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. 2022 Jun 3:9:838182.
doi: 10.3389/fmed.2022.838182. eCollection 2022.

Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image

Yanfeng Bai  1 , Huogen Wang  2   3 , Xuesong Wu  1 , Menghan Weng  1 , Qingmei Han  1 , Liming Xu  1 , Han Zhang  1 , Chengdong Chang  1 , Chaohui Jin  2 , Ming Chen  2 , Kunfeng Luo  2 , Xiaodong Teng  1
Affiliations

Study on Molecular Information Intelligent Diagnosis and Treatment of Bladder Cancer on Pathological Tissue Image

Yanfeng Bai et al. Front Med (Lausanne). .

Abstract

Background: Molecular information about bladder cancer is significant for treatment and prognosis. The immunohistochemistry (IHC) method is widely used to analyze the specific biomarkers to determine molecular subtypes. However, procedures in IHC and plenty of reagents are time and labor-consuming and expensive. This study established a computer-aid diagnosis system for predicting molecular subtypes, p53 status, and programmed death-ligand 1 (PD-L1) status of bladder cancer with pathological images.

Materials and methods: We collected 119 muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from January 2016 to September 2018. All the pathological sections are scanned into digital whole slide images (WSIs), and the IHC results of adjacent sections were recorded as the label of the corresponding slide. The tumor areas are first segmented, then molecular subtypes, p53 status, and PD-L1 status of those tumor-positive areas would be identified by three independent convolutional neural networks (CNNs). We measured the performance of this system for predicting molecular subtypes, p53 status, and PD-L1 status of bladder cancer with accuracy, sensitivity, and specificity.

Results: For the recognition of molecular subtypes, the accuracy is 0.94, the sensitivity is 1.00, and the specificity is 0.909. For PD-L1 status recognition, the accuracy is 0.897, the sensitivity is 0.875, and the specificity is 0.913. For p53 status recognition, the accuracy is 0.846, the sensitivity is 0.857, and the specificity is 0.750.

Conclusion: Our computer-aided diagnosis system can provide a novel and simple assistant tool to obtain the molecular subtype, PD-L1 status, and p53 status. It can reduce the workload of pathologists and the medical cost.

Keywords: PD-L1; bladder cancer; deep learning; molecular information; molecular subtypes; p53; pathology.

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Conflict of interest statement

HW, CJ, MC, and KL were employed by Hithink RoyalFlush Information Network Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow of muscle-invasive bladder cancer (MIBC) histopathological molecular information recognition with convolutional neural networks (CNNs).
FIGURE 2
FIGURE 2
Receiver operator characteristic (ROC) curve of tumor classification pre-trained model.
FIGURE 3
FIGURE 3
Tumor segmentation results with the instance classifier and U-SE-Net. (A) pathological images; (B) segmentation results with the instance classifier (red masks); (C) segmentation results with U-SE-Net (red masks).
FIGURE 4
FIGURE 4
ROC curves of molecular subtype, PD-L1 status, and p53 status with CNN recognition. Left: molecular subtype; Middle: PD-L1 status; Right: p53 status.
FIGURE 5
FIGURE 5
Heatmaps of the same pathological image predicted by our proposed method for molecular subtype, PD-L1 status, and p53 status.
See this image and copyright information in PMC

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