Background Anorectal malignant melanoma (ARMM) and low rectal adenocarcinoma (LRAC) have some similarities in clinical behaviors, histopathological characteristics and ultrasonographic findings, diagnostic errors are common. By comparing the transrectally ultrasonographic features between the two tumors, we propose to provide more possibilities in differentiating them. Methods The data of 9 ARMMs and 27 age- and gender-matched LRACs (the lower margin below the peritoneal reflection) in West China Hospital Sichuan University between April 2008 and July 2019 were retrospectively reviewed. The ultrasonic features between the two groups were compared. Results Transrectal ultrasonography (TRUS) showed that the length of ARMM was shorter than that of LRAC (28.22 ± 12.29 mm vs. 40.22 ± 15.16 mm), and ARMM had a lower position than that of LRAC (the distance to anal verge was 50.78 ± 11.70 vs. 63.81 ± 18.73 mm). Unlike LRAC, the majority of ARMM in our study was confined to the intestinal mucosa/submucosa (66.67/25.93%) (P < 0.05). Conclusions Based on the data of our study, several ultrasonographic findings (length, invasion depth, and position) of ARMM were significantly different from LRAC. Accordingly, more attention should be paid to masses at anorectal junction with lower position, shorter length, and shallower infiltration depth. Instead of the most common tumor, LRAC, ARMM should be taken into account to avoid a misdiagnosis, which will result in a poorer prognosis.
Background Inhomogeneity within tumors can reflect tumor angiogenesis. Existing research into the quantization of angiogenesis mainly focuses on time-intensity curve parameters but has produced inconsistent results. In clinical work, it is difficult to achieve standardization and consistency for manual judgement of the inhomogeneity of contrast-enhanced images, while the artificial intelligence technology may be helpful. The aim of this study was to assess whether computers can assist in the artificial classification of tumor inhomogeneity in contrast-enhanced ultrasound (CEUS) images of rectal cancer. Methods A total of 500 contrast-enhanced ultrasonograms were retrospectively collected, which was verified of rectal cancer pathologically from 2016 to 2018 as training set. All images are from 18–80 years old patients with rectal cancer in our hospital. These tumors are usually located in the middle and lower segment of the rectum, which can be completely observed on ultrasound. The images were divided into 3 categories according to the inhomogeneous distribution of contrast agents inside the tumors. Computing methods were used to simulate manual classification. Computer processing steps included segmentation, gray level quantization, dimension reduction, and classification. The results of 6 different gray level quantization, 2 dimensionality reduction methods, and 3 classifiers were compared, from which the optimal parameters were selected in each step. The performance of computer classification was evaluated using manual classification results as the reference. Ninety-seven ultrasonograms of contrast-enhanced rectal tumors were collected as validation set from 2018.1 to 2018.6. Results The optimal gray level was set at 32. Principal component analysis (PCA) was the first choice for dimensionality reduction. The best classifier was support vector machines (SVM). The accuracy of computer classification was 87.80% (439/500). The accuracy of computer classification in the validation cohort was 60.82%. The area under the curve (AUC) of class 1, 2, and 3 were 0.76, 0.41, and 0.48, respectively. Conclusions Results showed that the computer methods are competent for classifying inhomogeneity of contrast-enhanced rectal cancers inside ultrasonograms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.