High-grade serous ovarian cancer recurrence risk prediction system based on imaging omics

A technology of radiomics and risk prediction, applied in informatics, medical simulation, image enhancement, etc., can solve the problems of ovarian cancer recurrence and limited value of prognosis prediction

Active Publication Date: 2020-10-30
FUDAN UNIV SHANGHAI CANCER CENT
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Problems solved by technology

Previous studies have shown that conventional sequence-based morphological features and functional sequence-based quantitative parameters are of limited value in predicting recurrence and prognosis of ovarian cancer

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  • High-grade serous ovarian cancer recurrence risk prediction system based on imaging omics
  • High-grade serous ovarian cancer recurrence risk prediction system based on imaging omics
  • High-grade serous ovarian cancer recurrence risk prediction system based on imaging omics

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[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0034] Such as figure 1 As shown, this embodiment provides a high-level serous ovarian cancer recurrence risk prediction system based on radiomics, which includes a tumor segmentation module 1, an image standardization module 2, a feature extraction module 3, a feature normalization module 4, and a feature Screening module 5 , resampling mod...

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Abstract

The invention discloses a high-grade serous ovarian cancer recurrence risk prediction system based on imaging omics. A method comprises the steps of T1 weighted enhanced imaging omics processing, T2 weighted imaging omics processing and information fusion. The image omics processing mainly comprises the steps of three-dimensional tumor segmentation, image standardization, image omics feature extraction, feature normalization, feature screening, SMOTE resampling and classifier training; the information fusion is mainly used for fusing recurrence risk prediction probabilities output by T1 and T2image omics processing, so the risk prediction accuracy is further improved.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a high-grade serous ovarian cancer recurrence risk prediction system based on radiomics method. Background technique [0002] High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer, accounting for about 70%, and the vast majority of patients are already in the advanced stage when they are diagnosed. Currently, the preferred treatment option remains initial cytoreductive surgery with postoperative platinum-based chemotherapy. Although the effective rate of initial treatment can reach 80%, about 85% of patients will relapse until drug resistance occurs, and the overall 5-year survival rate is only about 30%. Clinically, there is still a lack of effective and reliable markers to judge the risk of tumor recurrence, which is an urgent problem for gynecologists to solve. Recent studies have shown that maintenance therapy based on PARPI ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/12G06K9/62G16H50/20G16H50/50
CPCG06T7/0012G06T7/12G16H50/20G16H50/50G06T2207/10012G06T2207/30096G06T2207/20081G06F18/2411
Inventor 龚敬李海明顾雅佳彭卫军童彤朱晖
Owner FUDAN UNIV SHANGHAI CANCER CENT
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