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. 2022 Mar 23:2022:6495309.
doi: 10.1155/2022/6495309. eCollection 2022.

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm

Zhenge Zhang  1 , Chongyuan Zhang  1 , Li Xiao  1 , Shuirong Zhang  1
Affiliations

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm

Zhenge Zhang et al. Contrast Media Mol Imaging. .

Abstract

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Comparison of PSNR under different image processing methods.  ∗indicates that compared with the traditional algorithm, the differences were statistically significant as P < 0.05.
Figure 2
Figure 2
Comparison of SSIM under different image processing methods.  ∗indicates that the differences were statistically significant compared with those of the traditional algorithm, P < 0.05.
Figure 3
Figure 3
Imaging comparison of two algorithms. A and C are the images of conventional multimodal MRI; B and C are those of multimodal MRI under the alternating direction algorithm.
Figure 4
Figure 4
Result comparison of conventional MRI and medical examination.
Figure 5
Figure 5
Result comparison of MRI under alternating direction algorithm and medical examination.
Figure 6
Figure 6
Consistency comparison among the two methods and medical examination.  ∗indicates that compared with the Kappa coefficient of A group, the differences were statistically significant (P < 0.05).
Figure 7
Figure 7
Evaluation of the diagnosis results with the two examination methods.  ∗indicates that the differences were statistically significant, as the data were compared with those of A group (P < 0.05).
Figure 8
Figure 8
ROC curves of the two examination methods.
See this image and copyright information in PMC

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