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Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

We present a case of a previously healthy 50-year-old gentleman who had recurrent vomiting and abdominal pain of two-month duration. The patient was subsequently diagnosed with abdominal cocoon on computed tomography. Idiopathic sclerosing encapsulating peritonitis, also known as abdominal cocoon, is a rare cause of small bowel obstruction. Visualization of variable encasement of the small bowel loops by a characteristic membranous sac, either preoperatively with cross-sectional imaging or intraoperatively, is the key to diagnosis. This is a highly treatable condition; surgical excision of the sac with adhesiolysis facilitates a full recovery in affected patients.

Fascioliasis is a reemerging zoonosis endemic in many parts of the world. Reports of imported fascioliasis by migrants into nonendemic countries are common, but tourists and business travelers are rarely afflicted. Here, we report a case of a New Zealander traveler who acquired the infection on a business trip.