Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients

McKenney, Anna Sophia; Weg, Emily S.; Bale, Tejus; Wild, Aaron T.; Um, Hyemin; Fox, Michael J.; Lin, Andrew; Yang, Jonathan T.; Yao, Peter; Birger, Maxwell; Tixier, Florent; Sellitti, Matthew; Young, Robert J.; Veeraraghavan, Harini

doi:10.1016/j.adro.2022.100916

Cited by 8 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model combining delta-radiomics and lactate dehydrogenase had the highest performance (AUC, 0.82). Similarly, McKenney et al 98 demonstrated an improvement in the AUC of a machine-learning classifier on combining O 6 -methylguanine-DNA methyltransferase (MGMT) with radiomics to predict PsP, thereby demonstrating the association of molecular drivers such as lactate dehydrogenase and MGMT. An AUC of 0.81 with radiomics alone jumped to 0.89 with the addition of MGMT values.…”

Section: Using Radiomics To Predict and Distinguish Typical And Atypi...mentioning

confidence: 96%

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Viswanathan

Gupta

Madabhushi

2022

American Society of Clinical Oncology Educational Book

View full text Add to dashboard Cite

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment–related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment–related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges—including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias—must be addressed for seamless clinical translation of these tools.

show abstract

Section: Using Radiomics To Predict and Distinguish Typical And Atypi...mentioning

confidence: 96%

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Viswanathan

Gupta

Madabhushi

2022

American Society of Clinical Oncology Educational Book

View full text Add to dashboard Cite

show abstract

“…Moreover, concerning intratumoral heterogeneity, radiomics is often referred as a sort of «virtual biopsy», since it allows to enrich the traditional diagnostic radiologic workflow with more information not detectable by human eye and associated with processes not included in radiologic workflow ( 15 ). Current radiomics studies in the glioma field have shown promising results in demonstrating correlations between MRI features and GBM differential diagnosis ( 16 ), molecular characteristics ( 17 , 18 ), and prognoses ( 11 , 19 , 20 ).…”

Section: Introductionmentioning

confidence: 99%

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Brancato¹,

Cavaliere²,

Garbino³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

show abstract

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas

Lai,

Wu,

Chen

et al. 2024

J Digit Imaging. Inform. med.

View full text Add to dashboard Cite

Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients

Abstract: analysis to predict histopathologically confirmed pseudoprogression in glioblastoma patients, Advances in Radiation Oncology (2022), doi:

Cited by 8 publications

References 43 publications

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas

Contact Info

Product

Resources

About