Cargando…
Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer
Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048673/ https://www.ncbi.nlm.nih.gov/pubmed/29940810 http://dx.doi.org/10.1177/1533033818782788 |
_version_ | 1783340136405139456 |
---|---|
author | Shi, Liting He, Yaoyao Yuan, Zilong Benedict, Stanley Valicenti, Richard Qiu, Jianfeng Rong, Yi |
author_facet | Shi, Liting He, Yaoyao Yuan, Zilong Benedict, Stanley Valicenti, Richard Qiu, Jianfeng Rong, Yi |
author_sort | Shi, Liting |
collection | PubMed |
description | Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients’ medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature. |
format | Online Article Text |
id | pubmed-6048673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-60486732018-07-20 Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer Shi, Liting He, Yaoyao Yuan, Zilong Benedict, Stanley Valicenti, Richard Qiu, Jianfeng Rong, Yi Technol Cancer Res Treat Review Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients’ medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature. SAGE Publications 2018-06-26 /pmc/articles/PMC6048673/ /pubmed/29940810 http://dx.doi.org/10.1177/1533033818782788 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Review Shi, Liting He, Yaoyao Yuan, Zilong Benedict, Stanley Valicenti, Richard Qiu, Jianfeng Rong, Yi Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer |
title | Radiomics for Response and Outcome Assessment for Non-Small Cell Lung
Cancer |
title_full | Radiomics for Response and Outcome Assessment for Non-Small Cell Lung
Cancer |
title_fullStr | Radiomics for Response and Outcome Assessment for Non-Small Cell Lung
Cancer |
title_full_unstemmed | Radiomics for Response and Outcome Assessment for Non-Small Cell Lung
Cancer |
title_short | Radiomics for Response and Outcome Assessment for Non-Small Cell Lung
Cancer |
title_sort | radiomics for response and outcome assessment for non-small cell lung
cancer |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048673/ https://www.ncbi.nlm.nih.gov/pubmed/29940810 http://dx.doi.org/10.1177/1533033818782788 |
work_keys_str_mv | AT shiliting radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT heyaoyao radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT yuanzilong radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT benedictstanley radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT valicentirichard radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT qiujianfeng radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer AT rongyi radiomicsforresponseandoutcomeassessmentfornonsmallcelllungcancer |