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Osteosarcoma recurrence risk prediction model based on tissue morphological analysis

A technology of risk prediction and tissue morphology, applied in image analysis, biological neural network model, character and pattern recognition, etc., can solve the problem of long and arduous treatment process

Pending Publication Date: 2021-02-23
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

The cure rate of neoadjuvant chemotherapy for osteosarcoma is close to 60-70%, but the course of treatment can be long and arduous, usually lasting a year or more

Method used

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  • Osteosarcoma recurrence risk prediction model based on tissue morphological analysis
  • Osteosarcoma recurrence risk prediction model based on tissue morphological analysis
  • Osteosarcoma recurrence risk prediction model based on tissue morphological analysis

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specific Embodiment

[0036] 1. First, if figure 2 As shown, the trained DeepTisse Net is used to perform multiple tissue segmentation on the full-scan pathological image of osteosarcoma;

[0037] 2. Then, use the trained Unet to segment the tumor region in the pathological image of the full-scan osteosarcoma; image 3 As shown, using UNet to segment WSI living tumor nuclei; it includes training (a-c) and testing (d-e); with the 512x512 image patch downloaded in TCGA and its nuclear annotation (a-b) as the training set, live tumors are divided into 512x512 The overlapping plaques are sent to the trained Unet for nuclear segmentation (d-e);

[0038] 3. If image 3 As shown in (f-g), feature extraction is carried out for the nucleus in the tumor area, and the 6-dimensional most discriminative feature is selected by using the mRMR feature selection algorithm, and sent to the trained random forest classifier to predict whether the patient has recurrence Risk; the visualization of 6-dimensional feat...

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Abstract

The invention discloses an osteosarcoma recurrence risk prediction model based on tissue morphological analysis. The method belongs to the field of machine learning and image processing. The method comprises the following specific steps: 1, segmentation of multiple types of tissues and segmentation of cell nucleuses in a tumor region; 2, feature extraction and feature selection; and 3, classifierconstruction. The invention provides a quantitative computer-assisted osteosarcoma recurrence risk prediction model established by using cell nucleus characteristics in a tumor region. Experimental results show that the image features derived from tumor cell nucleuses can be used as new markers independent of traditional standard clinical diagnosis features for prognosis analysis, so that patientscan be helped to carry out personalized treatment, and the development of precise oncology is promoted.

Description

technical field [0001] The invention relates to the fields of machine learning and image processing, in particular to a model for predicting the recurrence risk of osteosarcoma based on histomorphological analysis. Background technique [0002] Osteosarcoma is a highly malignant tumor that is usually treated with a combination of surgery, chemotherapy, and radiation therapy. Most patients with high-grade tumors receive chemotherapy, known as neoadjuvant therapy, for about three months before surgery. The surgeon will then remove the tumor, along with a large amount of healthy tissue around it, to keep the area free of all disease. The cure rate of neoadjuvant chemotherapy for osteosarcoma is close to 60-70%, but the course of treatment can be long and arduous, usually lasting a year or more. Therefore, the ability to identify patients at high risk of recurrence could help these patients develop more detailed surveillance plans and more personalized multidrug adjuvant chemo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06K9/62G06N3/04G06N3/08G16H30/20
CPCG06T7/11G06N3/04G06N3/08G16H30/20G06T2207/10072G06T2207/30008G06T2207/30096G06F18/24
Inventor 鲁浩达徐军
Owner NANJING UNIV OF INFORMATION SCI & TECH
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