Characterization of Radiomics Features Extracted from Images Generated by the 0.35 T Scanner of an Integrated MRI-Linac
(2020) MSFT01 20201Medical Physics Programme
- Abstract
- Purpose: In an era of personalized oncology where the aim is to give every patient the right treatment at the right time an area of promising research is emerging called radiomics, or quantitative image analysis. The main underlying hypothesis is that pathophysiological information can be found in image texture not visible to the bare eye that can improve diagnosis, treatment adaption or be linked to a certain clinical outcome. This project in- vestigates the stability and repeatability of radiomic features extracted from images acquired with an integrated MRI-Linac with a 0.35 T scanner. The main objective was to identify radiomic features that are robust over various imaging conditions in both phantom and human data.
Methods: The... (More) - Purpose: In an era of personalized oncology where the aim is to give every patient the right treatment at the right time an area of promising research is emerging called radiomics, or quantitative image analysis. The main underlying hypothesis is that pathophysiological information can be found in image texture not visible to the bare eye that can improve diagnosis, treatment adaption or be linked to a certain clinical outcome. This project in- vestigates the stability and repeatability of radiomic features extracted from images acquired with an integrated MRI-Linac with a 0.35 T scanner. The main objective was to identify radiomic features that are robust over various imaging conditions in both phantom and human data.
Methods: The patient dataset included 50 images from ten stereotactic body radiation therapy (SBRT) pancreas cancer patients treated with 5 fractions, given on a daily basis, on the integrated MRI-Linac. Two anatomical sites were selected to represent heteroge- neous invariant tissue: the kidneys and liver. Eleven images from a Magphan RT phantom and 11 images from a ViewRay Daily QA phantom acquired monthly and daily respec- tively, constituted the basis for the phantom data, representing ideal imaging conditions. All images were acquired with a True Fast Imaging with Steady State Free Precession (TRUFI) pulse sequence with two different protocols. A high resolution (1.5mm3 voxel resolution) protocol was used for all phantom images and a protocol with lower resolution (1.5mm2 x 3.0mm) was used to collect the patient images. Totally 1087 shaped-based, first order statistics, second order statistics and higher order statistical radiomic features were extracted from each region of interest (ROI) and subject. Stability was assessed with the Coefficient of Variation (CoV) where features with CoV<5% were classified as robust. Common robust features among all datasets were identified as a final step.
Results: There were in total 130 radiomic features demonstrating robustness (CoV<5%) among all datasets. Robust features could be identified within each category, apart from two second order statistics groups: Gray level size zone and Neighborhood gray tone difference. The mean value of the CoV and the corresponding standard deviation was calculated for each robust feature in all four datasets.
Discussion and Conclusion: Several robust features in common with the result of this work can be identified in other MRI-based radiomics studies, which is promising. However, no overall agreement is found between all studies, emphasizing the need of more stability assessment research. The result in this work indicates that robust radiomic features over various imaging conditions, in both phantom and patient data, can be identified. It im- plies that phantom measurements can be used in stability assessment studies and that the 0.35 T scanner of the integrated MRI-Linac in this work is sufficiently stable over timefor radiomic studies. An additional promising finding is that many robust features also have been reported to have predictive value or discriminative power in other studies. Al- though preliminary, this result can serve as guidelines for further model building or further (Less) - Popular Abstract (Swedish)
- Cancer är en vanlig sjukdom i dagens samhälle, och man estimerar att en av tre i Sverige någon gång i livet kommer få en cancerdiagnos. De stora framsteg som skett inom forskning och teknologi har gjort att man har blivit bättre på att behandla så att allt fler överlever sin sjukdom. Det finns dock studier som tyder på att personer med samma diagnos kan reagera olika på samma typ av behandling. Varje tumör och patient är unik i sin genuppsättning och inom onkologi strävar man därför efter att ge en så individualiserad behandling som möjligt. Olika former av medicinsk bilddiagnostik (röntgen, magnetresonanstomografi (MR), Datortomografi (DT) m.m.) används idag inom sjukvården som ett verktyg för att säkerställa diagnos, rita ut... (More)
- Cancer är en vanlig sjukdom i dagens samhälle, och man estimerar att en av tre i Sverige någon gång i livet kommer få en cancerdiagnos. De stora framsteg som skett inom forskning och teknologi har gjort att man har blivit bättre på att behandla så att allt fler överlever sin sjukdom. Det finns dock studier som tyder på att personer med samma diagnos kan reagera olika på samma typ av behandling. Varje tumör och patient är unik i sin genuppsättning och inom onkologi strävar man därför efter att ge en så individualiserad behandling som möjligt. Olika former av medicinsk bilddiagnostik (röntgen, magnetresonanstomografi (MR), Datortomografi (DT) m.m.) används idag inom sjukvården som ett verktyg för att säkerställa diagnos, rita ut tumör/omgivande organ, dosplanera o.s.v. så att behandlingen kan skräddarsys så mycket som möjligt. Bilderna granskas visuellt av specialiserade läkare som bedömer tumörens utbredning, form och lokalisation. Detta kallas för en kvalitativ bedömning.
