Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Intelligent medicine Pub Date : 2022-05-01 DOI:10.1016/j.imed.2021.08.003
Jiaqi Lu , Ruiqing Liu , Yuejuan Zhang , Xianxiang Zhang , Longbo Zheng , Chao Zhang , Kaiming Zhang , Shuai Li , Yun Lu
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引用次数: 5

Abstract

Objective

Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical prognosis. However, there are significant differences and difficulties associated with manually identifying tumor sprouting. This study used the Faster region convolutional neural network (RCNN) model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer.

Methods

We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019. The pathologists used LabelImg software to identify tumor buds and to count their numbers. Finally, 1,000 images were screened, and the total number of tumor buds was approximately 3,226; the images were randomly divided into a training set and a test set at a ratio of 6:4. After the images in the training set were manually identified, the identified buds in the 600 images were used to train the Faster RCNN identification model. After the establishment of the artificial intelligence identification detection platform, 400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor buds. Finally, by comparing the results of the Faster RCNN system and the identification information of pathologists, the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment. The selected performance indicators include accuracy, precision, specificity, etc. ROC (receiver operator characteristic) and AUC (area under the curve) were used to quantify the performance of the system to automatically identify tumor budding areas and numbers.

Results

The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identification system was 0.96, the image diagnosis accuracy rate was 0.89, the precision was 0.855, the sensitivity was 0.94, the specificity was 0.83, and the negative predictive value was 0.933. After 400 test sets, pathological image verification showed that there were 356 images with the same positive budding area count, and the difference between the positive area count and the manual detection count in the remaining images was less than 3. The detection system based on tumor budding recognition in pathological sections is comparable to that of pathologists’ accuracy; however, it took significantly less time (0.03±0.01)s for the pathologist (13±5)s to diagnose the sections with the assistance of the AI model.

Conclusion

This system can accurately and quickly identify the tumor sprouting area in the pathological sections of colorectal cancer and count their numbers, which greatly improves the diagnostic efficacy, and effectively avoids the need for confirmation by different pathologists. The use of the AI reduces the burden of pathologists in reading sections and it has a certain clinical diagnosis and treatment value.

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基于人工智能的结直肠癌肿瘤萌芽病理特征检测平台的开发与应用
目的肿瘤萌芽是肿瘤恶性的独立危险因素,临床预后较差。然而,人工识别肿瘤发芽存在显著差异和困难。本研究采用Faster区域卷积神经网络(RCNN)模型构建基于病理切片的结直肠癌肿瘤萌芽人工智能识别框架,自动识别出萌芽区域,辅助结直肠癌的临床诊断和治疗。方法回顾性收集2019年1月~ 2019年10月收治的100例结直肠癌手术病理切片。病理学家使用LabelImg软件来识别肿瘤芽并计算它们的数量。最后,筛选1000张图像,肿瘤芽总数约为3226个;将图像按6:4的比例随机分为训练集和测试集。在对训练集中的图像进行人工识别后,使用600张图像中识别出的芽来训练Faster RCNN识别模型。人工智能识别检测平台建立后,利用测试集中的400张图像对识别检测系统进行测试,对肿瘤芽的面积和数量进行识别和预测。最后,通过比较Faster RCNN系统的结果和病理学家的识别信息,评估人工智能自动检测平台的性能,确定结直肠癌病理切片中肿瘤发芽的面积和数量,以实现辅助诊断并建议适当的治疗。所选择的性能指标包括准确性、精密度、特异性等。使用ROC (receiver operator characteristic)和AUC (area under the curve)来量化系统自动识别肿瘤出芽区域和数量的性能。结果人工智能检测识别系统的受试者工作特征曲线AUC为0.96,图像诊断正确率为0.89,精密度为0.855,灵敏度为0.94,特异度为0.83,阴性预测值为0.933。经过400个测试集的病理图像验证,有356张图像具有相同的阳性出芽面积计数,其余图像中阳性出芽面积计数与人工检测计数的差值小于3。基于病理切片肿瘤萌芽识别的检测系统的准确性可与病理学家的检测系统相媲美;在人工智能模型的辅助下,病理医师对切片的诊断时间(13±5)s明显缩短(0.03±0.01)s。结论该系统能够准确、快速地识别结直肠癌病理切片上的肿瘤萌芽区域,并对其数量进行计数,大大提高了诊断效率,有效地避免了需要不同病理医师的确认。人工智能的使用减轻了病理医师阅读切片的负担,具有一定的临床诊疗价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligent medicine
Intelligent medicine Surgery, Radiology and Imaging, Artificial Intelligence, Biomedical Engineering
CiteScore
5.20
自引率
0.00%
发文量
19
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