Advancements in Immunofluorescence Spatial Analysis for Companion Diagnostics

This systematic review, synthesized using the Synthory.AI service, explores recent innovations in immunofluorescence spatial analysis with a focus on companion diagnostic applications. Following PRISMA guidelines, the review included primary research studies from PubMed and PubMed Central™ published from January 2019 to September 2024. Out of 92 initial records, 12 studies met the inclusion criteria. The findings indicate significant improvements in biomarker detection and patient stratification within immuno-oncology, driven by multiplexed platforms offering enhanced sensitivity and specificity. Despite these advancements, challenges such as small sample sizes and methodological biases persist, necessitating larger, multi-institutional studies and standardized approaches. Future research should focus on refining biomarker discovery by stratifying cohorts based on treatment regimens and advancing imaging technologies to enhance spatial profiling accuracy. These efforts will enhance the clinical utility of immunofluorescence spatial techniques in oncology diagnostics.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-01
DOI
https://doi.org/10.5281/zenodo.15033696
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Advancements in Immunofluorescence Spatial Analysis for Companion Diagnostics

Zenodo (CERN European Organization for Nuclear Research)
Radiomics and Machine Learning in Medical Imaging
article

Advancements in Immunofluorescence Spatial Analysis for Companion Diagnostics

article en

Abstract

This systematic review, synthesized using the Synthory.AI service, explores recent innovations in immunofluorescence spatial analysis with a focus on companion diagnostic applications. Following PRISMA guidelines, the review included primary research studies from PubMed and PubMed Central™ published from January 2019 to September 2024. Out of 92 initial records, 12 studies met the inclusion criteria. The findings indicate significant improvements in biomarker detection and patient stratification within immuno-oncology, driven by multiplexed platforms offering enhanced sensitivity and specificity. Despite these advancements, challenges such as small sample sizes and methodological biases persist, necessitating larger, multi-institutional studies and standardized approaches. Future research should focus on refining biomarker discovery by stratifying cohorts based on treatment regimens and advancing imaging technologies to enhance spatial profiling accuracy. These efforts will enhance the clinical utility of immunofluorescence spatial techniques in oncology diagnostics.

Zenodo (CERN European Organization for Nuclear Research)
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Radiomics and Machine Learning in Medical Imaging
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