Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01

AUTHORS

Laura Filograna, Jacopo Lenkowicz, Francesco Cellini, Nicola Dinapoli, Stefania Manfrida, Nicola Magarelli, Antonio Leone, Cesare Colosimo, Vincenzo Valentini

ABSTRACT

OBJECTIVES: Recently, radiomic analysis has gained attention as a valuable instrument for the management of oncological patients. The aim of the study is to isolate which features of magnetic resonance imaging (MRI)-based radiomic analysis have to be considered the most significant predictors of metastasis in oncological patients with spinal bone marrow metastatic disease. MATERIALS AND METHODS: Eight oncological patients (3 lung cancer; 1 prostatic cancer; 1 esophageal cancer; 1 nasopharyngeal cancer; 1 hepatocarcinoma; 1 breast cancer) with pre-radiotherapy MR imaging for a total of 58 dorsal vertebral bodies, 29 metastatic and 29 non-metastatic were included. Each vertebral body was contoured in T1 and T2 weighted images at a radiotherapy delineation console. The obtained data were transferred to an automated data extraction system for morphological, statistical and textural analysis. Eighty-nine features for each lesion in both T1 and T2 images were computed as the median of by-slice values. A Wilcoxon test was applied to the 89 features and the most statistically significant of them underwent to a stepwise feature selection, to find the best performing predictors of metastasis in a logistic regression model. An internal cross-validation via bootstrap was conducted for estimating the model performance in terms of the area under the curve (AUC) of the receiver operating characteristic. RESULTS: Of the 89 textural features tested, 16 were found to differ with statistical significance in the metastatic vs non-metastatic group. The best performing model was constituted by two predictors for T1 and T2 images, namely one morphological feature (center of mass shift) (p value < 0.01) for both datasets and one histogram feature minimum grey level (p value < 0.01) for T1 images and one textural feature (grey-level co-occurrence matrix joint variance (p value < 0.01) for T2 images. The internal cross-validation showed an AUC of 0.8141 (95% CI 0.6854-0.9427) in T1 images and 0.9116 (95% CI 0.8294-0.9937) in T2 images. CONCLUSIONS: The results suggest that MRI-based radiomic analysis on oncological patients with bone marrow metastatic disease is able to differentiate between metastatic and non-metastatic vertebral bodies. The most significant predictors of metastasis were found to be based on T2 sequence and were one morphological and one textural feature. More... »

PAGES

1-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11547-018-0935-y

DOI

http://dx.doi.org/10.1007/s11547-018-0935-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106820613

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30191445


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