2023
DOI: 10.1002/cam4.6089
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Efficacy of fusion imaging for immediate post‐ablation assessment of malignant liver neoplasms: A systematic review

Pragati Rai,
Mohammed Yusuf Ansari,
Mohammed Warfa
et al.

Abstract: Background: Percutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post-procedural evaluation of the ablation zone is crucial, as it could influence the need for a second-look treatment or follow-up plan. Assessing treatment response immediately after ablation is essential to attain favorable o… Show more

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Cited by 28 publications
(2 citation statements)
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“…Deep learning models have achieved notable success in classifying, segmenting, and detecting relevant ROI in medical images and other modalities of data (2,(29)(30)(31)(32)(33). Recently, neural networks have been employed to upscale low-resolution medical images, transform medical imaging modalities, enhance visualization, and improve diagnostic accuracy.…”
Section: Literature Comparisonmentioning
confidence: 99%
“…Deep learning models have achieved notable success in classifying, segmenting, and detecting relevant ROI in medical images and other modalities of data (2,(29)(30)(31)(32)(33). Recently, neural networks have been employed to upscale low-resolution medical images, transform medical imaging modalities, enhance visualization, and improve diagnostic accuracy.…”
Section: Literature Comparisonmentioning
confidence: 99%
“…Within this plethora of research endeavors, precise segmentation models tailored specifically for liver US and CT images abound (12)(13)(14). These highly effective segmentation techniques have played an instrumental role in providing substantial support for clinical surgical treatments, aiding in intervention decision-making, and enhancing postoperative evaluations for HCC (15)(16)(17). Moreover, the application of AI in the medical domain extends beyond imaging.…”
Section: Introductionmentioning
confidence: 99%