (MCTs) are common benign tumors occurring in the ovaries. Malignant transformation of teratomas (TMT) occurs in 1–3 % of all MCTs, usually in postmenopausal women. Squamous cell carcinoma is the most common tumor type. Primary carcinoid tumors of the ovary are uncommon, representing only 0.3 % of all carcinoid tumors and less than 0.1 % of all ovarian cancers. A carcinoid tumor of the trabecular type arising from a MCT is presented in a 25-year-old woman. TMT was detected incidentally. Histologically, uniform, polygonal cells were arranged in a cord and trabecular pattern. Immunohistochemical staining showed positivity for neuro-specific enolase, synaptophysin, and CD56. This case represents a very rare example of carcinoid tumor occurring in a woman younger than 30 years of age. Our findings suggest that sufficient tumor sampling is necessary to avoid overlooking small lesions, which, in our case, were not detected on either radiologic or gross examination.

Background: We investigated the feasibility of a deep learning algorithm (DLA) based on apparent diffusion coefficient (ADC) maps for the segmentation and discrimination of clinically significant cancer (CSC, Gleason score ≥ 7) from non-CSC in patients with prostate cancer (PCa). Methods: Data from a total of 149 consecutive patients who had undergone 3T-MRI and been pathologically diagnosed with PCa were initially collected. The labelled data (148 images for GS6, 580 images for GS7) were applied for tumor segmentation using a convolutional neural network (CNN). For classification, 93 images for GS6 and 372 images for GS7 were used. For external validation, 22 consecutive patients from five different institutions (25 images for GS6, 70 images for GS7) representing different MR machines were recruited. Results: Regarding segmentation and classification, U-Net and DenseNet were used, respectively. The tumor Dice scores for internal and external validation were 0.822 and 0.7776, respectively. As for classification, the accuracies of internal and external validation were 73 and 75%, respectively. For external validation, diagnostic predictive values for CSC (sensitivity, specificity, positive predictive value and negative predictive value) were 84, 48, 82 and 52%, respectively. Conclusions: Tumor segmentation and discrimination of CSC from non-CSC is feasible using a DLA developed based on ADC maps (b2000) alone.

Introduction: Primary squamous cell carcinoma (SCC) of the kidney parenchyma is extremely rare, with only seven cases reported to date. Herein, we present a case of primary with colon invasion. Presentation of case: A 61-year-old man presented right flank pain for 5 months. Computed tomography (CT) showed multiple renal stones and a necrotic mass in the lower pole of the right kidney, which was suspicious for ascending colon invasion. There was no history of radiation or occupational exposure to chemicals. Radical nephrectomy, right hemicolectomy, and lymph node dissection were performed. The kidneys showed a solid mass with multiple renal stones. The tumor comprised nests of atypical squamous cells and keratin pearls; however, the renal pelvis was normal. 18-Fluorodeoxyglucose-positron emission tomography/CT failed to demonstrate other primary site. Therefore, the patient was diagnosed as primary SCC of the kidney. The patient did not receive adjuvant therapy and was alive during follow-up for 6 months after surgery. Discussion: SCC of the urinary tract is considered to be the result of squamous metaplasia of the urothelium, which may potentially progress to SCC. Squamous metaplasia may be caused by chronic irritation. However, the mechanism of SCC remains unclear. Although the prognosis of SCC is similar to that of urothelial carcinoma when compared stage for stage, SCC tends to be diagnosed at a more advanced stage. Conclusion: This is a rare case of kidney SCC with adjacent organ invasion. Additional studies are required to further our understanding of this rare tumor and improve diagnosis and treatment. Highlights:

No TH, et al. Infect Chemother. 2020 Dec;52(4):634-640. https://doi.org/10.3947/ic.2020.52.4.634

We investigated the feasibility of magnetic resonance elastography (MRE) using a pelvic acoustic driver for the detection and classification of prostate cancer (PCa). A total of 75 consecutive patients (mean age, 70; range, 56–86) suspected of having PCa and who underwent multi-parametric MRI including MRE and subsequent surgical resection were included. The analyzed regions consisted of cancer (n = 69), benign prostatic hyperplasia (BPH) (n = 70), and normal parenchyma (n = 70). A histopathologic topographic map served as the reference standard for each region. One radiologist and one pathologist performed radiologic–pathologic correlation, and the radiologist measured stiffness values in each region of interest on elastograms automatically generated by dedicated software. Paired t-tests were used to compare stiffness values between two regions. ROC curve analysis was also used to extract a cutoff value between two regions. The stiffness value of PCa (unit, kilopascal (kPa); 4.9 ± 1.1) was significantly different to that of normal parenchyma (3.6 ± 0.3, p < 0.0001) and BPH (4.5 ± 1.4, p = 0.0454). Under a cutoff value of 4.2 kPa, a maximum accuracy of 87% was estimated, with a sensitivity of 73%, a specificity of 99%, and an AUC of 0.839 for discriminating PCa from normal parenchyma. Between PCa and BPH, a maximum accuracy of 62%, a sensitivity of 70%, a specificity of 56%, and an AUC of 0.598 were estimated at a 4.5 kPa cutoff. The stiffness values tended to increase as the ISUP grade increased. In conclusion, it is feasible to detect and classify PCa using pelvic MRE. Our observations suggest that MRE could be a supplement to multi-parametric MRI for PCa detection.

The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WI_conv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WI_HR, 2 mm), with and without deep learning reconstruction (DLR) in patients with prostatic cancer (PCa). A total of 88 consecutive patients (28 EPE-positive and 60 negative) diagnosed with PCa via radical prostatectomy who had undergone 3T-MRI were included. Two independent reviewers performed a crossover review in three sessions, in which each reviewer recorded five-point confidence scores for the presence of EPE and image quality using a five-point Likert scale. Pathologic topographic maps served as the reference standard. For both reviewers, T2WI_conv showed better diagnostic performance than T2WI_HR with and without DLR (AUCs, in order, for reviewer 1, 0.883, 0.806, and 0.772, p = 0.0006; for reviewer 2, 0.803, 0.762, and 0.745, p = 0.022). The image quality was also the best in T2WI_conv, followed by T2WI_{HR with DLR} and T2WI_{HR without DLR} for both reviewers (median, in order, 3, 4, and 5, p < 0.0001). In conclusion, T2WI_conv was optimal in regard to image quality and diagnostic performance for the evaluation of EPE in patients with PCa.