Radiomics är ett relativt nytt forskningsområde som innebär att man studerar medicinska bilder kvantitativt, d.v.s. information i bilden kan översättas till en faktiskt siffra, s.k. features, som kan kopplas till en specifik frågeställning. Den underliggande hypotesen
är att medicinska bilder inte enbart kan tolkas visuellt, utan att de består av data som kan ge ytterligare diagnostiskt värde. Heterogena strukturer och mönster ej synliga för blotta ögat tros innehålla biologisk/fysiologisk information om tumören eller omgivande vävnad som kan användas för att optimera behandlingen efter patientens behov. Det långsiktiga målet med radiomics är att undersöka dessa kvantitativa features för ett stort antal patienter och på så vis kunna bygga kliniska modeller som kan användas för att förbättra diagnostik och beslutstagande.
Målet med detta examensarbete var att undersöka ett stort antal radiomic features som beräknats från bilder tagna med en s.k. integrerad MR-Linac. Det finns flera yttre faktorer utöver biologiska/fysiologiska förändringar som kan påverka resultatet av en features värde såsom bildtagningsmetod, inställningar, upplösning o.s.v.. Man måste således säkerställa vilka features som är stabilia under olika förhållanden så att ett kvantitativt mått beror på fysiologi och inte olika bildinställningar. I detta arbete undersöktes därför stabiliteten av features från både patient- och fantomdata (fantom är solida objekt som används för kvalitetskontroll av utrustning) med olika inställningar. Ett flertal gemensamma stabila features identifierades och ett stort antal av dessa har även visat sig vara kopplade till kliniskt utfall i literaturstudier. Detta är lovande då radiomics har förutsättningarna att spela en stor roll i individanpassad onkologi. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9036072
- author
- Ericsson Szecsenyi, Rebecka LU
- supervisor
- organization
- course
- MSFT01 20201
- year
- 2020
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 9036072
- date added to LUP
- 2021-01-17 12:57:46
- date last changed
- 2021-01-17 12:57:46
@misc{9036072,
abstract = {{Purpose: In an era of personalized oncology where the aim is to give every patient the right treatment at the right time an area of promising research is emerging called radiomics, or quantitative image analysis. The main underlying hypothesis is that pathophysiological information can be found in image texture not visible to the bare eye that can improve diagnosis, treatment adaption or be linked to a certain clinical outcome. This project in- vestigates the stability and repeatability of radiomic features extracted from images acquired with an integrated MRI-Linac with a 0.35 T scanner. The main objective was to identify radiomic features that are robust over various imaging conditions in both phantom and human data.
Methods: The patient dataset included 50 images from ten stereotactic body radiation therapy (SBRT) pancreas cancer patients treated with 5 fractions, given on a daily basis, on the integrated MRI-Linac. Two anatomical sites were selected to represent heteroge- neous invariant tissue: the kidneys and liver. Eleven images from a Magphan RT phantom and 11 images from a ViewRay Daily QA phantom acquired monthly and daily respec- tively, constituted the basis for the phantom data, representing ideal imaging conditions. All images were acquired with a True Fast Imaging with Steady State Free Precession (TRUFI) pulse sequence with two different protocols. A high resolution (1.5mm3 voxel resolution) protocol was used for all phantom images and a protocol with lower resolution (1.5mm2 x 3.0mm) was used to collect the patient images. Totally 1087 shaped-based, first order statistics, second order statistics and higher order statistical radiomic features were extracted from each region of interest (ROI) and subject. Stability was assessed with the Coefficient of Variation (CoV) where features with CoV<5% were classified as robust. Common robust features among all datasets were identified as a final step.
Results: There were in total 130 radiomic features demonstrating robustness (CoV<5%) among all datasets. Robust features could be identified within each category, apart from two second order statistics groups: Gray level size zone and Neighborhood gray tone difference. The mean value of the CoV and the corresponding standard deviation was calculated for each robust feature in all four datasets.
Discussion and Conclusion: Several robust features in common with the result of this work can be identified in other MRI-based radiomics studies, which is promising. However, no overall agreement is found between all studies, emphasizing the need of more stability assessment research. The result in this work indicates that robust radiomic features over various imaging conditions, in both phantom and patient data, can be identified. It im- plies that phantom measurements can be used in stability assessment studies and that the 0.35 T scanner of the integrated MRI-Linac in this work is sufficiently stable over timefor radiomic studies. An additional promising finding is that many robust features also have been reported to have predictive value or discriminative power in other studies. Al- though preliminary, this result can serve as guidelines for further model building or further}},
author = {{Ericsson Szecsenyi, Rebecka}},
language = {{eng}},
note = {{Student Paper}},
title = {{Characterization of Radiomics Features Extracted from Images Generated by the 0.35 T Scanner of an Integrated MRI-Linac}},
year = {{2020}},
}