Tailgut cyst is a rare congenital cystic lesion arising from the remnants of the embryonic postanal gut. It occurs exclusively within the retrorectal space and rarely in the perirenal area or in the subcutaneous tissue. A prerectal and retrovesical location of tailgut cyst is extremely rare. To the best of our knowledge, only three cases have been reported in the English literature. We experienced an unusual case of tailgut cyst developed in the prerectal and retrovesical space in a 14-year-old boy. Abdominal computed tomography demonstrated a prerectal cyst which was located at the anterolateral portion to the rectum. The cyst contained yellowish inspissated mucoid material. Microscopically, the cyst was lined by squamous, columnar, cuboidal and transitional epithelia and the wall was fibrotic with dispersed smooth muscle cells. Although tailgut cyst arising in prerectal area is extremely rare, its possibility should be considered in differential diagnosis of a prerectal and retrovesical cystic mass.
Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the four-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, 0.971, and 0.956 in the non-neoplasm, CIN3, CIN1, and CIN2 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the three-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%), and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.
Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1,106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the 4-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, and 0.971 in the non-neoplasm, CIN3, CIN1 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the 3-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%) and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.
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