TY - JOUR
T1 - Differentiating diffuse from focal pattern on Computed Tomography in multiple myeloma
T2 - Added value of a Radiomics approach
AU - Tagliafico, Alberto Stefano
AU - Cea, Michele
AU - Rossi, Federica
AU - Valdora, Francesca
AU - Bignotti, Bianca
AU - Succio, Giulia
AU - Gualco, Stefano
AU - Conte, Alessio
AU - Dominietto, Alida
PY - 2019/12
Y1 - 2019/12
N2 - Purpose: Focal pattern in multiple myeloma (MM) seems to be related to poorer survival and differentiation from diffuse to focal pattern on computed tomography (CT) has inter-reader variability. We postulated that a Radiomic approach could help radiologists in differentiating diffuse from focal patterns on CT. Methods: We retrospectively reviewed imaging data of 70 patients with MM with CT, PET-CT or MRI available before bone marrow transplant. Two general radiologist evaluated, in consensus, CT images to define a focal (at least one lytic lesion >5 mm in diameter) or a diffuse (lesions <5 mm, not osteoporosis) pattern. N = 104 Radiomics features were extracted and evaluated with an open source software. Results: The pathological group included: 22 diffuse and 39 focal patterns. After feature reduction, 9 features were different (p < 0.05) in the diffuse and focal patterns (n = 2/9 features were Shape-based: MajorAxisLength and Sphericity; n = 7/9 were Gray Level Run Length Matrix (Glrlm)). AUC of the Radiologists versus Reference Standard was 0.64 (95 % CI: (0.49–0.78) p = 0.20. AUC of the best 4 features (MajorAxisLength, Median, SizeZoneNonUniformity, ZoneEntropy) were: 0.73 (95 % CI: 0.58–0.88); 0.71 (95 % CI: 0.54–0.88); 0.79 (95 % CI: 0.66–0.92); 0.68 (95 % CI: 0.53–0.83) respectively. Conclusion: A Radiomics approach improves radiological evaluation of focal and diffuse pattern of MM on CT.
AB - Purpose: Focal pattern in multiple myeloma (MM) seems to be related to poorer survival and differentiation from diffuse to focal pattern on computed tomography (CT) has inter-reader variability. We postulated that a Radiomic approach could help radiologists in differentiating diffuse from focal patterns on CT. Methods: We retrospectively reviewed imaging data of 70 patients with MM with CT, PET-CT or MRI available before bone marrow transplant. Two general radiologist evaluated, in consensus, CT images to define a focal (at least one lytic lesion >5 mm in diameter) or a diffuse (lesions <5 mm, not osteoporosis) pattern. N = 104 Radiomics features were extracted and evaluated with an open source software. Results: The pathological group included: 22 diffuse and 39 focal patterns. After feature reduction, 9 features were different (p < 0.05) in the diffuse and focal patterns (n = 2/9 features were Shape-based: MajorAxisLength and Sphericity; n = 7/9 were Gray Level Run Length Matrix (Glrlm)). AUC of the Radiologists versus Reference Standard was 0.64 (95 % CI: (0.49–0.78) p = 0.20. AUC of the best 4 features (MajorAxisLength, Median, SizeZoneNonUniformity, ZoneEntropy) were: 0.73 (95 % CI: 0.58–0.88); 0.71 (95 % CI: 0.54–0.88); 0.79 (95 % CI: 0.66–0.92); 0.68 (95 % CI: 0.53–0.83) respectively. Conclusion: A Radiomics approach improves radiological evaluation of focal and diffuse pattern of MM on CT.
KW - Agreement
KW - Computed tomography
KW - Feature
KW - Multiple myeloma
KW - Radiomics
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U2 - 10.1016/j.ejrad.2019.108739
DO - 10.1016/j.ejrad.2019.108739
M3 - Article
AN - SCOPUS:85074691005
VL - 121
JO - European Journal of Radiology
JF - European Journal of Radiology
SN - 0720-048X
M1 - 108739
ER -