In:
Journal of Magnetic Resonance Imaging, Wiley, Vol. 57, No. 1 ( 2023-01), p. 248-258
Kurzfassung:
Computational analysis of routinely acquired MRI has potential to improve the tumor chemoresistance prediction and to provide decision support in precision medicine, which may extend patient survival. Most radiomic analytical methods are compatible only with rectangular regions of interest (ROIs) and irregular tumor shape is therefore an important limitation. Furthermore, the currently used analytical methods are not directionally sensitive. Purpose To implement a tumor analysis that is directionally sensitive and compatible with irregularly shaped ROIs. Study Type Retrospective. Subjects A total of 54 patients with histopathologic diagnosis of primary osteosarcoma on tubular long bones and with prechemotherapy MRI. Field Strength/Sequence A 1.5 T, T2‐weighted‐short‐tau‐inversion‐recovery‐fast‐spin‐echo. Assessment A model to explore associations with osteosarcoma chemo‐responsiveness included MRI data obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was analyzed in the MRI data by calculation of the nondirectional two‐dimensional (2D) and directional and nondirectional one‐dimensional (1D) Higuchi dimensions (Dh). MAP chemotherapy response was assessed by histopathological necrosis. Statistical Tests The area under the receiver operating characteristic (ROC) curve (AUC) evaluated the association of the calculated features with the actual chemoresponsiveness, using tumor histopathological necrosis (95%) as the endpoint. Least absolute shrinkage and selection operator (LASSO) machine learning and multivariable regression were used for feature selection. Significance was set at 〈 0.05. Results The nondirectional 1D Dh reached an AUC of 0.88 in association with the 95% tumor necrosis, while the directional 1D analysis along 180 radial lines significantly improved this association according to the Hanley/McNeil test, reaching an AUC of 0.95. The model defined by variable selection using LASSO reached an AUC of 0.98. The directional analysis showed an optimal predictive range between 90° and 97° and revealed structural osteosarcoma anisotropy manifested by its directionally dependent textural properties. Data Conclusion Directionally sensitive radiomics had superior predictive performance in comparison to the standard nondirectional image analysis algorithms with AUCs reaching 0.95 and full compatibility with irregularly shaped ROIs. Evidence Level 3 Technical Efficacy Stage 1
Materialart:
Online-Ressource
ISSN:
1053-1807
,
1522-2586
Sprache:
Englisch
Verlag:
Wiley
Publikationsdatum:
2023
ZDB Id:
1497154-9
