Lara Cavinato, Jimin Hong, Martin Wartenberg, Stefan Reinhard, Robert Seifert, Paolo Zunino, Andrea Manzoni, Francesca Ieva, Arturo Chiti, Axel Rominger, Kuangyu Shi
{"title":"Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment","authors":"Lara Cavinato, Jimin Hong, Martin Wartenberg, Stefan Reinhard, Robert Seifert, Paolo Zunino, Andrea Manzoni, Francesca Ieva, Arturo Chiti, Axel Rominger, Kuangyu Shi","doi":"10.1007/s00259-024-06958-6","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [<sup>18</sup>F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery. We thus establish a direct association between radiomics features and the underlying biological properties of tissues.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We considered 4 immunohistochemically stained Whole Slide Images of pancreatic tissue of one healthy control and three patients with PDAC and/or precursor lesions. From marker-specific images, tissue-depending diffusivity properties were estimated and computational domains were built to simulate the [<sup>18</sup>F]FLT spatial-temporal uptake exploiting Partial Differential Equations and Finite Elements Method. Consequently, we simulated the imaging process obtaining surrogated PET images for the considered patients, and we performed image-derived features extraction from PET images to be mapped with biological properties via correlation estimation.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The framework captured the phenotypic differences and generated Time Activity Curves reflecting the underlying tissue composition. Image-derived biomarkers were ranked in view of their association with biological characteristics of the tissue, unveiling their molecular correlative. Moreover, we showed that the proposed pipeline could serve as a digital phantom to optimize the image acquisition for lesion detection.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This innovative framework holds the potential to enhance interpretability and reliability of radiomics, fostering the adoption in personalized nuclear medicine and patient care.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"3 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Nuclear Medicine and Molecular Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00259-024-06958-6","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [18F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery. We thus establish a direct association between radiomics features and the underlying biological properties of tissues.
Methods
We considered 4 immunohistochemically stained Whole Slide Images of pancreatic tissue of one healthy control and three patients with PDAC and/or precursor lesions. From marker-specific images, tissue-depending diffusivity properties were estimated and computational domains were built to simulate the [18F]FLT spatial-temporal uptake exploiting Partial Differential Equations and Finite Elements Method. Consequently, we simulated the imaging process obtaining surrogated PET images for the considered patients, and we performed image-derived features extraction from PET images to be mapped with biological properties via correlation estimation.
Results
The framework captured the phenotypic differences and generated Time Activity Curves reflecting the underlying tissue composition. Image-derived biomarkers were ranked in view of their association with biological characteristics of the tissue, unveiling their molecular correlative. Moreover, we showed that the proposed pipeline could serve as a digital phantom to optimize the image acquisition for lesion detection.
Conclusions
This innovative framework holds the potential to enhance interpretability and reliability of radiomics, fostering the adoption in personalized nuclear medicine and patient care.
期刊介绍:
The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.