{"id":30042,"date":"2026-03-23T16:17:49","date_gmt":"2026-03-23T15:17:49","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=30042"},"modified":"2026-03-24T11:34:47","modified_gmt":"2026-03-24T10:34:47","slug":"a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/","title":{"rendered":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma"},"content":{"rendered":"\n

A new paper by members of the Computational Neuroscience team at Sano, published in
Neuro-Oncology, introduces a novel neuroimaging biomarker for glioblastoma (GBM) called
the Lesion-Tract Density Index (L-TDI). Moving beyond viewing GBM as a focal lesion, this
study treats it as a network disease that interacts with the brain\u2019s white matter scaffold. By
analyzing large-scale white matter pathways in two independent patient cohorts, the
researchers found that L-TDI robustly stratifies survival rates and predicts outcomes more
accurately than traditional measures like tumor volume. This work marks a significant step
toward connectomics-guided neuro-oncology and improved individualized patient care.<\/p>\n\n\n\n

<\/div>\n\n\n\n
\"The
Image source: Figure 1 from \u201cA non-local diffusion magnetic resonance imaging tract density index to capture network-level tumor\u2013brain interactions\u201d, Neuro-Oncology, Oxford University Press academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif<\/a><\/figcaption><\/figure>\n\n\n\n


See the full text:<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tRead the article\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n\n\n\n
<\/div>\n\n\n\n

Autors: Joan Falc\u00f3-Roget<\/a>, Gianpaolo Antonio Basile, Anna Janus<\/a>, Sara Lillo, Letterio S Politi, Jan K Argasinski<\/a>, Alberto Cacciola<\/p>\n\n\n\n

Keywords: glioblastoma, brain connectome, diffusion tractography, glioblastoma, survival prediction<\/p>\n","protected":false},"excerpt":{"rendered":"

