<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Voutouri C</submitter><funding>European Research Council</funding><funding>EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)</funding><pagination>203</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11487255</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>4(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.&lt;h4>Methods&lt;/h4>We developed a deep learning methodology based on shear wave </pubmed_abstract><journal>Communications medicine</journal><pubmed_title>A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models.</pubmed_title><pmcid>PMC11487255</pmcid><funding_grant_id>956201</funding_grant_id><funding_grant_id>101069207</funding_grant_id><pubmed_authors>Englezos D</pubmed_authors><pubmed_authors>Strouthos I</pubmed_authors><pubmed_authors>Voutouri C</pubmed_authors><pubmed_authors>Stylianopoulos T</pubmed_authors><pubmed_authors>Zamboglou C</pubmed_authors><pubmed_authors>Papanastasiou G</pubmed_authors></additional><is_claimable>false</is_claimable><name>A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models.</name><description>&lt;h4>Background&lt;/h4>In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.&lt;h4>Methods&lt;/h4>We developed a deep learning methodology based on shear wave </description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Oct</publication><modification>2026-06-01T09:14:52.637Z</modification><creation>2025-04-04T09:58:23.201Z</creation></dates><accession>S-EPMC11487255</accession><cross_references><pubmed>39420199</pubmed><doi>10.1038/s43856-024-00634-4</doi></cross_references></HashMap>