Abstract
Cervical cancer is the most common cancer among women worldwide. The diagnosis and classification of cancer are extremely important, as it influences the optimal treatment and length of survival. The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN) that identifies cervical malignancies and provides diagnostic interpretability. A total of 8496 labeled histology images were extracted from 229 cervical specimens (cervical squamous cell carcinoma, SCC, n = 37; cervical adenocarcinoma, AC, n = 8; nonmalignant cervical tissues, n = 184). AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. The performance of CNNs was quantified in terms of accuracy, precision, recall, and the area under the receiver operating curve (AUC). Six pathologists were recruited to make a comparison with the performance of CNNs. Guided Backpropagation and Gradient-weighted Class Activation Mapping (Grad-CAM) were deployed to highlight the area of high malignant probability. The Xception model had excellent performance in identifying cervical SCC and AC in test sets. For cervical SCC, AUC was 0.98 (internal validation) and 0.974 (external validation). For cervical AC, AUC was 0.966 (internal validation) and 0.958 (external validation). The performance of CNNs falls between experienced and inexperienced pathologists. Grad-CAM and Guided Gard-CAM ensured diagnoses interpretability by highlighting morphological features of malignant changes. CNN is efficient for histological image classification tasks of distinguishing cervical malignancies from benign tissues and could highlight the specific areas of concern. All these findings suggest that CNNs could serve as a diagnostic tool to aid pathologic diagnosis.
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This research was financially supported by the Chongming district Innovation and Entrepreneurship Project (granted by Mei Wang).
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LYX and CF contributed equally to this research. LYX, CF, and WM devised the study plan. LYX, CF, SJJ, and HYL helped with data acquisition. LYX, CF, and SJJ wrote the draft manuscript. WM and CF supervised the research.
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The study protocol was approved by the institutional review board of Xinhua hospital (CMEC-2022-KT-15). Written informed consent was waived by the institutional review boards owing to the retrospective study design. However, verbal informed consent was obtained from all patients.
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Li, Yx., Chen, F., Shi, Jj. et al. Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images. J Digit Imaging 36, 441–449 (2023). https://doi.org/10.1007/s10278-022-00722-8
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DOI: https://doi.org/10.1007/s10278-022-00722-8