Published in Neuro-Oncology Journals, 2026<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-30042","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"\nA non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma\" \/>\n<meta property=\"og:description\" content=\"Published in Neuro-Oncology Journals, 2026\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-24T10:34:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"1900\" \/>\n\t<meta property=\"og:image:height\" content=\"1524\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/\",\"name\":\"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg\",\"datePublished\":\"2026-03-23T15:17:49+00:00\",\"dateModified\":\"2026-03-24T10:34:47+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/#primaryimage\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg\",\"width\":1900,\"height\":1524,\"caption\":\"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care.\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research\",\"item\":\"https:\\\/\\\/sano.science\\\/research\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Publications\",\"item\":\"https:\\\/\\\/sano.science\\\/research-type\\\/publications\\\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/sano.science\\\/#website\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"name\":\"Centre for Computational Personalized Medicine\",\"description\":\"Sano \u2013 Centre for Computational Medicine\",\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/sano.science\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\",\"name\":\"Sano \u2013 Centre for Computational Medicine\",\"alternateName\":\"Sano\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"width\":700,\"height\":265,\"caption\":\"Sano \u2013 Centre for Computational Medicine\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/sano.science\\\/\",\"https:\\\/\\\/x.com\\\/sanoscience\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/sanoscience\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCDZ_8TcjMWUG2ZcgKKgfpwQ\",\"https:\\\/\\\/bsky.app\\\/profile\\\/sanoscience.bsky.social\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma - Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/","og_locale":"en_US","og_type":"article","og_title":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma","og_description":"Published in Neuro-Oncology Journals, 2026","og_url":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2026-03-24T10:34:47+00:00","og_image":[{"width":1900,"height":1524,"url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/","url":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/","name":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/#primaryimage"},"image":{"@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/#primaryimage"},"thumbnailUrl":"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg","datePublished":"2026-03-23T15:17:49+00:00","dateModified":"2026-03-24T10:34:47+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/#primaryimage","url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma.jpeg","width":1900,"height":1524,"caption":"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care."},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/research\/a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Research","item":"https:\/\/sano.science\/research\/"},{"@type":"ListItem","position":3,"name":"Publications","item":"https:\/\/sano.science\/research-type\/publications\/"},{"@type":"ListItem","position":4,"name":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Cin69o","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">A new paper by members of the Computational Neuroscience team at Sano, published in<br>Neuro-Oncology, introduces a novel neuroimaging biomarker for glioblastoma (GBM) called<br>the Lesion-Tract Density Index (L-TDI). Moving beyond viewing GBM as a focal lesion, this<br>study treats it as a network disease that interacts with the brain\u2019s white matter scaffold. By<br>analyzing large-scale white matter pathways in two independent patient cohorts, the<br>researchers found that L-TDI robustly stratifies survival rates and predicts outcomes more<br>accurately than traditional measures like tumor volume. This work marks a significant step<br>toward connectomics-guided neuro-oncology and improved individualized patient care.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">A new paper by members of the Computational Neuroscience team at Sano, published in<br>Neuro-Oncology, introduces a novel neuroimaging biomarker for glioblastoma (GBM) called<br>the Lesion-Tract Density Index (L-TDI). Moving beyond viewing GBM as a focal lesion, this<br>study treats it as a network disease that interacts with the brain\u2019s white matter scaffold. By<br>analyzing large-scale white matter pathways in two independent patient cohorts, the<br>researchers found that L-TDI robustly stratifies survival rates and predicts outcomes more<br>accurately than traditional measures like tumor volume. This work marks a significant step<br>toward connectomics-guided neuro-oncology and improved individualized patient care.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-IwNK8h","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":30035,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-U0lq6Y","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg\" alt=\"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care.\" class=\"wp-image-30035\"\/><figcaption class=\"wp-element-caption\">Image source: Figure 1 from \u201cA non-local diffusion magnetic resonance imaging tract density index to capture network-level tumor\u2013brain interactions\u201d, Neuro-Oncology, Oxford University Press <a href=\"https:\/\/academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif<\/a><\/figcaption><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg\" alt=\"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care.\" class=\"wp-image-30035\"\/><figcaption class=\"wp-element-caption\">Image source: Figure 1 from \u201cA non-local diffusion magnetic resonance imaging tract density index to capture network-level tumor\u2013brain interactions\u201d, Neuro-Oncology, Oxford University Press <a href=\"https:\/\/academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif<\/a><\/figcaption><\/figure>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Cin69o","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><br>See the full text:<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><br>See the full text:<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-SkBTix","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"Read the article","button_type":"link","url":"https:\/\/academic.oup.com\/neuro-oncology\/article\/28\/2\/564\/8287534","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-SkBTix","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Plfl6U","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\" type=\"attachment\" id=\"18504\">Joan Falc\u00f3-Roget<\/a>, Gianpaolo Antonio Basile, <a href=\"https:\/\/sano.science\/people\/anna-janus\/\" type=\"people\" id=\"22468\">Anna Janus<\/a>, Sara Lillo, Letterio S Politi, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/Jan_Argasinski.webp\" type=\"attachment\" id=\"21360\">Jan K Argasinski<\/a>, Alberto Cacciola<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\" type=\"attachment\" id=\"18504\">Joan Falc\u00f3-Roget<\/a>, Gianpaolo Antonio Basile, <a href=\"https:\/\/sano.science\/people\/anna-janus\/\" type=\"people\" id=\"22468\">Anna Janus<\/a>, Sara Lillo, Letterio S Politi, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/Jan_Argasinski.webp\" type=\"attachment\" id=\"21360\">Jan K Argasinski<\/a>, Alberto Cacciola<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-l9wZem","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Keywords: glioblastoma, brain connectome, diffusion tractography, glioblastoma, survival prediction<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Keywords: glioblastoma, brain connectome, diffusion tractography, glioblastoma, survival prediction<\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/30042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":13,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/30042\/revisions"}],"predecessor-version":[{"id":30085,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/30042\/revisions\/30085"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=30042"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=30042"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=30042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}