TY - JOUR
T1 - Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ
AU - Yoon, Hai Jeon
AU - Kim, Yemi
AU - Kim, Bom Sahn
N1 - Funding Information:
The scientific guarantor of this publication is Bom Sahn Kim. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This research was supported by grants of National Research Foundation (2012R1A1A1012913 and 2012M3A9B6055379) of South Korea. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, observational, performed at one institution.
Publisher Copyright:
© 2015, European Society of Radiology.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Objectives: This study investigated whether texture-based imaging parameters could identify invasive components of ductal carcinoma in situ (DCIS). Methods: We enrolled 65 biopsy-confirmed DCIS patients (62 unilateral, 3 bilateral) who underwent 18 F-FDG PET, diffusion-weighted imaging (DWI), or breast-specific gamma imaging (BSGI). We measured SUVmax and intratumoral metabolic heterogeneity by the area under the curve (AUC) of cumulative SUV histograms (CSH) on PET, tumour-to-normal ratio (TNR) and coefficient of variation (COV) as an index of heterogeneity on BSGI, minimum ADC (ADCmin) and ADC difference (ADCdiff) as an index of heterogeneity on DWI. After surgery, final pathology was categorized as pure-DCIS (DCIS-P), DCIS with microinvasion (DCIS-MI), or invasive ductal carcinoma (IDC). Clinicopathologic features of DCIS were correlated with final classification. Results: Final pathology confirmed 44 DCIS-P, 14 DCIS-MI, and 10 IDC. The invasive component of DCIS was significantly correlated with higher SUVmax (p = 0.017) and lower AUC-CSH (p < 0.001) on PET, higher TNR (p = 0.008) and COV (p = 0.035) on BSGI, lower ADCmin (p = 0.016) and higher ADCdiff (p = 0.009) on DWI, and larger pathologic size (p = 0.018). On multiple regression analysis, AUC-CSH was the only significant predictor of invasive components (p = 0.044). Conclusions: The intratumoral metabolic heterogeneity of 18 F-FDG PET was the most important predictor of invasive components of DCIS. Key Points: • Preoperative identification of invasion in DCIS is important for axillary nodal management • Higher SUVmaxand lower AUC-CSH from FDG PET may indicate invasive components of DCIS • Higher TNR and COV from BSGI may indicate invasive components of DCIS • Lower ADCminand higher ADCdifffrom DWI may indicate invasive components of DCIS • AUC-CSH, an index of metabolic heterogeneity, is an independent predictor for invasive components
AB - Objectives: This study investigated whether texture-based imaging parameters could identify invasive components of ductal carcinoma in situ (DCIS). Methods: We enrolled 65 biopsy-confirmed DCIS patients (62 unilateral, 3 bilateral) who underwent 18 F-FDG PET, diffusion-weighted imaging (DWI), or breast-specific gamma imaging (BSGI). We measured SUVmax and intratumoral metabolic heterogeneity by the area under the curve (AUC) of cumulative SUV histograms (CSH) on PET, tumour-to-normal ratio (TNR) and coefficient of variation (COV) as an index of heterogeneity on BSGI, minimum ADC (ADCmin) and ADC difference (ADCdiff) as an index of heterogeneity on DWI. After surgery, final pathology was categorized as pure-DCIS (DCIS-P), DCIS with microinvasion (DCIS-MI), or invasive ductal carcinoma (IDC). Clinicopathologic features of DCIS were correlated with final classification. Results: Final pathology confirmed 44 DCIS-P, 14 DCIS-MI, and 10 IDC. The invasive component of DCIS was significantly correlated with higher SUVmax (p = 0.017) and lower AUC-CSH (p < 0.001) on PET, higher TNR (p = 0.008) and COV (p = 0.035) on BSGI, lower ADCmin (p = 0.016) and higher ADCdiff (p = 0.009) on DWI, and larger pathologic size (p = 0.018). On multiple regression analysis, AUC-CSH was the only significant predictor of invasive components (p = 0.044). Conclusions: The intratumoral metabolic heterogeneity of 18 F-FDG PET was the most important predictor of invasive components of DCIS. Key Points: • Preoperative identification of invasion in DCIS is important for axillary nodal management • Higher SUVmaxand lower AUC-CSH from FDG PET may indicate invasive components of DCIS • Higher TNR and COV from BSGI may indicate invasive components of DCIS • Lower ADCminand higher ADCdifffrom DWI may indicate invasive components of DCIS • AUC-CSH, an index of metabolic heterogeneity, is an independent predictor for invasive components
KW - Breast-specific gamma imaging
KW - DCIS
KW - Diffusion magnetic resonance imaging
KW - Invasive carcinoma
KW - Positron emission tomography
UR - http://www.scopus.com/inward/record.url?scp=84946493978&partnerID=8YFLogxK
U2 - 10.1007/s00330-015-3761-9
DO - 10.1007/s00330-015-3761-9
M3 - Article
C2 - 26063655
AN - SCOPUS:84946493978
SN - 0938-7994
VL - 25
SP - 3648
EP - 3658
JO - European Radiology
JF - European Radiology
IS - 12
ER -