Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology

Redalen, Kathrine Røe; Thorwarth, Daniela

doi:10.1016/j.phro.2020.06.007

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should also be noted that in the context of central nervous system tumors and other cancers treated with radiation therapy, the tumor volumes themselves are manually delineated to allow for targeting of the tumor with radiation therapy. A connection between segmentation and oncologic management that has led to a number of avenues exploring auto-segmentation to improve reproducibility and increase efficiency in the clinic [ 23 , 92 , 93 ]. Hand crafted methods and traditional radiomic continue to coexist with DL, discussed next however they both suffer from several limitations and are undergoing active evolution [ 33 ].…”

Section: Segmentationmentioning

confidence: 99%

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

AV¹,

Zhuge²,

Zhao³

et al. 2022

J Biotechnol Biomed

View full text Add to dashboard Cite

The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.

show abstract

Section: Segmentationmentioning

confidence: 99%

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

AV¹,

Zhuge²,

Zhao³

et al. 2022

J Biotechnol Biomed

View full text Add to dashboard Cite

show abstract

“…Taking advantage of its strong connection to the European Society for Radiotherapy and Oncology (ESTRO), we have strengthened the journal through linking it to ESTRO’s science-focused activities such as the physics workshops and the annual conferences. These activities have resulted in dedicated thematic special issues on radiotherapy physics and imaging topics [3] , [4] as well as conference highlight issues reporting in paper form the very best physics and imaging research that was presented at the conference [5] , [6] , [7] , [8] . Also this editorial accompanies the special issue of physics and imaging highlight papers from the ESTRO 2021 conference, with important papers from across our field already published [9] , [10] , and several other strong papers soon to appear.…”

mentioning

confidence: 99%