Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?
To assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS).



We identified 131 patients at our institution from 2000-2014 with a core needle biopsy-confirmed diagnosis of pure DCIS, a 1.5 or 3T preoperative bilateral breast MRI with nonfat-saturated T1-weighted MRI sequences, no preoperative therapy before breast MRI, and no prior history of breast cancer. A fellowship-trained radiologist identified the lesion on each breast MRI using a bounding box. Twenty-nine imaging features were then computed automatically using computer algorithms based on the radiologist's annotation.



The rate of upstaging of DCIS to invasive cancer in our study was 26.7% (35/131). Out of all imaging variables tested, the information measure of correlation 1, which quantifies spatial dependency in neighboring voxels of the tumor, showed the highest predictive value of upstaging with an area under the curve (AUC) = 0.719 (95% confidence interval [CI]: 0.609-0.829). This feature was statistically significant after adjusting for tumor size (P < 0.001).



Automatically assessed MRI features may have a role in triaging which patients with a preoperative diagnosis of DCIS are at highest risk for occult invasive disease.



4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1332-1340.
Department of Radiology, Duke University School of Medicine, Duke University, Durham, North Carolina, USA.
Michael R
0000-0002-8002-5210
MR
Harowicz
Department of Radiology, Duke University School of Medicine, Duke University, Durham, North Carolina, USA.
Ashirbani
A
Saha
Department of Radiology, Duke University School of Medicine, Duke University, Durham, North Carolina, USA.
Lars J
LJ
Grimm
Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
P Kelly
PK
Marcom
Department of Surgery, Duke University School of Medicine, Durham, North Carolina, USA.
Jeffrey R
JR
Marks
Department of Surgical Oncology, Duke University Medical Center, Durham, North Carolina, USA.
E Shelley
ES
Hwang
Department of Radiology, Duke University School of Medicine, Duke University, Durham, North Carolina, USA.|Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.|Duke University Medical Physics Program, Durham, North Carolina, USA.
Maciej A
MA
Mazurowski
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D062005:Biopsy, Large-Core Needle; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000601:surgery; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000000981:diagnostic imaging* / Q000601:surgery; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging / Q000379:methods; D057234:Preoperative Period; D011871:Radiology; D012189:Retrospective Studies
MRI; breast cancer; ductal carcinoma in situ; invasive carcinoma; upstaging