System and methods for automatic assessment of radiotherapy outcome in tumours using longitudinal tumour segmentation on serial MRI
OwnerSeyed Ali Jalalifar, Ali Sadeghi-Naini +2inventors
InventorsSeyed Ali Jalalifar, Ali Sadeghi-Naini, Hany Soliman, Arjun Sahgal
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Abstract
A system for automatic assessment of therapy outcome in cancer patients treated with radiation therapy, the system comprising a machine-learning-based segmentation model for delineating tumours longitudinally in serial magnetic resonance imaging (MRI) with high precision. Longitudinal segmentations of tumour before and/or during treatment and/or at multiple follow-up sessions after the radiation therapy permits monitoring changes in tumour size and is used in the system for automatic assessment of therapy outcome based on standard clinical criteria.
Claims28
A system for automatic assessment of therapy outcome in cancer patients treated with radiation therapy, the system comprising:
- an imaging system for acquiring a series of input images comprising a region of interest (ROI) comprising a tumour, wherein each of the input images in the series is acquired from a same subject in different imaging sessions before, during and/or after the radiation therapy;
- a computer system comprising a hardware processor and a memory device on which instructions are encoded to cause the hardware processor to perform operations of:
- with a machine-learning-based segmentation framework comprising one deep neural network or a plurality of deep neural networks in cascade and/or parallel configurations, generating at least a tumour mask as a final output of the segmentation framework for each input image in the series;
- calculating and reporting dimensions of the tumour in various directions;
- using the calculated tumour dimensions, identifying and reporting a tumour size status;
- using the calculated tumour dimensions, categorizing and reporting a tumour size change at each imaging session of the different imaging sessions into categories comprising at least one of shrinkage, steady and enlargement, based on a predefined criteria comprising at least a response assessment in neuro-oncology (RANO) criteria;
- using a pattern of the tumour-size-change categories across the different imaging sessions, automatically assessing and reporting the radiation therapy-specific outcome comprising at least one of local control, local failure, or presence or absence of adverse radiation effect.
A method for automatic assessment of therapy outcome in cancer patients treated with radiation therapy, the method comprising:
- with an imaging system, acquiring a series of input images comprising a region of interest (ROI) comprising a tumour, wherein each of the input images in the series are acquired from a same subject in different imaging sessions before, during and/or after the radiation therapy;
- with a machine-learning-based segmentation framework comprising one deep neural network or a plurality of deep neural networks in cascade and/or parallel configurations, generating at least a tumour mask as a final output of the segmentation framework for each input image in the series;
- calculating and reporting dimensions of the tumour in various directions;
- using the calculated tumour dimensions, identifying and reporting a tumour size status;
- using the calculated tumour dimensions, categorizing and reporting the tumour size change at each mentioned imaging session into categories comprising at least one of shrinkage, steady and enlargement, based on a predefined criteria comprising at least a response assessment in neuro-oncology (RANO) criteria;
- using a pattern of the tumour-size-change categories across the different imaging sessions, automatically assessing and reporting the radiation-specific therapy outcome comprising at least one of local control, local failure, or presence or absence of adverse radiation effect.
A computer readable medium storing instructions executable by a processor to carry out operations comprising:
- receiving a series of input images comprising a region of interest (ROI) comprising a tumour, wherein each of the input images in the series are acquired from a same subject in different imaging sessions before, during and/or after radiation therapy;
- with a machine-learning-based segmentation framework comprising one deep neural network or a plurality of deep neural networks in cascade and/or parallel configurations, generating at least a tumour mask as a final output of the segmentation framework for each input image in the series;
- calculating and reporting dimensions of the tumour in various directions;
- using the calculated tumour dimensions, identifying and reporting a tumour size status;
- using the calculated tumour dimensions, categorizing and reporting a tumour size change at each imaging session of the different imaging sessions into categories comprising at least one of shrinkage, steady and enlargement, based on a predefined criteria comprising at least a response assessment in neuro-oncology (RANO) criteria;
- using a pattern of the tumour-size-change categories across the different imaging sessions, automatically assessing and reporting the radiation-specific therapy outcome comprising at least one of local control, local failure, or presence or absence of adverse radiation effect.
Dependencies are inferred from the claim text.