Image-Based Subtype Classification for Glioblastoma Using Deep Learning : Prognostic Significance and Biologic Relevance
PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment.
MATERIALS AND METHODS: Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed.
RESULTS: Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes.
CONCLUSION: Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
|---|---|
| Enthalten in: |
JCO clinical cancer informatics - 8(2024) vom: 01. Jan., Seite e2300154 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yuan, Min [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 18.01.2024 Date Revised 18.01.2024 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1200/CCI.23.00154 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM367215594 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM367215594 | ||
| 003 | DE-627 | ||
| 005 | 20240118232148.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240117s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1200/CCI.23.00154 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1263.xml |
| 035 | |a (DE-627)NLM367215594 | ||
| 035 | |a (NLM)38231003 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yuan, Min |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Image-Based Subtype Classification for Glioblastoma Using Deep Learning |b Prognostic Significance and Biologic Relevance |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 18.01.2024 | ||
| 500 | |a Date Revised 18.01.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment | ||
| 520 | |a MATERIALS AND METHODS: Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed | ||
| 520 | |a RESULTS: Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes | ||
| 520 | |a CONCLUSION: Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma | ||
| 650 | 4 | |a Journal Article | |
| 650 | 7 | |a Biological Products |2 NLM | |
| 700 | 1 | |a Ding, Haolun |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Bangwei |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Miaomiao |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yaning |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Xu Steven |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t JCO clinical cancer informatics |d 2017 |g 8(2024) vom: 01. Jan., Seite e2300154 |w (DE-627)NLM275406369 |x 2473-4276 |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2024 |g day:01 |g month:01 |g pages:e2300154 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1200/CCI.23.00154 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2024 |b 01 |c 01 |h e2300154 | ||